phd in statistics and computer science

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Wharton Online

PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage .

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information.

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

Youth Program
Wharton Online

Statistics and Data Science

Wharton’s phd program in statistics and data science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. these include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer science..

Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

analysis of observational studies;
Bayesian inference, bioinformatics;
decision theory;
game theory;
high dimensional inference;
information theory;
machine learning;
model selection;
nonparametric function estimation; and
time series analysis.

For information on courses and sample plan of study, please visit the University Graduate Catalog .

Get the Details.

Visit the Statistics and Data Science website for details on program requirements and courses. Read faculty and student research and bios to see what you can do with a Statistics PhD.

Statistics and Data Science Doctoral Coordinator

Dr. Bhaswar Bhattacharya Associate Professor of Statistics and Data Science Associate Professor of Mathematics (secondary appointment) Email: [email protected] Phone: 215-573-0535

Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
Our students receive job offers from top academic and non-academic institutions .
Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
GRE requirement: Waived for Fall 2024.
Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
Deadline: Jan 8, 2024 .
Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
Timeline: P.hD students begin the program in September only. Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
What are the required application materials? Specific admission requirements for our programs can be found here .
Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle.
Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
What is the minimum TOEFL or IELTS requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously? For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences. Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

This option is only available for fall-term applicants.
Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University 107 Low Library, MC 4303 535 West 116th Street New York, NY 10027
How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
How do I find out which professors are taking on new students to mentor this year? Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at [email protected]

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

Complete all required coursework. .
Gain admittance to candidacy.
Submit a prospectus to be approved by a faculty committee.
Present a dissertation with original research. Review the Dissertation Publication page for more information.
Complete the necessary teaching requirement
Submit necessary forms to file for graduation
Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

Meet the department minimum residency requirement of 2 years
STAT 344-0 Statistical Computing
STAT 350-0 Regression Analysis
STAT 353-0 Advanced Regression (new 2021-22)
STAT 415-0 I ntroduction to Machine Learning
STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

STAT 351-0 Design and Analysis of Experiments
STAT 425 Sampling Theory and Applications
MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

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Department of Statistics

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PhD Program

The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

PhD Program Requirements

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Ph.d. program.

Statistical Science at Duke is the world's leading graduate research and educational environment for Bayesian statistics, emphasizing the major themes of 21st century statistical science: foundational concepts of statistics, theory and methods of complex stochastic modeling, interdisciplinary applications of statistics, computational statistics, big data analytics, and machine learning. Life as a Ph.D. student in Statistical Science at Duke involves immersion in a broad range of research experiences and emphasizes conceptual innovation, as well as building a deep and broad foundation in theory and methods.

Coupled with our core emphases in modeling, computation and the methodologies of modern statistical science is a broad range of interdisciplinary relationships with many other disciplines (biomedical sciences, environmental sciences, genomics, computer science, engineering, finance, neuroscience, social sciences, and others). The rich opportunities for students in interdisciplinary statistical research at Duke are complemented by opportunities for engagement in research in summer projects with nonprofit agencies, industry, and academia.

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Statistics and Data Science, PhD

Major areas of departmental research include:

analysis of observational studies;
Bayesian inference, bioinformatics;
decision theory;
game theory;
high dimensional inference;
information theory;
machine learning;
model selection;
nonparametric function estimation; and
time series analysis.

For more information: https://statistics.wharton.upenn.edu/programs/phd/curriculum/

View the University’s Academic Rules for PhD Programs .

The total course units required for graduation is 13.

Electives must include suitable courses numbered 9000 and above, when offered.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

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Interdisciplinary Doctoral Program in Statistics

The Interdisciplinary Doctoral Program in Statistics is an opportunity for students in a multitude of disciplines to specialize at the doctoral level in a statistics-grounded view of their field. Participating programs include Aeronautics and Astronautics, Brain and Cognitive Sciences, Economics, Mathematics, Mechanical Engineering, Physics, Political Science, and the IDSS Social and Engineering Systems Doctoral Program.

The program is administered jointly by the Statistics and Data Science Center and the participating academic units. Students enrolled in a doctoral program in a participating department may choose to be considered for the Interdisciplinary Doctoral Program in Statistics. Please refer to the program's website for details on the selection process.

Selected students will complete the home department’s degree requirements (including the qualifying exam) along with specified statistics requirements including a doctoral seminar, coursework in probability, statistics, computation and statistics, and data analysis, and a dissertation that utilizes statistical methods in a substantial way.

For more information about the program, contact the Statistics Academic Administrator .

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The PDF includes all information on this page and its related tabs. Subject (course) information includes any changes approved for the current academic year.

School of Computer Science and Statistics

View the contact page for more contact and location information

Postgraduate
PhD Programme

Structured PhD Programme

Immerse yourself in a vibrant community where world-class resources and opportunities await. Explore diverse research areas and unlock the potential of cutting-edge technology and industry partnerships.

As a Ph.D. student in the School, you would be joining a large and vibrant group of over 200 researchers, of which over 150 are Ph.D. students. Trinity College Dublin is the top-ranked university in Ireland and has consistently been ranked in the top 100 universities of the world. Further, we are one of only a handful of Schools of both computer science and statistics and so offer a unique perspective on research in both disciplines. As a Ph.D. student you will have access to world-class researchers, expertise, journals and other publications, and state of the art computing resources.

The School of Computer Science and Statistics collaborates with leading industry players in the disciplines, for example IBM, Intel and Microsoft, and fosters innovation through its many successful start-up companies, such as Havok and the first Irish company to be floated on the NASDAQ, Iona Technologies .

Students have a wide and varied choice of research areas that is conducted through a number of research disciplines.

Finance & Fees

Professor Prof. Ivana Dusparic

Programme Director

Statistics & Data Science

Dietrich college of humanities and social sciences, statistics/machine learning joint ph.d. degree, this program differs from the standard statistics ph.d. program in its emphasis on machine learning and computer science. students in this track will be involved in courses and research from both the departments of statistics and machine learning..

During the first year, students will normally be situated in and supported by the Department of Statistics. During later years, students will be located in the Department of their primary advisor.

Students will be granted the joint degree if they meet TWO sets of program requirements corresponding to the TWO departments, namely the ML Ph.D. Requirements and the Statistics Ph.D. Requirements, as we present next.

Statistics Joint Program Requirements

Students in this program are subject to all of the core Ph.D. requirements , except that the Data Analysis Exam is not required. (The Data Analysis Exam is required, however, in order to receive the M.S. in Statistics .)

Students in this program are required to complete the Advanced Data Analysis (ADA) project to the same standards as regular Statistics Ph.D. students. Namely, they are required to work on a substantive, real data project with a domain expert as outside advisor. A faculty member from the Department of Statistics must play an oversight role in the project, if not as the primary advisor. This project will satisfy the ML Data Analysis Project (DAP), speaking skills, and writing requirements, provided that an ML faculty member is an advisor.

The student must be advised by a faculty from the home department along with a Core Faculty member from the joint department as a co-mentor. Joint Statistics-MLD faculty cannot serve both roles. Both faculty must be identified at the time of admission to the joint program.

The thesis proposal and defense must be announced to the MLD community.

For questions, please send email to: [email protected] (link sends e-mail)

ML Joint Program Requirements

How to apply.

Students interested in this joint Ph.D. degree should apply to the Ph.D. program that best aligns with their research interests (Ph.D. in Statistics or Ph.D. in Machine Learning).

Machine Learning Ph.D. Online Application

Statistics Ph.D. Online Application

Academic Programs
Graduate Programs

Doctor of Philosophy

The Doctor of Philosophy program in statistics prepares students for careers in university teaching and research or in government or industrial research. Students entering the program spend four semesters acquiring a basic background in probability, mathematical statistics, and applied statistics and take general examinations on these subjects. More specialized study follows with the thesis research, which usually begins in the third year. This research may be concentrated in any area of statistics or probability in which a faculty member is interested. Students also have the opportunity to gain experience in applied statistics through participation in statistical consulting . Completion of the Ph.D. program normally requires three to five years.

Requirements

Students who enter this program should have knowledge of probability equivalent to the content of STAT 51600 or STAT 51900 . A course in mathematical statistics is desirable, as is a course in regression.

Besides satisfying the general regulations of the Graduate School for the degree of Doctor of Philosophy, the student must complete the following requirements:

Plan of Study

A tentative plan of study should be submitted electronically to the Graduate School by the end of the third session for doctoral students. An individual plan of study is crafted by the student and major professor with the approval of the student's doctoral advisory committee and the department.

Qualifying Examination

There are four Ph.D. qualifying examinations that cover material in methodology, probability, mathematical statistics, and computational statistics and are based on the core courses of the first year of graduate study in statistics.

Preliminary Examination

A student who has submitted an approved plan of study and passed the qualifying exams is required to take a preliminary exam. The purpose of the preliminary exam is to test the preparedness of the student for research. The preliminary exam is an oral exam that is administered by the student’s Ph.D. advisory committee. The student will then be recommended to the Graduate School for admissions to candidacy for the Ph.D degree.

Dissertation

A thesis must be submitted in final form, presenting new results of sufficient importance to merit publication. These results may be theoretical advances in probability or statistics, or methodological advances in the application of probability or statistics. The thesis must be accepted by the advisory committee. The student must present the contents of the thesis in an open examination.

Once a student is admitted to candidacy, the final examination marking completion of the doctoral program must be passed within five calendar years. Extensions of the limit may be granted by the graduate committee on petition by the student and his or her major professor. This may require reapproval of the plan of study and/or retaking of all or part of the Ph.D. degree qualifying examination.

Teaching Experience

Every doctoral candidate is required to teach at least quarter-time for one semester unless decided otherwise by the department.

Upon successful fulfillment of these requirements, the candidate will be recommended to the faculty to receive the Ph.D. degree.

Faculty Research Areas

If you are interested in applying to the Ph.D. Program, please visit How to Apply .

How to Apply
Faculty Research Groups
Graduate Courses
Career Development
Degrees Offered
Doctor of Philosophy Degree
MS Statistics Degree in Applied Statistics
MS Statistics Degree in Applied Statistics - Online Program
MS Data Science in Finance Residential Program
MS Statistics Degree in Mathematical Statistics or Probability
Joint Statistics and Computer Science Degree
Fifth Year Applied Statistics MS Degree
Concurrent Degree
Graduate Certificate in Applied Statistics
Non-Degree Seeking Graduate Students

Purdue Department of Statistics, 150 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

Trouble with this page? Disability-related accessibility issue ? Please contact the College of Science .

Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

COMS 4231 Analysis of Algorithms I
COMS 6232 Analysis of Algorithms II
COMS 4111 Introduction to Databases
COMS 4113 Distributed Systems Fundamentals
EECS 6720 Bayesian Models for Machine Learning
COMS 4771 Machine Learning
COMS 4772 Advanced Machine Learning
IEOR E6613 Optimization I
IEOR E6614 Optimization II
IEOR E6711 Stochastic Modeling I
EEOR E6616 Convex Optimization
STAT 6301 Probability Theory I
STAT 6201 Theoretical Statistics I
STAT 6101 Applied Statistics I
STAT 6104 Computational Statistics
STAT 5224 Bayesian Statistics
STCS 6701 Foundations of Graphical Models (joint with Computer Science)

Information Request Form

Ph.d. specialization committee.

View All People
Faculty of Arts and Sciences Professor of Statistics
The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

The Fu Foundation School of Engineering and Applied Science Vice Dean of Research
Tang Family Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

Data Science Institute Interim Director
The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
Data Science Institute Associate Director for Academic Affairs

PhD Program information

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests. Many students are involved in interdisciplinary research. Students may also have the option to pursue a designated emphasis (DE) which is an interdisciplinary specialization: Designated Emphasis in Computational and Genomic Biology , Designated Emphasis in Computational Precision Health , Designated Emphasis in Computational and Data Science and Engineering . The program requires four semesters of residence.

Normal progress entails:

Year 1 . Perform satisfactorily in preliminary coursework. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity. Years 2-3 . Continue coursework. Find a thesis advisor and an area for the oral qualifying exam. Formally choose a chair for qualifying exam committee, who will also serve as faculty mentor separate from the thesis advisor. Pass the oral qualifying exam and advance to candidacy by the end of Year 3. Present research at BSTARS each year. Years 4-5 . Finish the thesis and give a lecture based on it in a department seminar.

Program Requirements

Qualifying Exam

Course work and evaluation

Preliminary stage: the first year.

Effective Fall 2019, students are expected to take four semester-long courses for a letter grade during their first year which should be selected from the core first-year PhD courses offered in the department: Probability (204/205A, 205B,), Theoretical Statistics (210A, 210B), and Applied Statistics (215A, 215B). These requirements can be altered by a member of the PhD Program Committee (in consultation with the faculty mentor and by submitting a graduate student petition ) in the following cases:

Students primarily focused on probability will be allowed to substitute one semester of the four required semester-long courses with an appropriate course from outside the department.
Students may request to postpone one semester of the core PhD courses and complete it in the second year, in which case they must take a relevant graduate course in their first year in its place. In all cases, students must complete the first year requirements in their second year as well as maintain the overall expectations of second year coursework, described below. Some examples in which such a request might be approved are described in the course guidance below.
Students arriving with advanced standing, having completed equivalent coursework at another institution prior to joining the program, may be allowed to take other relevant graduate courses at UC Berkeley to satisfy some or all of the first year requirements

Requirements on course work beyond the first year

Students entering the program before 2022 are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year , students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge.

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U). Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

First year course work: For the purposes of satisfactory progression in the first year, grades in the core PhD courses are evaluated as: A+: Excellent performance in PhD program A: Good performance in PhD program A-: Satisfactory performance B+: Performance marginal, needs improvement B: Unsatisfactory performance

First year and beyond: At the end of each year, students must meet with his or her faculty mentor to review their progress and assess whether the student is meeting expected milestones. The result of this meeting should be the completion of the student’s annual review form, signed by the mentor ( available here ). If the student has a thesis advisor, the thesis advisor must also sign the annual review form.

Guidance on choosing course work

Choice of courses in the first year: Students enrolling in the fall of 2019 or later are required to take four semesters of the core PhD courses, at least three of which must be taken in their first year. Students have two options for how to schedule their four core courses:

Option 1 -- Complete Four Core Courses in 1st year: In this option, students would take four core courses in the first year, usually finishing the complete sequence of two of the three sequences. Students following this option who are primarily interested in statistics would normally take the 210A,B sequence (Theoretical Statistics) and then one of the 205A,B sequence (Probability) or the 215A,B sequence (Applied Statistics), based on their interests, though students are allowed to mix and match, where feasible. Students who opt for taking the full 210AB sequence in the first year should be aware that 210B requires some graduate-level probability concepts that are normally introduced in 205A (or 204).
Option 2 -- Postponement of one semester of a core course to the second year: In this option, students would take three of the core courses in the first year plus another graduate course, and take the remaining core course in their second year. An example would be a student who wanted to take courses in each of the three sequences. Such a student could take the full year of one sequence and the first semester of another sequence in the first year, and the first semester of the last sequence in the second year (e.g. 210A, 215AB in the first year, and then 204 or 205A in the second year). This would also be a good option for students who would prefer to take 210A and 215A in their first semester but are concerned about their preparation for 210B in the spring semester. Similarly, a student with strong interests in another discipline, might postpone one of the spring core PhD courses to the second year in order to take a course in that discipline in the first year. Students who are less mathematically prepared might also be allowed to take the upper division (under-graduate) courses Math 104 and/or 105 in their first year in preparation for 205A and/or 210B in their second year. Students who wish to take this option should consult with their faculty mentor, and then must submit a graduate student petition to the PhD Committee to request permission for postponement. Such postponement requests will be generally approved for only one course. At all times, students must take four approved graduate courses for a letter grade in their first year.

After the first year: Students with interests primarily in statistics are expected to take at least one semester of each of the core PhD sequences during their studies. Therefore at least one semester (if not both semesters) of the remaining core sequence would normally be completed during the second year. The remaining curriculum for the second and third years would be filled out with further graduate courses in Statistics and with courses from other departments. Students are expected to acquire some experience and proficiency in computing. Students are also expected to attend at least one departmental seminar per week. The precise program of study will be decided in consultation with the student’s faculty mentor.

Remark. Stat 204 is a graduate level probability course that is an alternative to 205AB series that covers probability concepts most commonly found in the applications of probability. It is not taught all years, but does fulfill the requirements of the first year core PhD courses. Students taking Stat 204, who wish to continue in Stat 205B, can do so (after obtaining the approval of the 205B instructor), by taking an intensive one month reading course over winter break.

Designated Emphasis: Students with a Designated Emphasis in Computational and Genomic Biology or Designated Emphasis in Computational and Data Science and Engineering should, like other statistics students, acquire a firm foundation in statistics and probability, with a program of study similar to those above. These programs have additional requirements as well. Interested students should consult with the graduate advisor of these programs.

Starting in the Fall of 2019, PhD students are required in their first year to take four semesters of the core PhD courses. Students intending to specialize in Probability, however, have the option to substitute an advanced mathematics class for one of these four courses. Such students will thus be required to take Stat 205A/B in the first year, at least one of Stat 210A/B or Stat 215A/B in the first year, in addition to an advanced mathematics course. This substitute course will be selected in consultation with their faculty mentor, with some possible courses suggested below. Students arriving with advanced coursework equivalent to that of 205AB can obtain permission to substitute in other advanced probability and mathematics coursework during their first year, and should consult with the PhD committee for such a waiver.

During their second and third years, students with a probability focus are expected to take advanced probability courses (e.g., Stat 206 and Stat 260) to fulfill the coursework requirements that follow the first year. Students are also expected to attend at least one departmental seminar per week, usually the probability seminar. If they are not sufficiently familiar with measure theory and functional analysis, then they should take one or both of Math 202A and Math 202B. Other recommended courses from the department of Mathematics or EECS include:

Math 204, 222 (ODE, PDE) Math 205 (Complex Analysis) Math 258 (Classical harmonic analysis) EE 229 (Information Theory and Coding) CS 271 (Randomness and computation)

The Qualifying Examination

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department. At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Qualifying Exam Chair. For qualifying exam committees formed in the Fall of 2019 or later, the qualifying exam chair will also serve as the student’s departmental mentor, unless a student already has two thesis advisors. The student must select a qualifying exam chair and obtain their agreement to serve as their qualifying exam chair and faculty mentor. The student's prospective thesis advisor cannot chair the examination committee. Selection of the chair can be done well in advance of the qualifying exam and the rest of the qualifying committee, and because the qualifying exam chair also serves as the student’s departmental mentor (unless the student has co-advisors), the chair is expected to be selected by the beginning of the third year or at the beginning of the semester of the qualifying exam, whichever comes earlier. For more details regarding the selection of the Qualifying Exam Chair, see the "Mentoring" tab.

Paperwork and Application. Students at the point of taking a qualifying exam are assumed to have already found a thesis advisor and to should have already submitted the internal departmental form to the Graduate Student Services Advisor ( found here ). Selection of a qualifying exam chair requires that the faculty member formally agree by signing the internal department form ( found here ) and the student must submit this form to the Graduate Student Services Advisor. In order to apply to take the exam, the student must submit the Application for the Qualifying Exam via CalCentral at least three weeks prior to the exam. If the student passes the exam, they can then officially advance to candidacy for the Ph.D. If the student fails the exam, the committee may vote to allow a second attempt. Regulations of the Graduate Division permit at most two attempts to pass the oral qualifying exam. After passing the exam, the student must submit the Application for Candidacy via CalCentral .

The Doctoral Thesis

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. See Alumni if you would like to view thesis titles of former PhD Students.

Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict. For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website.

Students who have advanced from candidacy (i.e. have taken their qualifying exam and submitted the advancement to candidacy application) must have a joint meeting with their QE chair and their PhD advisor to discuss their thesis progression; if students are co-advised, this should be a joint meeting with their co-advisors. This annual review is required by Graduate Division. For more information regarding this requirement, please see https://grad.berkeley.edu/ policy/degrees-policy/#f35- annual-review-of-doctoral- candidates .

Teaching Requirement

For students enrolled in the graduate program before Fall 2016, students are required to serve as a Graduate Student Instructor (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the Fall 2016 entering class, students are required to serve as a GSI for a minimum of two 50% GSI appointment during the regular academic semesters prior to graduation (20 hours a week is equivalent to a 50% GSI appointment for a semester) for Statistics courses numbered 150 and above. Exceptions to this policy are routinely made by the department.

Each spring, the department hosts an annual conference called BSTARS . Both students and industry alliance partners present research in the form of posters and lightning talks. All students in their second year and beyond are required to present a poster at BSTARS each year. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research. It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor (or their faculty mentors if an advisor has not yet been determined).

Mentoring for PhD Students

Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. This faculty mentor at this stage is not expected to be the student’s PhD advisor nor even have research interests that closely align with the student. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships. Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.

Research-focused mentoring : Once students have found a thesis advisor, that person will naturally be the faculty member most directly overseeing the student’s progression. However, students will also choose an additional faculty member to serve as a the chair of their qualifying exam and who will also serve as a faculty mentor for the student and as a member of his/her thesis committee. (For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor).

The student will be responsible for identifying and asking a faculty member to be the chair of his/her quals committee. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier. Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year (and may have already taken qualifying exams), but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.

Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor. If students are co-advised, this should be a joint meeting with their co-advisors.

If there is a need for a substitute faculty mentor (e.g. existing faculty mentor is on sabbatical or there has been a significant shift in research direction), the student should bring this to the attention of the PhD Committee for assistance.

