phd in computer science columbia

MASTER OF SCIENCE PROGRAM

The Master of Science (MS) program is intended for people who wish to broaden and deepen their understanding of Computer Science. Columbia University and the New York City environment provide excellent career opportunities in multiple industries.

The program provides a unique opportunity to develop leading-edge in-depth knowledge of specific computer science disciplines. The department currently offers concentration tracks covering eight such disciplines. MS students are encouraged to participate in state-of-the-art research with our research groups and labs.

REQUIREMENTS

• Complete a total of 30 points (Courses must be at the 4000 level or above)
• Maintain at least a 2.7 overall GPA. (No more than 1 D is permitted). The full Academic Standing Policy can be found here .
• Complete the Columbia Engineering Professional Development & Leadership (PDL) requirement (Not applicable to CVN students)
• Satisfy breadth requirements
• Take at least 6 points of technical courses at the 6000 level
• At most up to 3 points of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Submit the Non-CS/NonTrack form and the course syllabus to your CS Faculty Advisor for review

TRACK OPTIONS

Choose one of the tracks below, view each track webpage for details on requirements.

Columbia Video Network (CVN) students should also choose from one of the above tracks. For faculty advisement, please contact the assigned track advisors .

Cs ms faculty track advisors.

CS Faculty Advisors will be assigned after you select a track in Mice. If you do not yet have a Mice account but are a CS MS student, please contact [email protected] . Contact your Track Advisor to get special permission for any course not specifically approved on your CS track websites .

DEGREE PROGRESS CHECKLIST

Students should keep an updated copy of their Degree Progress Checklist on hand for any academic progress reviews with their Faculty and/or Admin advisor. This form will also be requested a few weeks before graduation to verify your program requirements are met.

If you are following the old MS track requirements, please refer to the old requirements page

Topics courses.

If you are interested in applying a specialized Topics in Computer Science courses (COMS 4995 or COMS 6998) to your Track electives, please view Topics Courses by Track Approval . 

Students may take multiple sections of COMS 4995 and/or COMS 6998, as each topic title will vary by content each semester. If you aren’t sure if a course is the same, please email your MS Faculty Track Advisor.

No approval is required for the course to count as a General Elective.

A list of current and recent Topics Course Descriptions can be found here .

MS IN COMPUTER ENGINEERING

In addition to the Computer Science MS Program, we offer the Computer Engineering MS Program jointly with the Electrical Engineering Department. More information about the program can be found in the Computer Engineering section of the SEAS bulletin and on the Computer Engineering website .

DUAL MS IN JOURNALISM AND COMPUTER SCIENCE

Admitted students will enroll for a total of four semesters. In addition to taking classes already offered at the Journalism and Engineering schools, students will attend a seminar and workshop designed specifically for the dual degree program. The seminar will teach students about the impact of digital techniques on journalism; the emerging role of citizens in the news process; the influence of social media; and the changing business models that will support news gathering. In the workshop, students will use a hands-on approach to delve deeply into information design, focusing on how to build a site, section, or application from concept to development, ensuring the editorial goals are kept uppermost in mind. For more information, please visit the program website .

IMPORTANT AND USEFUL LINKS

• MS TRACK ADVISORS
• MS PROGRAM FAQ
• FIELDWORK/CPT FAQ
• COLUMBIA ENGINEERING RESEARCH OPPORTUNITIES
• COLUMBIA ENGINEERING PROFESSIONAL DEVELOPMENT & LEADERSHIP (PDL) PROGRAM
• COMPUTER SCIENCE ACADEMIC HONESTY POLICY

ADMISSIONS INFORMATION

• SEAS Admissions contact: [email protected]
• CS Admissions contact: [email protected]

Updated 03/25/2024

Find open faculty positions here .

Computer Science at Columbia University

Upcoming events, last day of classes.

Monday 10:00 am

Class Day Graduate Ceremony

Sunday 3:00 pm

South Lawn, Morningside Campus

Class Day Undergraduate Ceremony

Monday 11:45 am

CS Awards Ceremony and Celebration

Monday 1:00 pm

CSB 451 CS Auditorium

In the News

Press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Theory of Computation at Columbia

The Theory of Computation group is a part of the Department of Computer Science in the Columbia School of Engineering and Applied Sciences .

We research the fundamental capabilities and limitations of efficient computation. In addition, we use computation as a lens to gain deeper insights into problems from the natural, social, and engineering sciences. Our active research areas include algorithmic game theory, complexity theory, cryptography, the design and analysis of algorithms, interactive computation and communication, theoretical neuroscience, property testing, the role of randomness in computation, sublinear and streaming algorithms, and the theoretical foundations of machine learning.

Our group is highly collaborative, both within Columbia and among peer institutions. We have a weekly Theory Lunch and Student Seminar . We also have an Undergraduate Theory Learning Seminar that organizes student-run reading groups for undergraduates. Most graduate students have (at least) two advisors and collaborate with several professors and other students. Some of our faculty are cross-listed with the IEOR department and the Data Science Institute . We interact with the New York theory community at large through NYCAC , NYC Theory Day , NYC Crypto Day , and the Simons Collaboration on Algorithms and Geometry .

We regularly communicate on two listservs, which you can join on the attached links: theory-read (for seminar talks that include faculty) and theory-phd (for student-centric things, including our student seminar).

Our department and research group are growing, and we're always looking for new members and collaborators. If you would like to join our group as a graduate student, please apply to the PhD program in Computer Science at Columbia. Please reach out to faculty directly for inquiries about postdoc positions.

Recurring Events

• Theory Lunch
• Student Learning Seminar
• Undergraduate Theory Learning Seminar
• NYC Crypto Day and NYC Theory Day

Our Friends

• Columbia's Data Science Institute
• IEOR and Statistics at Columbia
• IAS School of Mathematics
• Rutgers DIMACS
• Simons Algorithms and Geometry Collaboration
• Columbia's Year on Statistical ML

Recent Courses

• COMS 4281: Introduction to Quantum Computing (F23)
• COMS 4261: Introduction to Cryptography (F23)
• COMS 4252: Computational Learning Theory (F23)
• COMS 4236: Introduction to Computational Complexity (F23)
• COMS 6998: Foundations of Data Privacy (F23)
• COMS 6998: Introduction to Property Testing (F23)
• COMS 6998: Algebraic Techniques in TCS (F23)
• COMS 6998: Algorithms for Massive Data (F23)
• COMS 6998: Reading the CS Classics (S23)
• COMS 4261: Introduction to Cryptography (S23)
• COMS 4232: Advanced Algorithms (S23)
• COMS 4236: Introduction to Computational Complexity (S23)
• COMS 4252: Computational Learning Theory (F22)
• COMS 4236: Introduction to Computational Complexity (F22)
• COMS 4995: Logic and Computability (F22)
• COMS 6998: Fair and Robust Algorithms (F22)
• COMS 6998: Fine Grained Complexity (F22)
• COMS 6998: Communication Complexity & Apps. (S22)
• COMS 6998: Frontiers of Quantum Complexity (S22)
• COMS 6998: Quantum Computing: Theory & Practice (S22)
• COMS 4261: Introduction to Cryptography (S22)
• COMS 4236: Introduction to Computational Complexity (S22)
• COMS 4232: Advanced Algorithms (S22)
• COMS 4252: Computational Learning Theory (F21)
• COMS 4995: Foundations of Blockchains (F21)
• COMS 4995: Logic and Computability (F21)
• COMS 6995: Algebraic Techniques in TCS (F21)
• COMS 6995: Computation and the Brain (F21)
• COMS 4252: Computational Learning Theory (S21)
• COMS 4281: Introduction to Quantum Computing (S21)
• COMS 4995: Advanced Algorithms (S21)
• COMS 4995: Information Theory in TCS (F20)
• COMS 4995: Foundations of Blockchains (F20)
• COMS 6995: Computation and the Brain (F20)
• COMS 4232: Advanced Algorithms (S20)
• COMS 4995: Incentives in Computer Science (S20)
• COMS 6261: Information-Theoretic Cryptography (S20)

Recent Alumni (PhDs and Postdocs)