PhD Student Forms:

Important milestones: .

Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships).

†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.

Expected Progress Reviews:

* These meetings do not need to be held in the semester that you take your Qualifying Exam, since the relevant people should be members of your exam committee and will discuss your research progress during your qualifying exam

** If you are being co-advised by someone who is not your primary advisor because your primary advisor cannot be your sole advisor, you should be meeting with that person like a research mentor, if not more frequently, to keep them apprised of your progress. However, if both of your co-advisors are leading your research (perhaps independently) and meeting with you frequently throughout the semester, you do not need to give a fall research progress report.

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Department of statistics and applied probability - uc santa barbara, phd in statistics and applied probability.

Our doctoral program in Statistics and Applied Probability prepares graduate students to expand the boundaries of statistical theory and practice for use in real-world problem solving. Graduates are trained for a career in academics or industry where they are working in and contributing to the forefront of new methods and technology. This program provides rigorous mathematical training in statistics and probability that can be used to develop real-world methodologies applicable to a wide range of interdisciplinary fields including finance, environmental science, computer science, and biomedical science. Recent dissertations have been written in the areas of smoothing splines, spatial statistics, micro-array analysis, functional data models, empirical processes, mathematical and statistical finance, Bayesian inference, and bootstrap estimation methods.

Admission Requirements

Our doctoral program in Statistics and Applied Probability is open to those who hold a bachelor’s degree in Statistics, Mathematics, or other fields with strong quantitative requirements. Students must have a minimum overall grade point average of 3.0; one year of statistical theory that includes hypothesis testing, confidence intervals, best statistics and most powerful tests, regression and ANOVA concepts; and one course in linear algebra that includes vector spaces, bases in vector spaces, eigenvalues, and eigenvectors.

For further admissions requirements and procedures, please visit our admissions page .

Normative Time to Degree

The normative time to advancement to candidacy is 3 years. The normative time for completion of the PhD program is 5 years. Students are expected to have their core courses completed and written qualifying exams passed within the first 2 years.

Registration Expectations

In addition to department requirements, every UC Santa Barbara graduate student is required to follow University policy with regards to degree requirements and registration expectations. You can read over these requirements on the Graduate Division website: here.

Sample Study-Plan

Every full-time student at UC Santa Barbara is required to take 8 units of coursework per quarter. Financial support is contingent on normal progress towards the degree objective.

The following would be a typical program for a well-prepared student seeking a Ph.D. objective with no optional degree emphasis.

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PhD in Computer Science

The PhD in Computer Science program provides students with the advanced coursework and groundbreaking research opportunities they need to contribute at the forefront of the world’s fastest-growing fields. Forging knowledge in 15 core areas like artificial intelligence, data science, programming languages, and human-centered computing, you’ll gain significant expertise in conducting and presenting the results of your research. Ultimately, you’ll produce and defend original work that contributes to critical discourse in your chosen area.

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Khoury College doctorate students gain deep knowledge and invaluable experience—preparing you for a research career in academia or industry.

Khoury Computer Science PhD graduates have found prestigious positions across industry and academia.

Tenure-track faculty:

University of Michigan, Ann Arbor
University of British Columbia (UBC)
Indiana University
University of Maryland
University College London
NC State University
UMass Boston
City University of Hong Kong

Postdoc research scientists:

University of Paris
Virginia Tech
Microsoft Research
GE Global Research

Senior software engineers and industry leaders:

Students graduating with a PhD in Computer Science will:

Gain a broad understanding of computer science fundamentals, spanning a substantial portion of the following core areas: artificial intelligence and data science, human-centered computing, software, systems, and theory
Gain significant expertise in at least one research area in computer science
Produce and defend original research in an area of computer science
Be able to communicate research results effectively in both oral and written forms

Our flagship campus in Boston is just minutes away from esteemed universities, exciting start-ups, and leaders in tech, finance, health care, and more.

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PhD in Data Science and Analytics

Degrees & Programs

Doctoral Degree in Data Science and Analytics
Certificates

We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection ofcomputer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community.

Herman Ray , Director, Ph.D. in Data Science and Analytics

About the Doctoral Degree in Data Science and Analytics

This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.

Because this degree is a Ph.D., it creates flexibility. Graduates can either pursue a position in the private or public sector as a "practicing" Data Scientist – where continued demand is expected to greatly outpace the supply - or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation.

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

Successful applicants will have completed a masters degree in a computational field (e.g., engineering, computer science, statistics, economics, finance, etc.)
Applicants are expected to have deep proficiency in at least one analytical programming language (e.g., SAS, R, Python). SQL and Java are helpful but not required.
Interested applicants who have earned an undergraduate degree are encouraged to apply to the Ph.D. Program with the embedded MS in Computer Science or with the MS in Applied Statistics.

Stage Two: Coursework

The Ph.D. in Data Science and Analytics requires 78 total credit hours spread over four years of study. Example Program of Study:

CS 8265 - Big Data Analytics
CS 8267 - Machine Learning
MATH 8010 - Theory of Linear Models (optional)
MATH 8020 - Graph Theory
MATH 8030 - Applied Discrete and Combinatorial Mathematics
STAT 8240 - Data Mining I
STAT 8250 - Data Mining II
Comprehensive Exam
21 credit hours of electives in computer science, statistics, mathematics, information technology, or other area by permission.
Research Proposal
DS 9700 Doctoral Internship/Research Lab
DS 9900 Dissertation
Dissertation Proposal Defense
DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

Relevant, interdisciplinary research forms the foundation of the Ph.D. in Data Science and Analytics. While students are encouraged to engage in research from their first semester, the last two years of the program are structured to help students transition into becoming independent, lead researchers. In this last stage of the program, students will work with research faculty, including their advisor, in one of our data science research labs.

Program Student Learning Outcomes

At the end of the program, students will be able to:

Demonstrate their understanding of the research process
Demonstrate mastery of core concepts relevant to three key areas in mathematics, statistics and computer science
Develop themselves as professionals prepared for work as a doctoral-educated individual beyond graduation

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

Applied Computer Science
Applied Economics and Statistics
Applied Statistics
Applied Mathematics
Bioinformatics
Business Analytics
Chemical Biology
Computer Science
Data Science
Forecasting & Strategic Management
Integrative Biology
Public Admin in Economic Policy Mgmt
Mathematics
Mechanical Engineering
Software Engineering

What is the Project Engagement requirement?

Can I pursue the program part- time while I am working full-time?

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

Ajou University, South Korea
Albert-Ludwigs University of Freiburg
Auburn University
Bowling Green State University
Clemson University
Columbia University
Columbus State University
Florida State University
Georgia Southern University
Georgia State
Georgia Tech
Iran University of Science and Technology
Kennesaw State University
Marshall University
Michigan State University
Murray State University
North Carolina State University
St. Petersburg State University, Russia
University of KwaZulu-Natal, South Africa
University of Michigan
University of North Carolina
University of Toledo

Ph.D. in Data Science and Analytics Student Cohorts

Royce Alfred

Bachelor's Degree: Psychology, Kennesaw State University

Master's Degree: Applied Statistics and Analytics, Kennesaw State University

Work History: 4 years as a Data Scientist at Equifax

Professional Objective: Work as a research data scientist in the corporate environment

Venkata Abhiram Chitty

Bachelor's Degree: Mathematics, Statistics and Computer Science, Osmania University, Telangana, India

Master's Degree: Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India

Professional Objective: To apply my Data Science skills in public health domain and help the society

Caleb Greski

Bachelor's Degree:

Master's Degree:

Work History:

Courses Taught:

Publications:

Professional Objective:

Moukthika Kadaparthi

Bachelor's Degree: Electrical and Electronics Engineering, SASTRA Deemed University

Master's Degree: Computers and Information Science, Cleveland State University

Work History:

Business Intelligence Analyst, Philips Healthcare, Georgia
Graduate Research Assistant, Cleveland State University, Ohio

Professional Objective: My objective is to enter academia with the aim of sharing the practical applications of data science in diverse domains and its potential positive impacts. With my unique blend of academic rigor and industry experience, I am driven to analyze complex data sets using cutting-edge data science techniques, to provide actionable insights and support data-driven decision-making.

Bachelor's Degree: Civil Engineering, Huazhong University of Science and Technology, China

Master's Degree: Business Analytics, Syracuse University

Credit Modeling Analyst, Agricultural Development Bank of China
Research Assistant, Changjiang Securities
Graduate Assistant, Syracuse University

Courses Taught: Calculus I, Marketing Analytics, Data Mining

Awards: Merit-Based Scholarship, Syracuse University

Professional Objective: To secure a challenging position in a reputable organization to expand myself within the field of Artificial Intelligence.

Kausar Perveen

Bachelor's Degree: Bachelor in Engineering Software Engineering, National University of Sciences and Technology, Pakistan

Master's Degree: Masters in Data Science, Illinois Institute of Technology, Chicago

Fullstack Developer at ItRunsInMyFamily, Charleston, South Carolina
Software Engineer II , Xgrid Pakistan
Senior Research Coordinator, Aga Khan University Pakistan
Machine Learning Engineer, Agoda Thailand

Publications: National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan

Service and Awards:

Fulbright Scholarship award for Master’s degree in Data Science
Aga Khan Education Service Pakistan, merit cumulative need based scholarship for Bachelors in Software Engineering

Professional Objective: My main motivation behind getting a degree in Data Science is to receive and perform qualified research experience in Data Science and public health

Bachelor's Degree: Statistics, University of Dhaka, Dhaka, Bangladesh

Master's Degree: Mathematics (Statistics Concentration), University of Toledo, Ohio

Analytics Engineer Intern, Cooper Smith, Toledo, Ohio
Business AnalystAkij Food and Beverage Limited, Dhaka, Bangladesh

Courses Taught: Introduction to Statistics

Professional Objective: I am interested to work as a data scientist in the industry

Ayomide Isaac Afolabi

Bachelor's Degree: Chemical Engineering, Ladoke Akintola University of Technology

Master's Degree: Data Science, Auburn University

Work History: Graduate Research Assistant, Auburn University

Courses Taught: Python Programming

Publications: Larson EA, Afolabi A, Zheng J, Ojeda AS. Sterols and sterol ratios to trace fecal contamination: pitfalls and potential solutions. Environ Sci Pollut Res Int. 2022 Jul;29(35):53395-53402. doi: 10.1007/s11356-022-19611-2 . Epub 2022 Mar 14. PMID: 35287190

Professional Objective: To work as a research data scientist in the industry

Dinesh Chowdary Attota

Bachelor's Degree: Computer Science, Jawaharlal Nehru Technological University Kakinada (JNTUK), India

Master's Degree: Computer Science, Kennesaw State University

Work History: Associate Consultant, SL Techknow Solutions India Pvt Ltd, India 2018 - 2020

Publications:

An Ensemble Multi-View Federated Learning Intrusion Detection for IoT
A Conversational Recommender System for Exploring Pedagogical Design Patterns
An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning

Professional Objective: I'd like to be a faculty member at a university so that I can continue to do research.