• Hamoon Mousavi
• Chengyu Lin
• Emmanouil Vlatakis
• Kiran Vodrahalli
• Sandip Sinha
• Peilin Zhong
• Kira Goldner
• Chin Ho Lee
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• Sepideh Mahabadi
• Ilya Razenshteyn
• Alexander Golovnev
• Stuart Hadfield
• Clément Canonne
• Xiaorui Sun
• Dimitris Paparas
• Fernando Krell
• Igor Carboni Oliveira
• Li-Yang Tan
• Iasonas Petras
• Dana Dachman-Soled
• Mariana Raykova
• Seung Geol Choi
• Yevgeniy Vahlis
• Spyridon Antonakopoulos
• Ilias Diakonikolas
• Ragesh Jaiswal
• Emanuele Viola
• Homin K. Lee
• Hoeteck Wee

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Computer Science

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Departmental Office: 450 Computer Science Building; 212-939-7000 http://www.cs.columbia.edu/

Director of Undergraduate Studies: Dr. Jae Woo Lee, 715 CEPSR; 212-939-7066; [email protected]

The majors in the Department of Computer Science provide students with the appropriate computer science background necessary for graduate study or a professional career. Computers impact nearly all areas of human endeavor. Therefore, the department also offers courses for students who do not plan a computer science major or concentration. The computer science majors offer maximum flexibility by providing students with a range of options for program specialization. The department offers four majors: computer science; information science; data science, offered jointly with the Statistics Department; and computer science-mathematics, offered jointly with the Mathematics Department.

Computer Science Major

Students study a common core of fundamental topics, supplemented by a program of six electives that provides a high degree of flexibility. Three of the electives are chosen from a list of upper-level courses that represent area foundations within computer science. The remaining electives are selected from the complete list of upper-level computer science courses. Students are encouraged to work with their faculty advisor to create a plan tailored to fit their goals and interests. The department webpage provides several example programs for students interested in a variety of specific areas in computer science. 

Information Science Major

Information science is an interdisciplinary major designed to provide a student with an understanding of how information is organized, accessed, stored, distributed, and processed in strategic segments of today’s society. Recent years have seen an explosive growth of online information, with people of all ages and all walks of life making use of the World Wide Web and other information in digital form.

This major puts students at the forefront of the information revolution, studying how online access touches on all disciplines and changing the very way people communicate. Organizations have large stores of in-house information that are crucial to their daily operation. Today’s systems must enable quick access to relevant information, must ensure that confidential information is secure, and must enable new forms of communication among people and their access to information.

The information science major can choose a scientific focus on algorithms and systems for organizing, accessing, and processing information, or an interdisciplinary focus in order to develop an understanding of, and tools for, information modeling and use within an important sector of modern society such as economics or health.

Advanced Placement

The department grants 3 points for a score of 4 or 5 on the AP Computer Science A exam, along with an exemption from COMS W1004 Introduction to Computer Science and Programming in Java . However, we recommend that you take COMS W1004 before taking COMS W3134/W3137 Data Structures.

Pre-Introductory Courses

COMS W1004 is the first course in the Computer Science major curriculum, and it does not require any previous computing experience.  Before taking COMS W1004, however, students have an option to start with one of the pre-introductory courses: ENGI E1006 or COMS W1002.

ENGI E1006 Introduction to Computing for Engineers and Applied Scientists is a general introduction to computing for STEM students.  ENGI E1006 is in fact a required course for all engineering students.  COMS W1002 Computing in Context is a course primarily intended for humanities majors, but it also serves as a pre-introductory course for CS majors.  ENGI E1006 and COMS W1002 do not count towards Computer Science major.

Laboratory Facilities

The department has well-equipped lab areas for research in computer graphics, computer-aided digital design, computer vision, databases and digital libraries, data mining and knowledge discovery, distributed systems, mobile and wearable computing, natural language processing, networking, operating systems, programming systems, robotics, user interfaces, and real-time multimedia.

Research labs contain several large Linux and Solaris clusters; Puma 500 and IBM robotic arms; a UTAH-MIT dexterous hand; an Adept-1 robot; three mobile research robots; a real-time defocus range sensor; interactive 3-D graphics workstations with 3-D position and orientation trackers; prototype wearable computers, wall-sized stereo projection systems; see-through head-mounted displays; a networking testbed with three Cisco 7500 backbone routers, traffic generators; an IDS testbed with secured LAN, Cisco routers, EMC storage, and Linux servers; and a simulation testbed with several Sun servers and Cisco Catalyst routers.  The department uses a SIP IP phone system. The protocol was developed in the department.

The department's computers are connected via a switched 1Gb/s Ethernet network, which has direct connectivity to the campus OC-3 Internet and internet 2 gateways. The campus has 802.11b/g wireless LAN coverage.

The research facility is supported by a full-time staff of professional system administrators and programmers.

Peter N. Belhumeur Steven M. Bellovin Luca Carloni Xi Chen Steven K. Feiner Luis Gravano Julia B. Hirschberg Gail E. Kaiser John R. Kender Tal Malkin Kathleen R. McKeown Vishal Misra Shree Kumar Nayar Jason Nieh Christos Papadimitriou Itsik Pe'er Toniann Pitassi Kenneth A. Ross Tim Roughgarden Daniel S. Rubenstein Henning G. Schulzrinne Rocco A. Servedio Simha Sethumadhavan Salvatore J. Stolfo Bjarne Stroustrup Vladimir Vapnik Jeannette Wing Junfeng Yang Mihalis Yannakakis Richard Zemei

Associate Professors

Alexandr Andoni Elias Bareinboim Augustin Chaintreau Stephen A. Edwards Roxana Geambasu Daniel Hsu Suman Jana Martha Allen Kim Baishakhi Ray Carl Vondrick Eugene Wu Zhou Yu Changxi Zheng Xia Zhou

Assistant Professors

Josh Alman Lydia Chilton Ronghui Gu Kostis Kaffes David Knowles Brian Smith Henry Yuen

Senior Lecturer in Discipline

• Adam Cannon
• Jae Woo Lee

Lecturer in Discipline

Daniel Bauer Brian Borowski Tony Dear

Associated Faculty Joint

Andrew Blumberg Shih-Fu Chang Feniosky Peña-Mora Clifford Stein

Shipra Agrawal Mohammed AlQuraishi Elham Azizi Paolo Blikstein Asaf Cidon Matei Ciocarlie Rachel Cummings Noemie Elhadad Javad Ghaderi Gamze Gursoy Xiaofan Jiang Ethan Katz-Bassett Hod Lipson Smaranda Muresan Liam Paninski Brian Plancher Mark Santolucito Lisa Soros Barbara Tversky Venkat Venkatasubramanian Rebecca Wright Gil Zussman

Senior Research Scientists

Gaston Ormazabal Moti Yung

Alfred V. Aho Peter K. Allen Edward G. Coffman Jr. Zvi Galil Jonathan L. Gross Steven M. Nowick Stephen H. Unger Henryk Wozniakowski Yechiam Yemini

Guidelines for all Computer Science Majors and Minors

Students may receive credit for only one of the following two courses:

• COMS W1004 Introduction to Computer Science and Programming in Java
• COMS W1005 Introduction to Computer Science and Programming in MATLAB .

Students may receive credit for only one of the following three courses:

• COMS W3134 Data Structures in Java
• COMS W3136 ESSENTIAL DATA STRUCTURES
• COMS W3137 HONORS DATA STRUCTURES ＆ ALGOL

However, COMS W1005 and COMS W3136 cannot be counted towards the Computer Science major, minor, and concentration. 

Transfer and Double Counting

Up to four transfer courses are accepted toward the major. Up to two transfer courses are accepted toward the minor or concentration. Calculus, linear algebra, and probability/statistics courses can be transferred in addition to the four/two-course limits.

Double-counting policies are to be construed within the larger double-counting policy of the student's home school. Double-counting policies are detailed on each School's Bulletin and/or Catalogue.

The CS department allows the following courses in the CS Core and Mathematics requirement to be double-counted with another major, minor, or concentration. No other courses can be double-counted with another program.

• Any calculus courses (including Honors Math A and B)
• One Linear Algebra course
• One Probability/Statistics course

Barnard does not allow a grade of D to count towards any major. Consult with your advisor.

Guidelines for all Computer Science Majors and Concentrators

The following requirements are new as of the academic year 2023-2024. Students who declared a CS major in the academic year 2022-2023 or earlier have the option to follow the old requirements.

Students who declared a CS major in the academic year 2022-2023 or earlier have the option to follow the requirements listed below or to follow the old requirements. The old requirements are noted on the Undergraduate Programs pages of the Computer Science Department website ( https://www.cs. columbia.edu /education/undergraduate/ ). Please note that the information on the department website is more accurate than the information in the archived Bulletins. Students with questions about which requirements to follow are advised to talk with the Director of Undergraduate Studies.

A maximum of one course worth no more than 4 points passed with a grade of D may be counted toward the major or concentration.

Major in Computer Science

Please read  Guidelines for all Computer Science Majors and Concentrators  above.

Please read Guidelines for all Computer Science Majors and Minors above.

All majors should confer with their program adviser each term to plan their programs of study. Students considering a major in computer science are encouraged to talk to a program adviser during their first or second year. The Computer Science major is composed of four basic components: The Mathematics Requirement, the Computer Science Core, the Area Foundation Courses, and the Computer Science Electives.

Program of Study

Adjustments were made to the course lists below in march 2023..

Students who declared before Spring 2024 should see the Department of Computer Science website for the old requirements. 

For students who declare in Spring 2024 and beyond:

Mathematics Requirement (6-11 points)

Computer science core (20-21 points):, area foundation courses (9 to 12 points):.

Select three from the following list:

Computer Science Electives (9 to 12 points)

Any three COMS courses or jointly offered computer science courses such as CSXX or XXCS course that are worth at least 3 points and are at the 3000 level or above. This includes 3000-level courses offered by Barnard CS.

Restrictions

Note: No more than 6 points of project/thesis courses (COMS W3902, W3998, W4901) can count toward the major. COMS W3999 Fieldwork cannot be used as a CS Elective.

No more than one course from each set below may be applied towards the computer science major:

 IEOR E3658, STAT UN1201, MATH UN2015

 MATH UN2015, MATH UN2010, APAM E3101, COMS W3251

 COMS W4771, COMS W4721

Major in Computer Science—Mathematics

For a description of the joint major in computer science—mathematics, see the Mathematics section in this bulletin.

For a description of the joint major in mathematics—computer science, see the  Mathematics   section in this catalog.

Major in Information Science

The major in information science requires a minimum of 33 points, including a core requirement of five courses. Adjustments were made to the course lists below in March 2022.

The elective courses must be chosen with a faculty adviser to focus on the modeling and use of information within the context of a disciplinary theme. After discussing potential selections, students prepare a proposal of study that must be approved by the faculty adviser. In all cases, the six courses must be at the 3000 level or above, with at least three courses chosen from computer science. Following are some example programs. For more examples or templates for the program proposal, see a faculty adviser.

Note: In most cases, additional courses will be necessary as prerequisites in order to take some of the elective courses. This will depend on the student's proposed program of study.

Core Requirement

Following are some suggested programs of instruction:

Information Science and Contemporary Society

Students may focus on how humans use technology and how technology has changed society.

The requirements include:

Information Science and the Economy

Students may focus on understanding information modeling together with existing and emerging needs in economics and finance as well as algorithms and systems to address those needs.

Information Science and Health Sciences

Students may focus on understanding information modeling together with existing and emerging needs in health sciences, as well as algorithms and systems to address those needs.

Major in Data Science

In response to the ever-growing importance of "big data" in scientific and policy endeavors, the last few years have seen explosive growth in theory, methods, and applications at the interface between computer science and statistics. The statistics and computer science departments have responded with a joint major that emphasizes the interface between the disciplines.

Minor in Computer Science

Please read  Guidelines for all Computer Science Majors and Minors  above.

For students who declare in Spring 2014 and beyond:

The minor in computer science requires a minimum of 22-24 points, as follows:

Concentration in Computer Science

Please read  Guidelines for all Computer Science Majors and Concentrators  above. Adjustments were made to the course lists below in March 2022.

The concentration in computer science requires a minimum of 22-24 points, as follows:

COMS W1001 Introduction to Information Science. 3 points .

Basic introduction to concepts and skills in Information Sciences: human-computer interfaces, representing information digitally, organizing and searching information on the internet, principles of algorithmic problem solving, introduction to database concepts, and introduction to programming in Python.

COMS W1002 COMPUTING IN CONTEXT. 4.00 points .

CC/GS: Partial Fulfillment of Science Requirement

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002

COMS W1003 INTRO-COMPUT SCI/PROGRAM IN C. 3.00 points .

COMS W1004 Introduction to Computer Science and Programming in Java. 3 points .

A general introduction to computer science for science and engineering students interested in majoring in computer science or engineering. Covers fundamental concepts of computer science, algorithmic problem-solving capabilities, and introductory Java programming skills. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: 1004  or  1005 .

COMS W1005 Introduction to Computer Science and Programming in MATLAB. 3 points .

A general introduction to computer science concepts, algorithmic problem-solving capabilities, and programming skills in MATLAB. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: W1004  or  W1005 .

COMS W1011 INTERMED COMPUTER PROGRAMMING. 3.00 points .

COMS W1012 COMPUTING IN CONTEXT REC. 0.00 points .

COMS W1103 HONORS INTRO COMPUTER SCIENCE. 3.00 points .

COMS W1404 EMERGING SCHOLARS PROG SEMINAR. 1.00 point .

Pass/Fail only.

Prerequisites: the instructor's permission. Corequisites: COMS W1002 or COMS W1004 or COMS W1007 Corequisites: COMS W1004 ,COMS W1007, COMS W1002 Peer-led weekly seminar intended for first and second year undergraduates considering a major in Computer Science. Pass/fail only. May not be used towards satisfying the major or SEAS credit requirements

COMS W3011 INTERMED COMPUTER PROGRAMMING. 3.00 points .

COMS W3101 PROGRAMMING LANGUAGES. 1.00 point .

Prerequisites: Fluency in at least one programming language. Introduction to a programming language. Each section is devoted to a specific language. Intended only for those who are already fluent in at least one programming language. Sections may meet for one hour per week for the whole term, for three hours per week for the first third of the term, or for two hours per week for the first six weeks. May be repeated for credit if different languages are involved

COMS W3102 DEVELOPMENT TECHNOLOGY. 1.00-2.00 points .

Lect: 2. Lab: 0-2.

Prerequisites: Fluency in at least one programming language. Introduction to software development tools and environments. Each section devoted to a specific tool or environment. One-point sections meet for two hours each week for half a semester, and two point sections include an additional two-hour lab

COMS W3107 Clean Object-Oriented Design. 3.00 points .

Prerequisites: Intro to Computer Science/Programming in Java (COMS W1004) or instructor’s permission. May not take for credit if already received credit for COMS W1007.

Prerequisites: see notes re: points A course in designing, documenting, coding, and testing robust computer software, according to object-oriented design patterns and clean coding practices. Taught in Java.Object-oriented design principles include: use cases; CRC; UML; javadoc; patterns (adapter, builder, command, composite, decorator, facade, factory, iterator, lazy evaluation, observer, singleton, strategy, template, visitor); design by contract; loop invariants; interfaces and inheritance hierarchies; anonymous classes and null objects; graphical widgets; events and listeners; Java's Object class; generic types; reflection; timers, threads, and locks

COMS W3123 ASSEMBLY LANG AND COMPUT LOGIC. 3.00 points .

COMS W3132 Intermediate Computing in Python. 4.00 points .

Essential data structures and algorithms in Python with practical software development skills, applications in a variety of areas including biology, natural language processing, data science and others

COMS W3134 Data Structures in Java. 3 points .

Prerequisites: ( COMS W1004 ) or knowledge of Java.

Data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Rudiments of the analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134 , COMS W3136 , COMS W3137 .