Nzubechukwu Ohalete

Bachelor's Degree: Mathematics,University of Nigeria, Nsukka

Master's Degree: Applied Statistics, Bowling Green State University

Work History: Graduate Assistant/Data Analyst, Federal University of Technology, Owerri - Mathematics Department

Courses Taught: Elementary Mathematics, Mathematical Methods

Awards: James A. Sullivan Outstanding Graduate Student Award, Applied Statistics and Operations Research Department, April 2022

Professional Objective: To use data science techniques to solve problems which makes our lives better and also makes our world a better place

Ryan Parker

Bachelor's Degree: Microbiology, University of Tennessee - Knoxville

Master's Degree: Integrative Biology, Kennesaw State University

Work History: Instructor of Biology, Kennesaw State University

Courses Taught: Nursing Microbiology Lectures and Labs, Introductory Biology Labs, Biotechnology Lectures and Labs

Parker RA, Gabriel KT, Graham K, Cornelison CT. Validation of methylene blue viability staining with the emerging pathogen Candida auris. J Microbiol Methods. 2020 Feb;169:105829. doi: 10.1016/j.mimet.2019.105829 . Epub 2019 Dec 27. PMID: 31884053.
Parker RA, Gabriel KT, Graham KD, Butts BK, Cornelison CT. Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens. 2022 Jul 22;11(8):821. doi: 10.3390/pathogens11080821 . PMID: 35894044; PMCID: PMC9331469.

Awards: Best Graduate Poster: Symposium for Student Scholars hosted by Kennesaw State University (Fall 2018) for Poster: "Antifungal Activity of Select Essential Oils and Synergism with Antifungal Drugs against Candida auris"

Professional Objective : To apply Data Science techniques to large scientific datasets, such as genomic and astronomical data, and to help bridge the gap between disparate fields by working in an interdisciplinary space to offer integrative and data-driven solutions to the increasingly complex problems presented to the traditional Sciences.

Askhat Yktybaev

Bachelor's Degree: Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia

Master's Degree: Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia; Public Administration in Economic Policy Management, School of International and Public Affairs, Columbia University

Work History:

from Data Analyst to Head of Research Unit, Central Bank of Kyrgyz Republic
Sr. Data Scientist in OJSC, Aiyl Bank, Kyrgyzstan
Consultant, The World Bank, Washington D.C.

Courses Taught: Financial Programing in the Central Bank, Monetary Policy Transmission Mechanism

Service and Awards: Winner of the Joint Japan/World Bank Graduate Scholarship Program, National Bank Silver Medal for Best Forecast

Professional Objective: I want to found a successful Fintech startup one day.

Sanad Biswas

Bachelor's Degree: Statistics, Biostatistics and Informatics, University of Dhaka, Bangladesh

Master's Degree: Statistics, University of Toledo, OH

Research Assistant: US Army Research Lab, Kennesaw State University
Consultant, Statistical Consulting Service, University of Toledo
Graduate Teaching Assistant, University of Toledo

Courses Taught: Calculus and Business Calculus, Facilitated students’ study of Statistics courses at the University of Toledo.

Professional Objective: To work as a researcher in the industry or as a faculty. I am primarily interested in the application of machine learning in different fields.

Mallika Boyapati

Bachelor's Degree: Electronics and Computer Engineering, K L University, India

Master's Degree: Applied Computer Science, Columbus State University

T-Mobile, Seattle, WA, USA: Sr. Data analyst, 2018- 2021
UITS, Columbus State University, Columbus, GA, USA: Data Analyst -Graduate assistant, 2016-2018
Menlo Technologies, India: Jr. Data Analyst, Intern, 2014- 2016

Courses Taught: DATA 4310 - Statistical Data Mining

Publications:

Anti-Phishing Approaches in the Era of the Internet of Things. In: Pathan, AS.K. (eds) Towards a Wireless Connected World: Achievements and New Technologies. Springer, Cham - https://doi.org/10.1007/978-3-031-04321-5_3
An empirical analysis of image augmentation against model inversion attack in federated learning - https://doi.org/10.1007/s10586-022-03596-1
M. Boyapati and R. Aygun, "Phishing Web Page Detection using Web Scraping," SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 167-174, doi: 10.1109/SoutheastCon51012.2023.10115148.
M. Boyapati and R. Aygun, "Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 139-142, doi: 10.1109/ICSC56153.2023.00029.
Boyapati, M., Aygun, R. (2023) Explainable Machine Learning for Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering. In Encyclopedia with Semantic Computing and Robotic Intelligence VOL. 0 https://doi.org/10.1142/S2529737623500119
Winners of Dataiku March Madness Bracket-thon, 2021 in predicting the NBA bracket
Winners of 2021 Analytics Day Ph.D. level research poster presentation

Professional Objective: To leverage strong analytical and technical abilities to research and develop effective data models, visualize data, and uncover insights that makes an impact in field of data science

Nina Grundlingh

Bachelor's Degree: Applied Mathematics and Statistics, University of KwaZulu-Natal, South Africa

Master's Degree: Statistics, University of KwaZulu-Natal, South Africa

Courses Taught: Introduction to Statistics, University of KwaZulu-Natal

Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in South Africa. The 61st conference of the South African Statistical Association, 27-29 November 2019, Nelson Mandela University, South Africa.
Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in the South African population. College of Agriculture, Engineering and Science Postgraduate Research & Innovation Symposium 2019, 17 October 2019, University of KwaZulu-Natal, Westville, South Africa (the award for best MSc presentation was also received for this).
Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling risk factors of diabetes and pre-diabetes in South Africa. IBS SUSAN-SSACAB 2019 Conference, 8-11 September 2019, Cape Town, South Africa.
University of KwaZulu-Natal Postgraduate Research & Innovation Symposium 2019 – Best Masters oral presentation
South African Statistical Association Honours Project Competition 2018/2019 – 2nd place and special prize for best use of SAS

Professional Objective: To work in a teaching position – sharing how data science can be applied to different fields and the positive impact it could have. I would like to use my theological background and passion to bring insight, clarity, and wisdom to data science problems.

Namazbai Ishmakhametov

Bachelor's Degree: Specialist in Mathematical Methods in Economics, Kyrgyz-Russian Slavic University

Master's Degree: Analytics, Institute for Advanced Analytics at North Carolina State University

Expert at the Centre for Economic Research, National bank of the Kyrgyz Republic
Consultant in World Bank project dedicated to strengthening the regulatory practices in Kyrgyz Republic
Consultant at Deloitte Consulting LLP, Science Based Services group, Analytics & Cognitive offering
Macroeconomic modeling expert in the Economic Department, National bank of the Kyrgyz Republic

Courses Taught: Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan

Ishmakhametov Namazbai, Abdygulov Tolkunbek, Jenish Nurbek. 2020. “ Impact of 2014-2015 shocks on economic behavior of the households in the Kyrgyz Republic ". Working Paper of the National Bank of the Kyrgyz Republic
Sherrill W. Hayes, Jennifer L. Priestley, Namazbai Ishmakhametov, Herman E. Ray. 2020. “ I’m not Working from Home, I’m Living at Work ”: Perceived Stress and Work-Related Burnout before and during COVID-19”. PsyArxiv Preprints
Ishmakhametov Namazbai, Arykov Ruslan. 2016. “ Credit Risk Model on the Example of the Commercial Banks of the Kyrgyz Republic ”. Working Paper of the National Bank of the Kyrgyz Republic
Namazbai Ishmakhametov, Anvar Muratkhanov.2015. “Modeling strategy of the Bank of the Kyrgyz Republic”. National bank of Poland – Swiss National bank joint seminar. Zurich, Switzerland

Professional Objective: To apply my quantitative skills in the field of biotech either in corporate or government sector

Symon Kimitei

Bachelor's Degrees: Mathematics, Kennesaw State University, and Computer Science, Kennesaw State University

Master's Degree: Mathematics (Scientific Computing Concentration), Georgia State University

Work History: Senior Lecturer and Math Department Coordinator of Supplemental Instruction, Kennesaw State University

Courses Taught: Calculus 1, Precalculus, Applied Calculus & College Algebra

Haskin, S., Kimitei, S., Chowdhury, M., Rahman, F., Longitudinal Predictive Curves of Health-Risk Factors for American Adolescent Girls. Journal of Adolescent Health. JAH-2021-00601R1
Symon K Kimitei, Algorithms for Toeplitz Matrices with Applications to Image Deblurring . 2008. Georgia State University, Masters thesis. ScholarWorks

Poster Presentations:

Kimitei, Symon & Sammie Haskin. "Nadaraya-Watson Kernel Regression Longitudinal Analysis of Healthcare Risk Factors of African American and Caucasian American Girls." Kennesaw State University R Day Presentation. 11 Nov. 2019. Poster presentation.
Kimitei, Symon. " Social Network Analysis in Supreme Court Case Rulings by Precedence Using SAS Optgraph/Python." 23rd Annual Symposium of Scholars. Kennesaw State University. 19 April. 2018. Poster presentation.

Professional Objective: As a Ph.D. student in Analytics & Data Science, I hope to gain skills in the program that will propel me into a Data Scientist / Machine Learning Engineer with a specialization in the design and implementation of deep learning & machine learning algorithms.

Jitendra Sai Kota

Bachelor's Degree: Computer Science & Engineering, Amrita Vishwa Vidyapeetham, India

Master's Degree: Computer Science, Florida State University

Work History: Teaching Assistant Professor in Computer Science at an Engineering College in India

Courses Taught: Problem Solving & Program Design through C, Artificial Intelligence, Data Mining

Publications: Kota, Jitendra Sai, Vayelapelli, Mamatha. 2020. "Predicting the Outcome of a T20 Cricket Game Based on the Players' Abilities to Perform Under Pressure". IEIE Transactions on Smart Processing and Computing 9(3):230-237. DOI: 10.5573/IEIESPC.2020.9.3.230

Professional Objective: to work in Data Science in a Corporate Environment

ResearchGate

Catrice Taylor

Bachelor's Degree: Economics, Clemson University

Master's Degrees: Applied Economics and Statistics, Clemson University, and Applied Statistics, Kennesaw State University

Professional Objective: To work as an industry data scientist in a corporate environment

Sahar Yarmohammadtoosky

Bachelor's Degree: Applied Mathematics, Sheikh Bahaei University, Isfahan, Iran

Master's Degree: Applied Mathematics, Iran University of Science & Technology, Tehran, Iran

Courses Taught: Numerical Analysis and Linear Algebra, Iran University of Science & Technology

Publications: Noah, G., Sahar, Y., Anthony P. & Hung, C.C. "ISODS: An ISODATA-Based Initial Centroid Algorithm". Accepted to: 10th International Conference on Information, March 6 - 8, 2021, Hosei University, Tokyo, Japan

Professional Objective: My goal is to become a competent Data Science specialist capable of using my skills to bring meaning to data, getting a faculty position at a university

Martin Brown

Bachelor's Degree: Mathematics, Swansea University, United Kingdom

Master's Degree: Mathematics, Murray State University

Graduate Research Assistant, Kennesaw State University, August 2020 to present
Graduate Teaching Assistant, Murray State University, August 2018 to May 2020

Course Taught: Problem Solving in Mathematics

Publications: Brown, Martin K. W. "Evaluating an Ordinal Output using Data Modeling, Algorithmic Modeling, and Numerical Analysis" (2020). Murray State Thesis and Dissertations 168 .