COMS W3136 ESSENTIAL DATA STRUCTURES. 4.00 points .

Prerequisites: ( COMS W1004 ) or ( COMS W1005 ) or (COMS W1007) or ( ENGI E1006 ) A second programming course intended for nonmajors with at least one semester of introductory programming experience. Basic elements of programming in C and C , arraybased data structures, heaps, linked lists, C programming in UNIX environment, object-oriented programming in C , trees, graphs, generic programming, hash tables. Due to significant overlap, students may only receive credit for either COMS W3134 , W3136 , or W3137

COMS W3137 HONORS DATA STRUCTURES ＆ ALGOL. 4.00 points .

Prerequisites: ( COMS W1004 ) or (COMS W1007) Corequisites: COMS W3203 An honors introduction to data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Design and analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134 , W3136 , or W3137

COMS W3157 ADVANCED PROGRAMMING. 4.00 points .

Prerequisites: ( COMS W3134 ) or ( COMS W3137 ) C programming language and Unix systems programming. Also covers Git, Make, TCP/IP networking basics, C fundamentals

COMS W3202 FINITE MATHEMATICS. 3.00 points .

COMS W3203 DISCRETE MATHEMATICS. 4.00 points .

Prerequisites: Any introductory course in computer programming. Logic and formal proofs, sequences and summation, mathematical induction, binomial coefficients, elements of finite probability, recurrence relations, equivalence relations and partial orderings, and topics in graph theory (including isomorphism, traversability, planarity, and colorings)

COMS W3210 Scientific Computation. 3 points .

Prerequisites: two terms of calculus.

Introduction to computation on digital computers. Design and analysis of numerical algorithms. Numerical solution of equations, integration, recurrences, chaos, differential equations. Introduction to Monte Carlo methods. Properties of floating point arithmetic. Applications to weather prediction, computational finance, computational science, and computational engineering.

COMS W3251 COMPUTATIONAL LINEAR ALGEBRA. 4.00 points .

COMS W3261 COMPUTER SCIENCE THEORY. 3.00 points .

Prerequisites: ( COMS W3203 ) Corequisites: COMS W3134 , COMS W3136 , COMS W3137 Regular languages: deterministic and non-deterministic finite automata, regular expressions. Context-free languages: context-free grammars, push-down automata. Turing machines, the Chomsky hierarchy, and the Church-Turing thesis. Introduction to Complexity Theory and NP-Completeness

COMS W3410 COMPUTERS AND SOCIETY. 3.00 points .

Broader impact of computers. Social networks and privacy. Employment, intellectual property, and the media. Science and engineering ethics. Suitable for nonmajors

COMS E3899 Research Training. 0.00 points .

Research training course. Recommended in preparation for laboratory related research

COMS W3902 UNDERGRADUATE THESIS. 0.00-6.00 points .

Prerequisites: Agreement by a faculty member to serve as thesis adviser. An independent theoretical or experimental investigation by an undergraduate major of an appropriate problem in computer science carried out under the supervision of a faculty member. A formal written report is mandatory and an oral presentation may also be required. May be taken over more than one term, in which case the grade is deferred until all 6 points have been completed. Consult the department for section assignment

COMS W3995 Special Topics in Computer Science. 3 points .

Prerequisites: the instructor's permission.

Consult the department for section assignment. Special topics arranged as the need and availability arise. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit.

COMS W3998 UNDERGRAD PROJECTS IN COMPUTER SCIENCE. 1.00-3.00 points .

Prerequisites: Approval by a faculty member who agrees to supervise the work. Independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit. Consult the department for section assignment

COMS W3999 FIELDWORK. 1.00 point .

May be repeated for credit, but no more than 3 total points may be used toward the 128-credit degree requirement. Final report and letter of evaluation required. May not be used as a technical or non-technical elective. May not be taken for pass/fail credit or audited

COMS E3999 Fieldwork. 1 point .

Prerequisites: Obtained internship and approval from faculty advisor.

May be repeated for credit, but no more than 3 total points may be used toward the 128-credit degree requirement. Only for SEAS computer science undergraduate students who include relevant off-campus work experience as part of their approved program of study. Final report and letter of evaluation required. May not be used as a technical or non-technical elective. May not be taken for pass/fail credit or audited.

COMS W4111 INTRODUCTION TO DATABASES. 3.00 points .

CC/GS: Partial Fulfillment of Science Requirement Prerequisites: COMS W3134, COMS W3136, or COMS W3137; or the instructor's permission.

Prerequisites: ( COMS W3134 ) or ( COMS W3136 ) or ( COMS W3137 ) or The fundamentals of database design and application development using databases: entity-relationship modeling, logical design of relational databases, relational data definition and manipulation languages, SQL, XML, query processing, physical database tuning, transaction processing, security. Programming projects are required

COMS W4112 DATABASE SYSTEM IMPLEMENTATION. 3.00 points .

Prerequisites: ( COMS W4111 ) and fluency in Java or C++. CSEE W3827 is recommended. The principles and practice of building large-scale database management systems. Storage methods and indexing, query processing and optimization, materialized views, transaction processing and recovery, object-relational databases, parallel and distributed databases, performance considerations. Programming projects are required

COMS W4113 FUND-LARGE-SCALE DIST SYSTEMS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( COMS W3157 or COMS W4118 or CSEE W4119 ) Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( COMS W3157 or COMS W4118 or CSEE W4119 ) Design and implementation of large-scale distributed and cloud systems. Teaches abstractions, design and implementation techniques that enable the building of fast, scalable, fault-tolerant distributed systems. Topics include distributed communication models (e.g. sockets, remote procedure calls, distributed shared memory), distributed synchronization (clock synchronization, logical clocks, distributed mutex), distributed file systems, replication, consistency models, fault tolerance, distributed transactions, agreement and commitment, Paxos-based consensus, MapReduce infrastructures, scalable distributed databases. Combines concepts and algorithms with descriptions of real-world implementations at Google, Facebook, Yahoo, Microsoft, LinkedIn, etc

COMS E4115 PROGRAMMING LANG ＆ TRANSL. 3.00 points .

COMS W4115 PROGRAMMING LANG ＆ TRANSLATORS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( COMS W3261 ) and ( CSEE W3827 ) or equivalent, or the instructor's permission. Modern programming languages and compiler design. Imperative, object-oriented, declarative, functional, and scripting languages. Language syntax, control structures, data types, procedures and parameters, binding, scope, run-time organization, and exception handling. Implementation of language translation tools including compilers and interpreters. Lexical, syntactic and semantic analysis; code generation; introduction to code optimization. Teams implement a language and its compiler

COMS W4118 OPERATING SYSTEMS I. 3.00 points .

Prerequisites: ( CSEE W3827 ) and knowledge of C and programming tools as covered in COMS W3136 , W3157 , or W3101, or the instructor's permission. Design and implementation of operating systems. Topics include process management, process synchronization and interprocess communication, memory management, virtual memory, interrupt handling, processor scheduling, device management, I/O, and file systems. Case study of the UNIX operating system. A programming project is required

COMS W4119 COMPUTER NETWORKS. 3.00 points .

Introduction to computer networks and the technical foundations of the internet, including applications, protocols, local area networks, algorithms for routing and congestion control, security, elementary performance evaluation. Several written and programming assignments required

COMS W4121 COMPUTER SYSTEMS FOR DATA SCIENCE. 3.00 points .

Prerequisites: background in Computer System Organization and good working knowledge of C/C++ Corequisites: CSOR W4246 , STAT GU4203 An introduction to computer architecture and distributed systems with an emphasis on warehouse scale computing systems. Topics will include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism, scheduling, caching, prefetching, network and memory architecture, latency and throughput optimizations, specialization, and an introduction to programming data center computers

COMS W4137 From Algorithmic Thinking to Development. 3.00 points .

Algorithmic problem-solving and coding skills needed to devise solutions to interview questions for software engineering positions. Solutions are implemented in Python, Java, C, and C . Approaches include brute-force, hashing, sorting, transform-and-conquer, greedy, and dynamic programming. Focus on experimentation and team work

COMS W4152 Engineering Software-as-a-Service. 3.00 points .

Modern software engineering concepts and practices including topics such as Software-as-a-Service, Service-oriented Architecture, Agile Development, Behavior-driven Development, Ruby on Rails, and Dev/ops

COMS W4153 Cloud Computing. 3.00 points .

Software engineering skills necessary for developing cloud computing and software-as-a-service applications, covering topics such as service-oriented architectures, message-driven applications, and platform integration. Includes theoretical study, practical application, and collaborative project work

COMS W4156 ADVANCED SOFTWARE ENGINEERING. 3.00 points .

Prerequisites: ( COMS W3157 ) or equivalent. Software lifecycle using frameworks, libraries and services. Major emphasis on software testing. Centers on a team project

COMS W4160 COMPUTER GRAPHICS. 3.00 points .

Prerequisites: ( COMS W3134 ) or ( COMS W3136 ) or ( COMS W3137 ) COMS W4156 is recommended. Strong programming background and some mathematical familiarity including linear algebra is required. Introduction to computer graphics. Topics include 3D viewing and projections, geometric modeling using spline curves, graphics systems such as OpenGL, lighting and shading, and global illumination. Significant implementation is required: the final project involves writing an interactive 3D video game in OpenGL

COMS W4162 Advanced Computer Graphics. 3 points .

Prerequisites: ( COMS W4160 ) or equivalent, or the instructor's permission.

A second course in computer graphics covering more advanced topics including image and signal processing, geometric modeling with meshes, advanced image synthesis including ray tracing and global illumination, and other topics as time permits. Emphasis will be placed both on implementation of systems and important mathematical and geometric concepts such as Fourier analysis, mesh algorithms and subdivision, and Monte Carlo sampling for rendering. Note: Course will be taught every two years.

COMS W4165 COMPUT TECHNIQUES-PIXEL PROCSS. 3.00 points .

An intensive introduction to image processing - digital filtering theory, image enhancement, image reconstruction, antialiasing, warping, and the state of the art in special effects. Topics from the basis of high-quality rendering in computer graphics and of low-level processing for computer vision, remote sensing, and medical imaging. Emphasizes computational techniques for implementing useful image-processing functions

COMS W4167 COMPUTER ANIMATION. 3.00 points .

Prerequisites: Multivariable calculus, linear algebra, C++ programming proficiency. COMS W4156 recommended.

Theory and practice of physics-based animation algorithms, including animated clothing, hair, smoke, water, collisions, impact, and kitchen sinks. Topics covered: Integration of ordinary differential equations, formulation of physical models, treatment of discontinuities including collisions/contact, animation control, constrained Lagrangian Mechanics, friction/dissipation, continuum mechanics, finite elements, rigid bodies, thin shells, discretization of Navier-Stokes equations. General education requirement: quantitative and deductive reasoning (QUA). 

COMS W4170 USER INTERFACE DESIGN. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) Introduction to the theory and practice of computer user interface design, emphasizing the software design of graphical user interfaces. Topics include basic interaction devices and techniques, human factors, interaction styles, dialogue design, and software infrastructure. Design and programming projects are required

COMS W4172 3D UI AND AUGMENTED REALITY. 3.00 points .

Prerequisites: ( COMS W4160 ) or ( COMS W4170 ) or the instructor's permission. Design, development, and evaluation of 3D user interfaces. Interaction techniques and metaphors, from desktop to immersive. Selection and manipulation. Travel and navigation. Symbolic, menu, gestural, and multimodal interaction. Dialogue design. 3D software support. 3D interaction devices and displays. Virtual and augmented reality. Tangible user interfaces. Review of relevant 3D math

COMS W4181 SECURITY I. 3.00 points .

Not offered during 2023-2024 academic year.

Prerequisites: COMS W3157 or equivalent. Introduction to security. Threat models. Operating system security features. Vulnerabilities and tools. Firewalls, virtual private networks, viruses. Mobile and app security. Usable security. Note: May not earn credit for both W4181 and W4180 or W4187

COMS W4182 SECURITY II. 3.00 points .

Prerequisites: COMS W4181 , COMS W4118 , COMS W4119 Advanced security. Centralized, distributed, and cloud system security. Cryptographic protocol design choices. Hardware and software security techniques. Security testing and fuzzing. Blockchain. Human security issues. Note: May not earn credit for both W4182 and W4180 or W4187

COMS W4186 MALWARE ANALYSIS＆REVERSE ENGINEERING. 3.00 points .

Prerequisites: COMS W3157 or equivalent. COMS W3827 Hands-on analysis of malware. How hackers package and hide malware and viruses to evade analysis. Disassemblers, debuggers, and other tools for reverse engineering. Deep study of Windows Internals and x86 assembly

COMS W4203 Graph Theory. 3 points .

Prerequisites: ( COMS W3203 )

General introduction to graph theory. Isomorphism testing, algebraic specification, symmetries, spanning trees, traversability, planarity, drawings on higher-order surfaces, colorings, extremal graphs, random graphs, graphical measurement, directed graphs, Burnside-Polya counting, voltage graph theory.

COMS W4205 Combinatorial Theory. 3 points .

Lect: 3. Not offered during 2023-2024 academic year.

Prerequisites: ( COMS W3203 ) and course in calculus.

Sequences and recursions, calculus of finite differences and sums, elementary number theory, permutation group structures, binomial coefficients, Stilling numbers, harmonic numbers, generating functions. 

COMS W4223 Networks, Crowds, and the Web. 3.00 points .

This class introduces fundamental ideas and algorithms on networks of information collected by online services. It covers properties pervasive in large networks, dynamics of individuals that lead to large collective phenomena, mechanisms underlying the web economy, and results and tools informing societal impact of algorithms on privacy, polarization and discrimination

COMS W4231 ANALYSIS OF ALGORITHMS I. 3.00 points .

COMS W4232 Advanced Algorithms. 3.00 points .

Prerequisite: Analysis of Algorithms (COMS W4231).

Prerequisites: see notes re: points Introduces classic and modern algorithmic ideas that are central to many areas of Computer Science. The focus is on most powerful paradigms and techniques of how to design algorithms, and how to measure their efficiency. The intent is to be broad, covering a diversity of algorithmic techniques, rather than be deep. The covered topics have all been implemented and are widely used in industry. Topics include: hashing, sketching/streaming, nearest neighbor search, graph algorithms, spectral graph theory, linear programming, models for large-scale computation, and other related topics

COMS W4236 INTRO-COMPUTATIONAL COMPLEXITY. 3.00 points .

Prerequisites: ( COMS W3261 ) Develops a quantitative theory of the computational difficulty of problems in terms of the resources (e.g. time, space) needed to solve them. Classification of problems into complexity classes, reductions, and completeness. Power and limitations of different modes of computation such as nondeterminism, randomization, interaction, and parallelism

COMS W4241 Numerical Algorithms and Complexity. 3 points .

Prerequisites: Knowledge of a programming language. Some knowledge of scientific computation is desirable.

Modern theory and practice of computation on digital computers. Introduction to concepts of computational complexity. Design and analysis of numerical algorithms. Applications to computational finance, computational science, and computational engineering.

COMS W4242 NUMRCL ALGORTHMS-COMPLEXITY II. 3.00 points .

COMS W4252 INTRO-COMPUTATIONAL LEARN THRY. 3.00 points .

Prerequisites: ( CSOR W4231 ) or ( COMS W4236 ) or COMS W3203 and the instructor's permission, or COMS W3261 and the instructor's permission.

Possibilities and limitations of performing learning by computational agents. Topics include computational models of learning, polynomial time learnability, learning from examples and learning from queries to oracles. Computational and statistical limitations of learning. Applications to Boolean functions, geometric functions, automata.

COMS W4261 INTRO TO CRYPTOGRAPHY. 3.00 points .