Awards: David Pryce History of Mathematics Prize 2017-2018

Professional Objective: To pursue a career in data science, machine learning, and predictive analytics to solve real-world issues

Inchan Hwang

Bachelor’s Degree: Computer Science, Georgia Southwestern State University

Master’s Degree: Software Engineering, Ajou University, South Korea

Courses Tutored: Precalculus, College Algebra, Calculus I at Georgia Southwestern State University

Tutoring College Algebra, Calculus I and II at Academic Skills Center, Georgia Southwestern State University Research Assistant at Intelligence of HyperConnected Systems Lab of Ajou University Fullstack web developer, windows system programmer in the cybersecurity industry Professional Objective: To work in big data analytics, and research and development of machine learning in engineering, and security

Duleep Prasanna Rathgamage Don

Bachelor's degree: Physics and Mathematics, The Open University of Sri Lanka

Master's degree: Mathematics, Georgia Southern University

Graduate Teaching Assistant, Georgia Southern University, 2016 - 2018
Graduate Teaching Assistant, University of Wyoming, 2019 - 2020

Courses Taught: Trigonometry, and Calculus I & II

Publications/Presentations:

Don, R. D. and Iacob, I. E., ‘DCSVM: Fast Multi-class Classification using Support Vector Machines’, International Journal of Machine Learning and Cybernetics .
Rathgamage Don, D., Iacob, E., ‘Divide and Conquer Support Vector Machine for Multiclass Classification’, Research Symposium (2018), Georgia Southern University.
Rathgamage Don, D., Iacob, E., ‘Multiclass Classification using Support Vector Machines’, MAA Southeastern Section Meeting (2018), Clemson University.

Professional Objective: To work in big data analytics, and research and development of machine learning in engineering, and medicine

Linglin Zhang

Bachelor’s Degree: Biological Sciences, Hubei University, China

Master’s Degree: Chemical Biology, University of Michigan and Bioinformatics, Georgia Institute of Technology

Selected Publications: Rebecca Shen, Zhi Li, Linglin Zhang, Yingqi Hua, Min Mao, Zhicong Li, Zhengdong Cai, Yunping Qiu, Jonathan Gryak, Kayvan Najarian. (2018). Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 690-693, 2018.

Professional Objective: To become a researcher in industry or academia. My background in Biology and Bioinformatics could provide me strong theoretical support on a research role in the health industry. The experience of doing an internship at Equifax equipped me of certain knowledge on business cases.

Yihong Zhang

Bachelor’s Degree: Psychology Mathematics Interdisciplinary, Chatham University

Master’s Degree: Mathematics and Statistics Allied with Computer Science, Georgia State University

Research Assistant - Collaborated with biomedical department to analyze and visualize microarray gene expression data, Facilitated in data pre-processing and machine learning modeling of clinical liver cirrhosis image data, Assisted in feature engineering of image analysis in deep learning for pathology diagnosis with Mayo Clinic’s pilot project.
Graduate Lab Assistant - Tutored students with statistics and math subjects.

Professional Objective: Make better use of data in healthcare and bioinformatic industry as a data scientist.

2019 - 2020

Trent Geisler

Graduation Date: Summer 2022

Dissertation: Novel Instance-Level Weighted Loss Function for Imbalanced Learning

Dissertation Advisor: Dr. Herman Ray

Current Position: Assistant Professor, Department of Systems Engineering, United States Military Academy West Point

Srivatsa Mallapragada

Bachelor’s Degree: Mechanical Engineering, Andhra University College of Engineering, India

Master’s Degree: Mechanical Engineering, University of North Carolina at Charlotte

Continuous Improvement Intern, Daimler Trucks North America at Cleveland, North Carolina, USA Computational Fluid Dynamics (CFD) Graduate Research Assistant, NC Motorsports and Research Laboratory Manufacturing Intern, Caterpillar India Pvt Ltd, Sriperambudur, India Selected Publications/Presentations:

Mallapragada, S. (2017). Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side Wall (Master’s thesis, The University of North Carolina at Charlotte). Uddin, M., Mallapragada, S., & Misar, A. (2018). Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side-Wall (No. 2018-01-0704). SAE Technical Paper. Dimensionality Reduction of Hyperspectral Images for Classification, Srivatsa, M., Michael, W. & Hung, C. C. Ninth International Conference on Information ISSN: 1343-4500 Bounds, C., Mallapragada, S., and Uddin, M., "Overset Mesh-Based Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side-Wall," SAE Int. J. Passeng. Cars - Mech. Syst. 12(3):211-223, 2019, https://doi.org/10.4271/06-12-03-0015. Service and Awards: Base SAS Programmer V9 Professional Objectives: I am currently working in unsupervised pattern recognition in high dimensional data sets. After I graduate, I would like to pursue a career in Data Science and Machine Learning in the corporate environment.

Sudhashree Sayenju

Graduation Date: Spring 2023

Dissertation: Quantification and Mitigation of Various Types of Biases in Deep NLP Models

Dissertation Advisor: Dr. Ramazan Aygun

Christina Stradwick

Bachelor’s Degree: Music Performance and Mathematics, Marshall University

Master’s Degree: Mathematics with Emphasis in Statistics, Marshall University

Courses Taught: Prep for College Algebra at Marshall University

Selected Presentations:

Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

Professional Objectives: To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.

2018 - 2019

Md Shafiul Alam

Graduation Date: Fall 2022

Dissertation: Appley: App roximate Shap ley Values for Model Explainability in Linear Time

Dissertation Advisor: Dr. Ying Xie

Current Position: AI Framework Engineer, Intel Corporation

Jonathan Boardman

Dissertation: Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics

Current Position: Data Scientist, Equifax

Tejaswini Mallavarapu

Bachelor’s Degree: Pharmacy, Acharya Nagarjuna University, India

Master’s Degree: Computer Science, Kennesaw State University

Graduate Research Assistant, Kennesaw State University, 2017-present
Research Analyst, Divis Laboratories, 2013-2014

Selected Publications:

T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Accepted, 2017.
M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Ch. MadhusudhanaRao, M. Tejaswini, "Design and Evaluation of Binding Properties of Cassia roxburghii Seed Galacto mannan and Moringa oleifera Gum in the Formulation of Paracetamol Tablets," Research Journal of Pharmacy and Technology(RJPT). 3(1): Jan.-Mar. 2010; Page 254-256.
M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Y.V. Kishore Reddy, M. Tejaswini, Ch. MadhusudhanaRao, V. Tejopavan, "Cassia roxburghii Seed Galacto manna— a potential binding agent in the tablet formulation," Journal of Biomedical Science and Research(JBSR), Vol 2 (1), 2010, 18-22

Professional Objective: To be a data scientist in the field of health care or bioinformatics where I can leverage my analytical skills and knowledge towards the advancement of the research field.

Seema Sangari

Dissertation: Debiasing Cyber Incidents - Correcting for Reporting Delays and Under-reporting

Dissertation Advisor: Dr. Michael Whitman

Current Position: Principal Modeler, HSB

Srivarna Settisara Janney

Bachelor’s Degree: Mechanical Engineering, Visveswaraiah Technological University, India

Graduate Research Assistant, Kennesaw State University, 2016-2018
Senior Software Engineer, Torry Harris Business Solutions (THBS), United Kingdom, 2010-2012 and India, 2012-2014
Software Engineer, Torry Harris Business Solutions (THBS), India, 2007-2010

Selected Publications/Presentations:

S.S. Janney, S. Chakravarty, “New Algorithms for CS – MRI: WTWTS, DWTS, WDWTS”, One-page research paper, 40th International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Jul 2018
Master thesis presented at Southeast Symposium on Contemporary Engineering Topics (SSCET), UAH Engineering Forum, Alabama, Aug 2018
Master thesis poster is accepted to be presented at Biomedical Engineering Society (BMES) 2018 Annual Meeting, Oct 2018
Submitted draft copy for book chapter contribution on “Bioelectronics and Medical Devices”, Elsevier Publisher, May 2018
Showcased 3MT, Georgia Council of Graduate Schools (GCGS), Apr 2018
Master thesis presented in workshop for “Medical Signal and Image Processing” at Department of Biotechnology & Medical Engineering, NIT Rourkella, Feb 2018
S.S. Janney, I. Karim, J. Yang, C.C Hung, Y. Wang, “Monitoring and Assessing Traffic Safety Using Live Video Images”, GDOT project showcase, 4th Annual Transportation Research Expo, Sept 2016
1st Place Winner, Graduate Research Project, C-day Poster Presentation, Kennesaw State University, Spring 2018
People's Choice Award, 3 Minute Thesis (3MT), Apr 2018
CCSE Dean’s 4.0 Club, Jan 2018
3rd Place Winner, Hackathon 2017 - HPCC Systems Big Data
Foundation of Computer Science, Certified by Kennesaw State University, Jun 2016
Fundamental of RESTful API Design, Certified by APIGEE, Nov 2014
Member of HandsOnAtlanta, since 2014
SOA Associate, Certified by IBM, Jun 2008

Professional Objective: I would like to be a researcher in Data Science and Analytics in medical imaging technologies contributing to advancements that would help medical and healthcare professionals provide value-based and personalized health care. I would like to look at career opportunities in industry and academia that fuel my interest in research.

2017 - 2018

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

Current Position: Assistant Professor, Saint Joseph's University - Erivan K. Haub School of Business

Mohammad Masum

Dissertation: Integrated Machine Learning Approaches to Improve Classification Performance and Feature Extraction Process for EEG Dataset

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

Dissertation: A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in the Episodes of Care Healthcare Delivery System

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

Dissertation: Tree-BERT - Advanced Representation Learning for Relation Extraction

Dissertation Advisor: Dr. Ying Xie

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

Dissertation: Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

Dissertation: Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

Dissertation: A Novel Penalized Log-likelihood Function for Class Imbalance Problem

Current Position: Data Scientist/Research Engineer, Hewlett Packard Enterprise

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date: Spring 2020

Dissertation: A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data

Dissertation Advisor: Dr. Stefano Mazzotta

Current Position: Assistant Professor, Business Analytics, Tennessee Technological University

Bogdan Gadidov

Graduation Date: Summer 2019

Dissertation: One- and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

Dissertation: Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Dissertation Advisor: Dr. Mingon Kang

Current Position: Assistant Professor, Chinese Academy of Medical Sciences, Peking Union Medical College

Graduation Date: Spring 2019

Dissertation: Deep Embedding Kernel

Current Position: Assistant Professor, Information Technology, Kennesaw State University

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation: Ordinal Hyperplane Loss

Dissertation Advisor: Dr. Ying Xie

Current Position: Principal Data Scientist, Microsoft

Contact Info

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Marietta Campus 1100 South Marietta Pkwy Marietta, GA 30060

Campus Maps

Phone 470-KSU-INFO (470-578-4636)

kennesaw.edu/info

Media Resources

Resources For

Graduate Studies

$UCLA Mathematics$

Master of Science in Mathematics and Applied Statistics

At California State University Long Beach

The Department of Mathematics & Statistics at CSULB offers four Master of Science programs .