Prerequisites: Comfort with basic discrete math and probability. Recommended: COMS W3261 or CSOR W4231 . An introduction to modern cryptography, focusing on the complexity-theoretic foundations of secure computation and communication in adversarial environments; a rigorous approach, based on precise definitions and provably secure protocols. Topics include private and public key encryption schemes, digital signatures, authentication, pseudorandom generators and functions, one-way functions, trapdoor functions, number theory and computational hardness, identification and zero knowledge protocols

COMS W4281 INTRO TO QUANTUM COMPUTING. 3.00 points .

Prerequisites: Knowledge of linear algebra. Prior knowledge of quantum mechanics is not required although helpful.

Introduction to quantum computing. Shor's factoring algorithm, Grover's database search algorithm, the quantum summation algorithm. Relationship between classical and quantum computing. Potential power of quantum computers.

COMS W4419 INTERNET TECHNOLOGY,ECONOMICS,AND POLICY. 3.00 points .

Technology, economic and policy aspects of the Internet. Summarizes how the Internet works technically, including protocols, standards, radio spectrum, global infrastructure and interconnection. Micro-economics with a focus on media and telecommunication economic concerns, including competition and monopolies, platforms, and behavioral economics. US constitution, freedom of speech, administrative procedures act and regulatory process, universal service, role of FCC. Not a substitute for CSEE4119. Suitable for non-majors. May not be used as a track elective for the computer science major.

COMS W4444 PROGRAMMING ＆ PROBLEM SOLVING. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and ( CSEE W3827 ) Hands-on introduction to solving open-ended computational problems. Emphasis on creativity, cooperation, and collaboration. Projects spanning a variety of areas within computer science, typically requiring the development of computer programs. Generalization of solutions to broader problems, and specialization of complex problems to make them manageable. Team-oriented projects, student presentations, and in-class participation required

COMS W4460 PRIN-INNOVATN/ENTREPRENEURSHIP. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission. Team project centered course focused on principles of planning, creating, and growing a technology venture. Topics include: identifying and analyzing opportunities created by technology paradigm shifts, designing innovative products, protecting intellectual property, engineering innovative business models

COMS W4701 ARTIFICIAL INTELLIGENCE. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and any course on probability. Prior knowledge of Python is recommended. Prior knowledge of Python is recommended. Provides a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included as time permits

COMS W4705 NATURAL LANGUAGE PROCESSING. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission. Computational approaches to natural language generation and understanding. Recommended preparation: some previous or concurrent exposure to AI or Machine Learning. Topics include information extraction, summarization, machine translation, dialogue systems, and emotional speech. Particular attention is given to robust techniques that can handle understanding and generation for the large amounts of text on the Web or in other large corpora. Programming exercises in several of these areas

COMS W4706 Spoken Language Processing. 3 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission.

Computational approaches to speech generation and understanding. Topics include speech recognition and understanding, speech analysis for computational linguistics research, and speech synthesis. Speech applications including dialogue systems, data mining, summarization, and translation. Exercises involve data analysis and building a small text-to-speech system.

COMS W4721 MACHINE LEARNING FOR DATA SCI. 3.00 points .

COMS W4725 Knowledge representation and reasoning. 3 points .

Prerequisites: ( COMS W4701 )

General aspects of knowledge representation (KR). The two fundamental paradigms (semantic networks and frames) and illustrative systems. Topics include hybrid systems, time, action/plans, defaults, abduction, and case-based reasoning. Throughout the course particular attention is paid to design trade-offs between language expressiveness and reasoning complexity, and issues relating to the use of KR systems in larger applications. 

COMS W4731 Computer Vision I: First Principles. 3.00 points .

Prerequisites: Fundamentals of calculus, linear algebra, and C programming. Students without any of these prerequisites are advised to contact the instructor prior to taking the course. Introductory course in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2D and 3D object representation, object recognition, vision systems and applications

COMS W4732 Computer Vision II: Learning. 3.00 points .

Advanced course in computer vision. Topics include convolutional networks and back-propagation, object and action recognition, self-supervised and few-shot learning, image synthesis and generative models, object tracking, vision and language, vision and audio, 3D representations, interpretability, and bias, ethics, and media deception

COMS W4733 COMPUTATIONAL ASPECTS OF ROBOTICS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136COMS W3137) Introduction to fundamental problems and algorithms in robotics. Topics include configuration spaces, motion and sensor models, search and sampling-based planning, state estimation, localization and mapping, perception, and learning

COMS W4735 VISUAL INTERFACES TO COMPUTERS. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) Visual input as data and for control of computer systems. Survey and analysis of architecture, algorithms, and underlying assumptions of commercial and research systems that recognize and interpret human gestures, analyze imagery such as fingerprint or iris patterns, generate natural language descriptions of medical or map imagery. Explores foundations in human psychophysics, cognitive science, and artificial intelligence

COMS W4737 Biometrics. 3 points .

Prerequisites: a background at the sophomore level in computer science, engineering, or like discipline.

In this course. we will explore the latest advances in biometrics as well as the machine learning techniques behind them. Students will learn how these technologies work and how they are sometimes defeated. Grading will be based on homework assignments and a final project. There will be no midterm or final exam. This course shares lectures with COMS E6737 . Students taking COMS E6737 are required to complete additional homework problems and undertake a more rigorous final project. Students will only be allowed to earn credit for COMS W4737 or COMS E6737 and not both.

COMS W4762 Machine Learning for Functional Genomics. 3 points .

Prerequisites: Proficiency in a high-level programming language (Python/R/Julia). An introductory machine learning class (such as COMS 4771 Machine Learning) will be helpful but is not required.

Prerequisites: see notes re: points

This course will introduce modern probabilistic machine learning methods using applications in data analysis tasks from functional genomics, where massively-parallel sequencing is used to measure the state of cells: e.g. what genes are being expressed, what regions of DNA (“chromatin”) are active (“open”) or bound by specific proteins.

COMS E4762 Machine Learning for Functional Genomics. 3.00 points .

This course will introduce modern probabilistic machine learning methods using applications in data analysis tasks from functional genomics, where massively-parallel sequencing is used to measure the state of cells: e.g. what genes are being expressed, what regions of DNA (“chromatin”) are active (“open”) or bound by specific proteins

COMS W4771 MACHINE LEARNING. 3.00 points .

Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence. Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in MATLAB

COMS W4772 ADVANCED MACHINE LEARNING. 3.00 points .

Prerequisites: ( COMS W4771 ) or instructor's permission; knowledge of linear algebra & introductory probability or statistics is required. An exploration of advanced machine learning tools for perception and behavior learning. How can machines perceive, learn from, and classify human activity computationally? Topics include appearance-based models, principal and independent components analysis, dimensionality reduction, kernel methods, manifold learning, latent models, regression, classification, Bayesian methods, maximum entropy methods, real-time tracking, extended Kalman filters, time series prediction, hidden Markov models, factorial HMMS, input-output HMMs, Markov random fields, variational methods, dynamic Bayesian networks, and Gaussian/Dirichlet processes. Links to cognitive science

COMS W4773 Machine Learning Theory. 3 points .

Prerequisites: Machine Learning (COMS W4771). Background in probability and statistics, linear algebra, and multivariate calculus. Ability to program in a high-level language, and familiarity with basic algorithm design and coding principles.

Core topics from unsupervised learning such as clustering, dimensionality reduction and density estimation will be studied in detail. Topics in clustering: k-means clustering, hierarchical clustering, spectral clustering, clustering with various forms of feedback, good initialization techniques and convergence analysis of various clustering procedures. Topics in dimensionality reduction: linear techniques such as PCA, ICA, Factor Analysis, Random Projections, non-linear techniques such as LLE, IsoMap, Laplacian Eigenmaps, tSNE, and study of embeddings of general metric spaces, what sorts of theoretical guarantees can one provide about such techniques. Miscellaneous topics: design and analysis of data structures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python.

COMS E4773 Machine Learning Theory. 3.00 points .

Theoretical study of algorithms for machine learning and high-dimensional data analysis. Topics include high-dimensional probability, theory of generalization and statistical learning, online learning and optimization, spectral analysis

COMS W4774 Unsupervised Learning. 3.00 points .

Prerequisites: Solid background in multivariate calculus, linear algebra, basic probability, and algorithms.