Teaching & Graduate Assistantships provide students with funding and with college teaching experience.

Graduates have found employment in both technical and academic workplaces. Many have obtained tenure-track community college professorships. Others have gone on to PhD programs.

MS in Mathematics, General Option

Study and explore concepts in areas including analysis, algebra, topology, and geometry, as well as the deep connections between and among these subjects.

MS in Mathematics, Option in Applied Mathematics

Study applied math methods with an emphasis on computational skills.

MS in Mathematics, Option in Mathematics Education for Secondary School Teachers

A flexible program that includes coursework in mathematics and in mathematics education research & theory.

MS in Applied Statistics

Using conceptual foundations and statistical software packages ( SAS , R , and Python ), students are trained to analyze real world data appropriately and communicate their findings effectively. The tools learnt here will open the door for careers in data science and analytics, or prepare you for a PhD in a variety of related fields.

More Information & Application Instructions

Apply for Teaching and Graduate Assistantships

Dr. John Brevik, Pure Mathematics Graduate Advisor, [email protected]

Dr. Paul Sun, Applied Math Graduate Advisor, [email protected]

Dr. Xuhui Li, Mathematics Education Graduate Advisor, [email protected]

Dr. Kagba Suaray, Statistics Graduate Advisor, [email protected]

MyU : For Students, Faculty, and Staff

UNITE Fall 2024 Course Offerings

UNITE Distributed Learning provides access to live streaming video of class sessions plus same-day access to streaming video archives and downloadable video and audio files of course meetings to the students who enroll through UNITE, "piggybacking" on an on-campus section of the course in a UNITE-enhanced classroom.

Semester Schedule

The UNITE sections of a course follow the same semester schedule as the on-campus section of the course. This includes exams (which may be required synchronous events - see below) and homework deadlines as well as University deadlines for adding courses, cancelling courses, refunds, etc.

Exams, Presentations and Homework

Assessments (exams, presentations, homework, etc.) vary class-to-class, instructor-to-instructor. Note that some courses require that exams be taken at the same time/same day as the on-campus section of the course upon which UNITE is "piggybacking" for UNITE-enrolled students as well as live student presentations to the class.

Courses Exams Requiring Synchronous, Live Proctoring

For courses in which the instructor is holding in-class, proctored exams for those enrolled in the on-campus sections, students enrolled through UNITE are REQUIRED to take exams on the same day/same time as the students enrolled in the on-campus sections of the course with a UNITE-approved proctor.

Any deviation from the same day/same time proctored exams for these courses - including the request to take the exams with the on-campus students - must be approved by the instructor. UNITE will NOT grant these permissions. Work out these arrangements with the instructor before the 100% refund period ends.

Students who arrange to come to campus and take in-class, proctored exams with the students enrolled in the on-campus section of a course do not need to find/submit a local proctor - note that this must be arranged with the instructor to verify permission/space (enrollment in a UNITE section does not hold a physical classroom seat in the classroom).

Students are responsible for finding and submitting proctor information to UNITE to evaluate and approve. UNITE will contact all students enrolled through UNITE to initiate this process shortly after the semester begins.

Final Exams: Final exam dates are posted in the official University of Minnesota Class Schedule. UNITE will stream video on Saturdays. If you are enrolled in a UNITE section with an exam on a Saturday, you will need to have a proctor administer the exam. If you need to make other arrangements you will need to contact the instructor directly to seek approval.

Courses with Exams Not Requiring Live, In-Person Proctoring

For courses for which the instructors are using other types of exams - take-home exams, online exams (with a video proctoring service or without) - instead of in-class, proctored exams, there is no need for students who enroll in the UNITE section of a course to find and submit a proctor to UNITE for approval.

Presentations

For courses with required live presentations by students - individually or as a group - UNITE will work with the student(s) and instructor to provide a live webconference between the remote student(s) and the classroom in real time. In some instances, UNITE-enrolled students are able to join the on-campus students in the classroom to present in person (though that is not required). For courses with required, live presentations it is best to note that commitment for the course with the instructor before the 100% refund period ends.

Homework Submission and Return

Increasing, faculty and TAs are using Canvas course sites for submission and return of homework.

For those faculty and TAs who do not, homework may be submitted to UNITE via email. Our office will record submissions and deliver to instructors and/or TAs for grading. Graded materials will be returned to your University email account when we receive it.

For more information, refer to the "Step Two: Know How UNITE Works" of UNITE Steps to Success .

The courses offered are subject to change. For the summer semester, UNITE will stop recording/streaming a course if there are no students enrolled in that course through UNITE.

Course descriptions taken from the University of Minnesota's Schedule Builder . Courses topics may be revised per instructor. Contact instructor for more detailed and up-to-date information.

Grad 0999 – 51566 Call Number – UNITE students must register online themselves for this status. Graduate students registering for this status must register before the semester begins or they will be charged the normal late registration fees.

Undergraduate students taking classes on campus may enroll in UNITE courses with instructors' permission. Learn more about Undergraduate Credit Enrollment though UNITE .

Please note Important Fall Semester Dates .

Students enrolled in on-campus sections have limited access to UNITE Media; refer to UNITE Streaming Video Access for On-Campus Students for more details.

FALL SCHEDULE

(Updated April 2nd, 2024)

Use online tools to search all University credit offerings: Aerospace Engineering's Class Schedules by Department online search tool Humphrey School of Public Affairs' ClassInfo online search tool (Note: These tools list ALL offerings - on-campus, including UNITE offerings)

AEROSPACE ENGINEERING

AEM 5321 (also offered as EE 5231) - Linear Systems and Optimal Control (3.0 cr) Instructor TBA UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [EE 3015, CSE grad student] or instr consent Description: Properties and modeling of linear systems. Linear quadratic and linear-quadratic-Gaussian regulators. Maximum principle.

AEM 5401 - Intermediate Dynamics (3.0 cr) Yohannes Ketema UNITE streams live video of on-campus section on MWF 11:15 a.m.–12:05 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE upper div or grad, 2012, Math 2243 Description: Three-dimensional Newtonian mechanics, kinematics of rigid bodies, dynamics of rigid bodies, generalized coordinates, holonomic constraints, Lagrange equations, applications.

AEM 5451 (also offered as EE 5251) - Optimal Filtering and Estimation (3.0 cr) Demoz Gerbe-Egziabher UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; EE 3025, EE 4231 recommended Description: Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications.

BIOMEDICAL ENGINEERING

BMEN 5001 - Advanced Biomaterials (3.0) Wei Shen UNITE streams live video of on-campus section on TTh 11:15 a.m. - 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 3301 or MatS 3011 or grad student or instr consent Description: Commonly used biomaterials. Chemical/physical aspects. Practical examples from such areas as cardiovascular/orthopedic applications, drug delivery, and cell encapsulation. Methods used for chemical analysis and for physical characterization of biomaterials. Effect of additives, stabilizers, processing conditions, and sterilization methods.

BMEN 5401 - Advanced Biomedical Imaging (3.0 cr) Alexander Opitz UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE upper div or grad student or instr consent Description: Functional biomedical imaging modalities. Principles/applications of technologies that offer high spatial/temporal resolution. Bioelectromagnetic and magnetic resonance imaging. Other modalities.

BMEN 5411 - Neural Engineering (3.0 cr) Tay Netoff UNITE streams live video of on-campus section on TTh 11:15 a.m. - 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: BMEN 3401 recommended Description: Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus.

BMEN 5910 - Special Topics in Biomedical Engineering: Biomedical Science Data (3.0 cr) Matthew Johnson UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE student, upper div or grad Description: Description coming from department.

BMEN 8001 - Polymeric Biomaterials (3.0 cr) Chun Wang UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [5001, [CHEN 4214 or MATS 4214 or equiv]] or instr consent Description: Introduction to polymeric biomaterial research. Molecular engineering, characterization of properties, material-cell interaction, biocompatibility/bioactivity. Applications in biology and medicine.

BMEN 8601 - Biomedical Engineering Seminar (1.0 cr) Seminars and Colloquia taken for credit are offered only as live and archived streaming video - NO downloadable video or audio podcast versions are offered. Wei Shen UNITE streams live video of on-campus section on MW 3:35 p.m. - 4:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Description: Lectures and demonstrations of university and industry research introducing students and faculty to methods and goals of biomedical engineering. For more information, see the Biomedical Engineering Graduate Seminar Web Site .

Looking for a course not listed here? Ask for it! We already offer many College of Science and Engineering courses through UNITE, but are looking for other courses that we can offer through UNITE. Use our online Course Request Form .

NOTE: UNITE WILL NOT TAKE REQUESTS FOR ADDITIONAL COURSES FOR FALL 2024 AFTER AUGUST 1ST, 2024.

COMPUTER SCIENCE AND ENGINEERING

CSCI 5106 - Programming Languages (3.0) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 1:00 p.m.–2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 4011 or instr consent Description: Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages.

CSCI 5204 (also offered as EE 5364) - Advanced Computer Architecture (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 9:45 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 4203 or EE 4363; Credit will not be granted if credit has been received forEE 5364 Description: Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design.

CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on MW 8:15 a.m. - 9:30 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSCI 4041 or instr consent Description: Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains.

CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on MW 8:15 a.m. - 9:30 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 4041 or instr consent Description: Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.

CSCI 5481 - Computational Techniques for Genomics (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSCI 4041 or instr consent Description: Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks.

CSCI 5525 - Machine Learning (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Grad student or instr consent Description: Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models.

CSCI 5541 - Natural Language Processing (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 11:15 a.m.– 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSCI 2041 Description: Computers are poor conversationalists, despite decades of attempts to change that fact. This course will provide an overview of the computational techniques developed in the attempt to enable computers to interpret and respond appropriately to ideas expressed using natural languages (such as English or French) as opposed to formal languages (such as C++ or Python). Topics in this course will include parsing, semantic analysis, machine translation, dialogue systems, and statistical methods in speech recognition.

CSCI 5707 - Principles of Database Systems (3.0 cr) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 4041 or instr consent], grad student; Credit will not be granted if credit has been received for CSCI 4707 or INET 4707 Description: Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends.