Prerequisites: see notes re: points Core topics from unsupervised learning such as clustering, dimensionality reduction and density estimation will be studied in detail. Topics in clustering: k-means clustering, hierarchical clustering, spectral clustering, clustering with various forms of feedback, good initialization techniques and convergence analysis of various clustering procedures. Topics in dimensionality reduction: linear techniques such as PCA, ICA, Factor Analysis, Random Projections, non-linear techniques such as LLE, IsoMap, Laplacian Eigenmaps, tSNE, and study of embeddings of general metric spaces, what sorts of theoretical guarantees can one provide about such techniques. Miscellaneous topics: design and analysis of datastructures for fast Nearest Neighbor search such as Cover Trees and LSH. Algorithms will be implemented in either Matlab or Python

COMS W4775 Causal Inference. 3.00 points .

Prerequisites: Discrete Math, Calculus, Statistics (basic probability, modeling, experimental design), some programming experience.

Prerequisites: see notes re: points Causal Inference theory and applications. The theoretical topics include the 3-layer causal hierarchy, causal bayesian networks, structural learning, the identification problem and the do-calculus, linear identifiability, bounding, and counterfactual analysis. The applied part includes intersection with statistics, the empirical-data sciences (social and health), and AI and ML

COMS E4775 Causal Inference. 3 points .

Prerequisites: (COMS4711W) and Discrete Math, Calculus, Statistics (basic probability, modeling, experimental design), Some programming experience

Causal Inference theory and applications. The theoretical topics include the 3-layer causal hierarchy,  causal bayesian networks, structural learning, the identification problem and the do-calculus, linear identifiability, bounding, and counterfactual analysis. The applied part includes intersection with statistics, the empirical-data sciences (social and health), and AI and ML.

COMS W4776 Machine Learning for Data Science. 3 points .

Prerequisites: ( STAT GU4001 or IEOR E4150 ) and linear algebra.

Introduction to machine learning, emphasis on data science. Topics include least square methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines kernel methods. Emphasizes methods and problems relevant to big data. Students may not receive credit for both COMS W4771 and W4776.

COMS W4824 COMPUTER ARCHITECTURE. 3.00 points .

COMS W4835 COMPUTER ORGANIZATION II. 3.00 points .

COMS E4899 Research Training. 0.00 points .

COMS W4901 Projects in Computer Science. 1-3 points .

Prerequisites: Approval by a faculty member who agrees to supervise the work.

A second-level independent project involving laboratory work, computer programming, analytical investigation, or engineering design. May be repeated for credit, but not for a total of more than 3 points of degree credit. Consult the department for section assignment.

COMS W4910 CURRICULAR PRACTICAL TRAINING. 1.00 point .

COMS E4995 COMPUTER ARTS/VIDEO GAMES. 3.00 points .

Special topics arranged as the need and availability arises. Topics are usually offered on a one-time basis. Since the content of this course changes each time it is offered, it may be repeated for credit. Consult the department for section assignment

COMS W4995 TOPICS IN COMPUTER SCIENCE. 3.00 points .

Prerequisites: Instructor's permission. Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section

COMS W4996 Special topics in computer science, II. 3 points .

Prerequisites: Instructor's permission.

A continuation of COMS W4995 when the special topic extends over two terms.

Computer Science - Electrical Engineering

CSEE W3826 FUNDAMENTALS OF COMPUTER ORG. 3.00 points .

CSEE W3827 FUNDAMENTALS OF COMPUTER SYSTS. 3.00 points .

Prerequisites: an introductory programming course. Fundamentals of computer organization and digital logic. Boolean algebra, Karnaugh maps, basic gates and components, flipflops and latches, counters and state machines, basics of combinational and sequential digital design. Assembly language, instruction sets, ALU’s, single-cycle and multi-cycle processor design, introduction to pipelined processors, caches, and virtual memory

CSEE W4119 COMPUTER NETWORKS. 3.00 points .

Introduction to computer networks and the technical foundations of the Internet, including applications, protocols, local area networks, algorithms for routing and congestion control, security, elementary performance evaluation. Several written and programming assignments required

CSEE W4121 COMPUTER SYSTEMS FOR DATA SCIENCE. 3.00 points .

Prerequisites: Background in Computer System Organization and good working knowledge of C/C++. Corequisites: CSOR W4246 Algorithms for Data Science, STAT W4203 Probability Theory, or equivalent as approved by faculty advisor. An introduction to computer architecture and distributed systems with an emphasis on warehouse scale computing systems. Topics will include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism, scheduling, caching, prefetching, network and memory architecture, latency and throughput optimizations, specialization, and an introduction to programming data center computers

CSEE W4140 NETWORKING LABORATORY. 3.00 points .

Prerequisites: ( CSEE W4119 ) or equivalent. In this course, students will learn how to put principles into practice, in a hands-on-networking lab course. The course will cover the technologies and protocols of the Internet using equipment currently available to large internet service providers such as CISCO routers and end systems. A set of laboratory experiments will provide hands-on experience with engineering wide-area networks and will familiarize students with the Internet Protocol (IP), Address Resolution Protocol (ARP), Internet Control Message Protocol (ICMP), User Datagram Protocol (UDP) and Transmission Control Protocol (TCP), the Domain Name System (DNS), routing protocols (RIP, OSPF, BGP), network management protocols (SNMP, and application-level protocols (FTP, TELNET, SMTP)

CSEE W4823 Advanced Logic Design. 3 points .

Prerequisites: ( CSEE W3827 ) or a half semester introduction to digital logic, or the equivalent.

An introduction to modern digital system design. Advanced topics in digital logic: controller synthesis (Mealy and Moore machines); adders and multipliers; structured logic blocks (PLDs, PALs, ROMs); iterative circuits. Modern design methodology: register transfer level modelling (RTL); algorithmic state machines (ASMs); introduction to hardware description languages (VHDL or Verilog); system-level modelling and simulation; design examples.

CSEE W4824 COMPUTER ARCHITECTURE. 3.00 points .

Prerequisites: ( CSEE W3827 ) or equivalent. Focuses on advanced topics in computer architecture, illustrated by case studies from classic and modern processors. Fundamentals of quantitative analysis. Pipelining. Memory hierarchy design. Instruction-level and thread-level parallelism. Data-level parallelism and graphics processing units. Multiprocessors. Cache coherence. Interconnection networks. Multi-core processors and systems-on-chip. Platform architectures for embedded, mobile, and cloud computing

CSEE W4840 EMBEDDED SYSTEMS. 3.00 points .

Prerequisites: ( CSEE W4823 ) Embedded system design and implementation combining hardware and software. I/O, interfacing, and peripherals. Weekly laboratory sessions and term project on design of a microprocessor-based embedded system including at least one custom peripheral. Knowledge of C programming and digital logic required

CSEE W4868 SYSTEM-ON-CHIP PLATFORMS. 3.00 points .

Prerequisites: ( COMS W3157 ) and ( CSEE W3827 ) Design and programming of System-on-Chip (SoC) platforms. Topics include: overview of technology and economic trends, methodologies and supporting CAD tools for system-level design, models of computation, the SystemC language, transaction-level modeling, software simulation and virtual platforms, hardware-software partitioning, high-level synthesis, system programming and device drivers, on-chip communication, memory organization, power management and optimization, integration of programmable processor cores and specialized accelerators. Case studies of modern SoC platforms for various classes of applications

Computer Science - Biomedical Engineering

CBMF W4761 COMPUTATIONAL GENOMICS. 3.00 points .