CSCI 8115 - Human-Computer Interaction and User Interface Technology (3.0 cr) UNITE section enrollment limited by department to 10 Instructor TBA UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 5115 or instr consent Description: Current research issues in human-computer interaction, user interface toolkits and frameworks, and related areas. Research techniques, model-based development, gesture-based interfaces, constraint-based programming, event processing models, innovative systems, HCI in multimedia systems.

CSCI 8523 - AI for Earth: Monitoring Changes in the Environment via Deep Learning (3.0) UNITE section enrollment limited by department to 10 Vipin Kumar UNITE streams live video of on-campus section on MW 2:30 p.m.–3:45 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSci 5523, CSci 5521, or equivalent Description: Advances in machine learning in conjunction with massive amounts of data from Earth observing satellites offer huge potential for improving our understanding of how the Earth's environment and ecosystems have been changing and how they are being impacted by humans actions and changing climate. Deep learning approaches, that have had phenomenal success in the domain of computer vision and language/speech translation, hold promise in dealing with environmental problems. However, due to challenges that are unique to environmental applications, off-the-shelf deep learning techniques developed for related applications such as computer vision often have limited utility. This class will introduce to the students the promise and challenges in using deep learning techniques to analyze complex, multi-scale, spatio-temporal data for monitoring changes in the Earth and its environment on a global scale.

CSCI 8970 (also offered as DSCI 8970) - Computer Science Colloquium (1.0 cr) UNITE section enrollment limited by department to 10 Seminars and Colloquia taken for credit are offered only as live and archived streaming video - NO downloadable video or audio podcast versions are offered. Instructor TBA UNITE streams live video of on-campus section on M 11:15 a.m. - 12:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Description: Recent developments in computer science and related disciplines. Students must attend 13 of the 15 lectures. For the entire schedule, see the Computer Science & Engineering Colloquia Series Web Site

DATA SCIENCE

DSCI 8970 (also offered as CSCI 8970) - Data Science Colloquium (1.0 cr) UNITE section enrollment limited by department to 10 Instructor TBA UNITE streams live video of on-campus section on M 11:15 a.m. - 12:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Description: Recent developments in computer science and related disciplines. Students must attend 13 of the 15 lectures. For the entire schedule, see the Computer Science & Engineering Colloquia Series Web Site

ELECTRICAL AND COMPUTER ENGINEERING

EE 4389W (also offered as EE 5389) - Introduction to Predictive Learning (3.0 cr) Vladimir Cherkassky UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students Description: Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.

EE 4541 - Digital Signal Processing (3.0 cr) Georgios Giannakis UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3015, 3025] or instr consent Description: Review of linear discrete time systems and sampled/digital signals. Fourier analysis, discrete/fast Fourier transforms. Interpolation/decimation. Design of analog, infinite-impulse response, and finite impulse response filters. Quantization effects.

EE 5163 - Semiconductor Properties and Devices I (3.0 cr) Tony Low UNITE streams live video of on-campus section on MW 11:15 a.m. - 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3161, 3601, CSE grad student] or dept consent Description: Principles/properties of semiconductor devices. Selected topics in semiconductor materials, statistics, and transport. Aspects of transport in p-n junctions, heterojunctions.

EE 5171 - Microelectronic Fabrication (4.0 cr) Steven Koester UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student or dept consent Description: Fabrication of microelectronic devices. Silicon integrated circuits, GaAs devices. Lithography, oxidation, diffusion. Process integration of various technologies, including CMOS, double poly bipolar, and GaAs MESFET.

EE 5181 - Micro and Nanotechnology by Self Assembly (3.0 cr) Jeong-Hyun Cho UNITE streams live video of on-campus section on TTh 4:00 p.m. - 4:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: EE 3161, Phys 1302 Description: Self-assembly process of micro and nano structures for realization of 1-, 2-, 3-dimensional micro- and nano-devices. Micro and nanoscale fabrication by electrostatic, magnetic, surface tension, Capillary, intrinsic and extrinsic forces. Nanoscale lithographic patterning. Devices packaging, Self-healing process.

EE 5231 (also offered as AEM 5321) - Linear Systems and Optimal Control (3.0 cr) Instructor TBA UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [EE 3015, CSE grad student] or instr consent Description: Properties and modeling of linear systems. Linear quadratic and linear-quadratic-Gaussian regulators. Maximum principle.

EE 5239 - Introduction to Nonlinear Optimization (3.0) Mingyi Hong UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3025, Math 2373, Math 2374, CSE grad student] or dept consent Description: Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications.

EE 5241 - Optimal Control and Reinforcement Learning (3.0 cr) Andrew Lamperski UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student or instructor consent Description: A wide variety of control problems such as "walk from home to school via the shortest path" or "maintain a constant temperature" can be modeled using optimization. This course will survey a variety of methods for modeling and solving optimal control problems. In particular, we will cover numerical optimal control, model predictive control, system identification, dynamic programming, and reinforcement learning. Examples from robotics and aerospace systems will be given.

EE 5251 (also offered as AEM 5451) - Optimal Filtering and Estimation (3.0 cr) Demoz Gerbe-Egziabher UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; EE 3025, EE 4231 recommended Description: Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications.

EE 5271 - Robot Vision (3.0 cr) Changhyun Choi UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [Math 2373 or equivalent; EE 1301 or equivalent basic programming course] Description: Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning.

EE 5301 - VLSI Design Automation I (3.0 cr) Kia Bazargan UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [2301, CSE grad student] or dept consent Description: Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms.

EE 5323 - VSLI Design I (3.0 cr) Gerald Sobelman This course uses software that is only available to students in CSELabs due to vendor licensing - there is no off-campus software option. Students will need to come to campus to use the software. UNITE streams live video of on-campus section on MWF 3:35 p.m. - 4:25 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [2301, 3115, CSE grad student] or dept consent Description: Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing.

EE 5329 - VLSI Digital Signal Processing Systems (3.0 cr) Instructor TBA This course uses software that is only available to students in CSELabs due to vendor licensing - there is no off-campus software option. Students will need to come to campus to use the software. UNITE streams live video of on-campus section on MWF 3:35 p.m. - 4:25 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent Description: Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis.

EE 5333 - Analog Integrated Circuit Design Ramesh Harjani UNITE streams live video of on-campus section on TTh 8:15 a.m. - 9:30 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3115, CSE grad student] or dept consent Description: Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter).

EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing (3.0 cr) Ulya Karpuzcu UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student; A previous course in computer architecture is suggested but not required. Description: Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.

EE 5351 - Applied Parallel Programming (3.0 cr) John Sartori On-campus sections meets MW 1:25 p.m. - 2:40 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [4363 or equivalent], programming experience (C/C++ preferred) Description: Parallel programming/architecture. Application development for many-core processors. Computational thinking, types of parallelism, programming models, mapping computations effectively to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, application case studies.

EE 5364 (also offered as CSCI 5204) - Advanced Computer Architecture (3.0 cr) UNITE section enrollment limited by department to 10 Pen-Chung Yew UNITE streams live video of on-campus section on TTh 9:45 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [[4363 or CSci 4203], CSE grad student] or dept consent; Credit will not be granted if credit has been received for: CSCI 5204 Description: Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design.

EE 5389 (also offered as EE 4389W) - Introduction to Predictive Learning (3.0 cr) Vladimir Cherkassky UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: EE 3025, STAT 3022 or equivalent; computer programming or MATLAB or similar environment is recommended. Description: Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.

EE 5531 - Probability and Stochastic Processes (3.0 cr) Soheil Mohajer UNITE streams live video of on-campus section on MW 11:15 a.m. - 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3025, CSE grad student] or dept consent Description: Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks.

EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0) Mehmet Akcakaya UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [4541, 5581, CSE grad student] or instr consent Description: Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks.

EE 5601 - Introduction to RF/Microwave Engineering (3.0 cr) Rhonda Franklin UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [EE 3601, CSE grad student] or dept consent Description: Fundamentals of EM theory and transmission lines concepts. Transmission lines and network analysis. CAD tool. Lumped circuit component designs. Passive circuit components. Connectivity to central communication theme.

EE 5624 - Optical Electronics (4.0 cr) James Leger UNITE streams live video of on-campus section on TTh 2:30 p.m. - 4:10 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [[3601 or Phys 3002], CSE grad student] or dept consent Description: Fundamentals of lasers, including propagation of Gaussian beams, optical resonators, and theory of laser oscillation. Polarization optics, electro-optic, acousto-optic modulation, nonlinear optics, phase conjugation.

EE 5653 - Physical Principles of Magnetic Materials (3.0 cr) Randall Victora UNITE streams live video of on-campus section on MWF 2:30 p.m. - 3:20 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student or dept consent Description: Physics of diamagnetism, paramagnetism, ferromagnetism, antiferromagnetism, ferrimagnetism. Ferromagnetic phenomena. Static/dynamic theory of micromagnetics, magneto-optics, and magnetization dynamics. Magnetic material applications.

EE 5811 - Biological Instrumentation (3.0) Sang-Hyun Oh UNITE streams live video of on-campus section on TTh 11:15 p.m. - 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student Description: This course will cover the physics and technology of biological instruments. The operating principles of optical, electrical, and mechanical biosensors will be discussed, followed by transport and delivery of biomolecules to the sensors. Techniques to manufacture these sensing devices, along with microfluidic packaging, will be covered. Lectures will be complemented by lab demo sessions to give students hands-on experiences in microfluidic chip fabrication, microscopy, and particle trapping experiments.

EE 5940 - Special Topics in Electrical Engineering I (3.0) Instructor TBA UNITE streams live video of on-campus section on MW 9:45 p.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE student, upper div or grad Description: Course description coming from department.

EE 8351 - Design Automation Techniques for Variation-Aware Computer (3.0 cr) Instructor TBA UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student. Some background in VLSI design and/or design automation is suggested but not required. Such prior exposure will make the experience in the class much more meaningful. Description: High-performance chip design can only be performed with the assistance of design automation tools that comprehend the needs of the designer and deliver solutions that can correctly analyze and optimize these systems. The objective of this class is to provide a view of this emerging universe and acquaint students with new research in this area. Specific topics to be covered include 1) Overview of technology trends and emerging systems 2) Variation-aware design and 3) Design automation issues.

EE 8591 - Predictive Learning from Data Instructor TBA UNITE streams live video of on-campus section on TTh 11:15 a.m. - 12:30 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: CSE grad student or instr consent Description: Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of new algorithms.

EE 8660 - Magnetics Seminar (1.0 cr) Seminars and Colloquia taken for credit are offered only as live and archived streaming video - NO downloadable video or audio podcast versions are offered. Beth Stadler UNITE streams live video of on-campus section on F 1:25 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Description: Current literature, individual assignments (no online seminar schedule available to share).

INDUSTRIAL AND SYSTEMS ENGINEERING

IE 3521 - Statistics, Quality and Reliability (4.0 cr) Instructor TBA UNITE streams live video of on-campus section on TTh 3:35 p.m. - 5:20 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: MATH 1372 or equiv Description: Random variables/probability distributions, statistical sampling/measurement, statistical inferencing, confidence intervals, hypothesis testing, single/multivariate regression, design of experiments, statistical quality control, quality management, reliability, maintainability.

IE 5511 - Human Factors and Work Analysis (4.0 cr) Instructor TBA UNITE streams live video of on-campus section on TTh 10:10 a.m. - 12:05 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Upper div CSE or grad student; Credit will not be granted if credit has been received for: HUMF 5211, IE 4511 or ME 5211 Description: Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards.

IE 5531 - Engineering Optimization I (4.0 cr) Instructor TBA UNITE streams live video of on-campus section on MW 11:15 a.m. - 1:10 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Upper div or grad student or CNR Description: Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning.

IE 5532 - Stochastic Models (3.0 cr) Instructor TBA UNITE streams live video of on-campus section on TTh 10:10 a.m. - 12:05 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Undergraduate probability and statistics. Familiarity with computer programming in a high level language. Description: Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control.

IE 8521 - Optimization (4.0 cr) Instructor TBA UNITE streams live video of on-campus section on TTh 1:25 p.m. - 3:20 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Familiarity with linear algebra and calculus. Description: Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems.

IE 8564 - Optimization for Machine Learning (4.0 cr) Instructor TBA UNITE streams live video of on-campus section on M 2:45 p.m. - 6:05 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Graduate Student Description: Machine learning has been widely used in many areas such as computer vision, search engines, speech recognition, robotics, recommender systems, bioinformatics, social networks, and finance. It has become an important tool in prediction and data analysis. This course provides a comprehensive overview of important optimization models for machine learning. It also systematically provides a theoretical and computational study on various optimization methods for solving these models and more general problems.

MECHANICAL ENGINEERING

ME 5312 - Solar Thermal Technologies(3.0) Natasha Wright UNITE streams live video of on-campus section on MW 10:10 p.m.–12:05 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: [3333, CSE upper Div] or grad student Description: Solar radiation fundamentals. Measurement/processing needed to predict solar irradiance dependence on time, location, and orientation. Characteristics of components in solar thermal systems: collectors, heat exchangers, thermal storage. System performance, low-temperature applications. Concentrating solar energy, including solar thermo-chemical processes, to produce hydrogen/solar power systems and photovoltaics. Solar design project.

ME 8446 - Advanced Combustion (3.0) Sayan Biswas UNITE streams live video of on-campus section on TTh 11:15 a.m.–12:05 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: Undergrad courses in thermodynamics, fluid mechanics, heat transfer, IT grad student; 5446 or 8641 highly recommended Description: Fundamental understanding of linkage between thermodynamics, chemical kinetics, and transport phenomena in combustion systems. Heat release rate, flame stability, and emissions. How those issues arise in furnaces, internal combustion engines, and rockets.

STAT 5021 - Statistical Analysis (4.0 cr) Enrollment in STAT 5021 includes on-campus lab in section 2 of the lab sections (T 10:10 a.m. - 11:00 a.m.), live-streamed from a UNITE classroom Instructor TBA UNITE streams live video of on-campus lecture section on MWF 10:10 a.m. - 11:00 a.m. UNITE streams live video of on-campus lab section on T 10:10 a.m. - 11:00 a.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: college algebra or instr consent; credit will not be granted if credit has been received for STAT 3011 Description: Intensive introduction to statistical methods for graduate students needing statistics as a research technique.

STAT 5102 - Theory of Statistics II (4.0 cr) Enrollment in STAT 5101 includes on-campus lab in section 2 of the lab sections (T 2:30 p.m. - 3:20 p.m.), live-streamed from a UNITE classroom Instructor TBA UNITE streams live video of on-campus lecture section on MWF 2:30 p.m. - 3:20 p.m. UNITE streams live video of on-campus lab section on T 2:30 p.m. - 3:20 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 5101 or Math 5651 Description: Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory.

STAT 5302 - Applied Regression Analysis (4.0 cr) Enrollment in STAT 5302 includes on-campus lab in section 2 of the lab sections (Th 11:15 a.m. - 12:05 p.m.), live-streamed from a UNITE classroom Instructor TBA UNITE streams live video of on-campus lecture section on MWF 1:25 p.m. - 2:15 p.m. UNITE streams live video of on-campus lab section on Th 11:15 a.m. - 12:05 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions. Description: Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications.

STAT 5421 - Statistical Analysis (3.0 cr) Instructor TBA UNITE streams live video of on-campus section on MWF 1:25 p.m. - 2:15 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102 Description: Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/log linear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models.

STAT 5511 - Time Series Analysis (3.0 cr) Instructor TBA UNITE streams live video of on-campus section on MWF 2:30 p.m. - 3:20 p.m. Archived videos typically available to UNITE-enrolled students within an hour Prerequisites: STAT 4102 or STAT 5102 Description: Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models.

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COMMENTS

PhD in Statistics and Computer Science
The 4-year PhD in Statistics and Computer Science is a high profile and rigorous doctoral program, within the Department of Decision Sciences, that develops strong mathematical, statistical, computational and programming backgrounds. It arises as an expansion of the PhD in Statistics, which has been offered by the Department since 2001 and has ...
PhD Program
PhD Program. Wharton's PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as ...
PhD
The Doctor of Philosophy program in the Field of Statistics is intended to prepare students for a career in research and teaching at the University level or in equivalent positions in industry or government. A PhD degree requires writing and defending a dissertation. Students graduate this program with a broad set of skills, from the ability to ...
Statistics and Data Science
Statistics and Data Science. Wharton's PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer ...
Department of Statistics
PhD Program Overview. The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability.
PhD Program
PhD Program Overview. The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals.
PhD Admissions
Our PhD program welcomes students from a broad range of theoretical, applied, and interdisciplinary backgrounds, and provides rigorous preparation for a future career in statistics, probability, or data science. Our top-ranked program usually takes 5 years to complete. PhD theses are diverse and varied, reflecting the scope of faculty research ...
PhD Program
PhD Program. A unique aspect of our Ph.D. program is our integrated and balanced training, covering research, teaching, and career development. The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference ...
Ph.D. Programs
The Ph.D. programs of the Department of Statistics at Carnegie Mellon University enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods.
Ph.D. Program
Statistical Science at Duke is the world's leading graduate research and educational environment for Bayesian statistics, emphasizing the major themes of 21st century statistical science: foundational concepts of statistics, theory and methods of complex stochastic modeling, interdisciplinary applications of statistics, computational statistics, big data analytics, and machine learning.
Statistics and Data Science, PhD < University of Pennsylvania
Statistics and Data Science, PhD. Wharton's PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across ...
Interdisciplinary Doctoral Program in Statistics < MIT
The Interdisciplinary Doctoral Program in Statistics is an opportunity for students in a multitude of disciplines to specialize at the doctoral level in a statistics-grounded view of their field. Participating programs include Aeronautics and Astronautics, Brain and Cognitive Sciences, Economics, Mathematics, Mechanical Engineering, Physics, Political Science, and the IDSS Social and ...
PhD Programme
The School of Computer Science and Statistics collaborates with leading industry players in the disciplines, for example IBM, Intel and Microsoft, and fosters innovation through its many successful start-up companies, such as Havok and the first Irish company to be floated on the NASDAQ, Iona Technologies. Students have a wide and varied choice ...
Statistics/Machine Learning Joint Ph.D. Degree
This program differs from the standard Statistics Ph.D. program in its emphasis on machine learning and computer science. Students in this track will be involved in courses and research from both the Departments of Statistics and Machine Learning. During the first year, students will normally be ...
Doctor of Philosophy
The Doctor of Philosophy program in statistics prepares students for careers in university teaching and research or in government or industrial research. Students entering the program spend four semesters acquiring a basic background in probability, mathematical statistics, and applied statistics and take general examinations on these subjects.
Ph.D. Specialization in Data Science
The Ph.D. specialization in data science is an option within the Applied Mathematics, Computer Science, Electrical Engineering, Industrial Engineering and Operations Research, and Statistics departments. Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization.
PhD Program information
The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to ...
PhD in Statistics and Applied Probability
PhD in Statistics and Applied Probability ... computer science, and biomedical science. Recent dissertations have been written in the areas of smoothing splines, spatial statistics, micro-array analysis, functional data models, empirical processes, mathematical and statistical finance, Bayesian inference, and bootstrap estimation methods. ...
PhD in Computer Science
The PhD in Computer Science program provides students with the advanced coursework and groundbreaking research opportunities they need to contribute at the forefront of the world's fastest-growing fields. Forging knowledge in 15 core areas like artificial intelligence, data science, programming languages, and human-centered computing, you ...
PhD in Data Science and Analytics
We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection ofcomputer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic ...
Where To Earn A Ph.D. In Data Science Online In 2024
Based in San Diego, California, National University (NU) offers a variety of online programs, including a Ph.D. in data science. NU's program requires 60 credits and takes an estimated 40 months ...
Graduate Studies
Loading Graduate Studies. JavaScript must be enabled for this to work. University of Waterloo. University of Waterloo. 43.471468-80.544205. Campus map 200 University Avenue West. Waterloo, ON, Canada N2L 3G1 +1 519 888 4567. Contact Waterloo Accessibility ...
PhD Students in Statistics and Computer Science
PhD > PhD in Statistics and Computer Science - from cohort 2020-21 > PhD Students; Admissions; Courses and Requirements ... Info for Current Students; Summer School in Advanced Statistics and Probability; PhD Students in Statistics and Computer Science. Current Students . Last modified 09/10/2020 - 11:40:59. Highlighted Sections. Give now ...
Master of Science in Mathematics and Applied Statistics
At California State University Long Beach The Department of Mathematics & Statistics at CSULB offers four Master of Science programs. Teaching & Graduate Assistantships provide students with funding and with college teaching experience. Graduates have found employment in both technical and academic workplaces. Many have obtained tenure-track community college professorships. Others have gone ...
Doctor of Philosophy (Ph.D.)
Baylor's Ph.D. in statistical science is an ideal degree for those seeking challenging research opportunities in industrial, corporate, or academic settings. The degree requirements include a balance of statistical theory, practice experience, and increasingly important communication skills. Students must complete 75 semester hours, including a statistics core of 27 hours, a consulting ...
UNITE Fall 2024 Course Offerings
NOTE: UNITE WILL NOT TAKE REQUESTS FOR ADDITIONAL COURSES FOR FALL 2024 AFTER AUGUST 1ST, 2024.COMPUTER SCIENCE AND ENGINEERINGCSCI 5106 - Programming Languages (3.0) UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate)Instructor TBAUNITE streams live video of on-campus section on TTh 1:00 p.m.-2:15 p.m ...