Prerequisites: Working knowledge of at least one programming language, and some background in probability and statistics. Computational techniques for analyzing genomic data including DNA, RNA, protein and gene expression data. Basic concepts in molecular biology relevant to these analyses. Emphasis on techniques from artificial intelligence and machine learning. String-matching algorithms, dynamic programming, hidden Markov models, expectation-maximization, neural networks, clustering algorithms, support vector machines. Students with life sciences backgrounds who satisfy the prerequisites are encouraged to enroll

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• Doctor of Philosophy in Computer Science (PhD)
• Graduate School
• Prospective Students
• Graduate Degree Programs

Canadian Immigration Updates

Applicants to Master’s and Doctoral degrees are not affected by the recently announced cap on study permits. Review more details

Go to programs search

PhD students in the Department of Computer Science may focus their research in the following areas:

• Artificial Intelligence:  computer vision, decision theory/game theory, knowledge representation and reasoning, intelligent user interfaces, machine learning, natural language understanding and generation, robotics and haptics.
• Computer Graphics:  animation, imaging, modeling, rendering, visualization.
• Data Management and Mining:  business intelligence, data integration, genomic analysis, text mining, web databases.
• Formal Verification and Analysis of Systems:  analog, digital and hybrid systems, VLSI, protocols, software.
• Human Centered Technologies:  human computer interaction (HCI), visual, haptic and multimodal interfaces, computer-supported cooperative work (CSCW), visual analytics.
• Networks, Systems, and Security:  high performance computing/parallel processing, networking, operating systems and virtualization, security.
• Scientific Computing:  numerical methods and software, differential equations, linear algebra, optimization.
• Software Engineering and Programming Languages:  development tools, foundations of computation, middleware, programming languages, software engineering.
• Theory: algorithm design and analysis (including empirical), algorithmic game theory, discrete optimization, graph theory, computational geometry

For specific program requirements, please refer to the departmental program website

What makes the program unique?

The UBC Department of Computer Science has many contacts in the computing industry. A strong rapport between the industry and research communities is beneficial to both, especially in cases where the department focuses its research to developing real-world applications.

I love the energy and optimism at UBC. I'm surrounded by nature (tall trees, beautiful gardens), fellow students who are excited about what they are studying, a seemingly unending stream of campus activities.

Quick Facts

Program enquiries, admission information & requirements, 1) check eligibility, minimum academic requirements.

The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:

• Canada or the United States
• International countries other than the United States

Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.

English Language Test

Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.

Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:

TOEFL: Test of English as a Foreign Language - internet-based

Overall score requirement : 100

IELTS: International English Language Testing System

Overall score requirement : 7.0

Other Test Scores

Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:

The GRE is not required.

2) Meet Deadlines

3) prepare application, transcripts.

All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.

Letters of Reference

A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.

Statement of Interest

Many programs require a statement of interest , sometimes called a "statement of intent", "description of research interests" or something similar.

Supervision

Students in research-based programs usually require a faculty member to function as their thesis supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.

Instructions regarding thesis supervisor contact for Doctor of Philosophy in Computer Science (PhD)

Citizenship verification.

Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.

4) Apply Online

All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.

Tuition & Financial Support

Financial support.

Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.

Program Funding Packages

All full-time PhD students will be provided with a funding package of at least $31,920 for each of the first four years of their PhD program. The funding package consists of any combination of internal or external awards, teaching-related work, research assistantships, and graduate academic assistantships. This support is contingent on full-time registration as a UBC Graduate student, satisfactory performance in assigned teaching and research assistantship duties, and good standing with satisfactory progress in your academic performance. CS students are expected to apply for fellowships or scholarship to which they are eligible.

Average Funding

• 40 students received Teaching Assistantships. Average TA funding based on 40 students was $6,950.
• 77 students received Research Assistantships. Average RA funding based on 77 students was $20,513.
• 18 students received Academic Assistantships. Average AA funding based on 18 students was $6,167.
• 81 students received internal awards. Average internal award funding based on 81 students was $11,015.
• 8 students received external awards. Average external award funding based on 8 students was $19,625.

Scholarships & awards (merit-based funding)

All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.

Graduate Research Assistantships (GRA)

Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their supervision. The duties constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is considered a form of fellowship for a period of graduate study and is therefore not covered by a collective agreement. Stipends vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded.

Graduate Teaching Assistantships (GTA)

Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union .

Graduate Academic Assistantships (GAA)

Academic Assistantships are employment opportunities to perform work that is relevant to the university or to an individual faculty member, but not to support the student’s graduate research and thesis. Wages are considered regular earnings and when paid monthly, include vacation pay.

Financial aid (need-based funding)

Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans .

All students may be able to access private sector or bank loans.

Foreign government scholarships

Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.

Working while studying

The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.

International students enrolled as full-time students with a valid study permit can work on campus for unlimited hours and work off-campus for no more than 20 hours a week.

A good starting point to explore student jobs is the UBC Work Learn program or a Co-Op placement .

Tax credits and RRSP withdrawals

Students with taxable income in Canada may be able to claim federal or provincial tax credits.

Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.

Please review Filing taxes in Canada on the student services website for more information.

Cost Estimator

Applicants have access to the cost estimator to develop a financial plan that takes into account various income sources and expenses.

Career Outcomes

111 students graduated between 2005 and 2013. Of these, career information was obtained for 106 alumni (based on research conducted between Feb-May 2016):

Sample Employers in Higher Education

Sample employers outside higher education, sample job titles outside higher education, phd career outcome survey, career options.

Our faculty and students actively interact with industry in numerous fields. Via internships, consulting and the launching of new companies, they contribute to the state-of-the-art in environmental monitoring, energy prediction, software, cloud computing, search engines, social networks, advertising, e-commerce, electronic trading, entertainment games, special effects in movies, robotics, bioinformatics, biomedical engineering, and more.

Alumni on Success

Job Title Senior Director, Product & Business Development

Employer NGRAIN

Enrolment, Duration & Other Stats

These statistics show data for the Doctor of Philosophy in Computer Science (PhD). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.

ENROLMENT DATA

Completion rates & times.

• Research Supervisors

Advice and insights from UBC Faculty on reaching out to supervisors

These videos contain some general advice from faculty across UBC on finding and reaching out to a supervisor. They are not program specific.

This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.

• Beschastnikh, Ivan (Computer and information sciences; software engineering; distributed systems; cloud computing; software analysis; Machine Learning)
• Bowman, William (Computer and information sciences; Programming languages and software engineering; Programming languages; Compilers; programming languages)
• Carenini, Giuseppe (Artificial intelligence, user modeling, decision theory, machine learning, social issues in computing, computational linguistics, information visualization)
• Clune, Jeff
• Conati, Cristina (artificial intelligence, human-computer interaction, affective computing, personalized interfaces, intelligent user interfaces, intelligent interface agents, virtual agent, user-adapted interaction, computer-assisted education, educational computer games, computers in education, user-adaptive interaction, Artificial intelligence, adaptive interfaces, cognitive systems, user modelling)
• Condon, Anne (Algorithms; Molecular Programming)
• Ding, Jiarui (Bioinformatics; Basic medicine and life sciences; Computational Biology; Machine Learning; Probabilistic Deep Learning; single-cell genomics; visualization; Cancer biology; Computational Immunology; Food Allergy; neuroscience)
• Evans, William (Computer and information sciences; Algorithms; theoretical computer science; Computer Sciences and Mathematical Tools; computational geometry; graph drawing; program compression)
• Feeley, Michael (Distributed systems, operating systems, workstation and pc clusters)
• Friedlander, Michael (numerical optimization, numerical linear algebra, scientific computing, Scientific computing)
• Friedman, Joel (Computer and information sciences; Algebraic Graph Theory; Combinatorics; Computer Science Theory)
• Garcia, Ronald (Programming languages; programming languages)
• Greenstreet, Mark (Dynamic systems, formal methods, hybrid systems, differential equations)
• Greif, Chen (Numerical computation; Numerical analysis; scientific computing; numerical linear algebra; numerical solution of elliptic partial differential equations)
• Gujarati, Arpan (Computer and information sciences; Systems)
• Harvey, Nicholas (randomized algorithms, combinatorial optimization, graph sparsification, discrepancy theory and learning theory; algorithmic problems arising in computer networking, including cache analysis, load balancing, data replication, peer-to-peer networks, and network coding.)
• Holmes, Reid (Computer and information sciences; computer science; open source software; software comprehension; software development tools; software engineering; software quality; software testing; static analysis)
• Hu, Alan (Computer and information sciences; formal methods; formal verification; model checking; nonce to detect automated mining of profiles; post-silicon validation; security; software analysis)
• Hutchinson, Norman (Computer and information sciences; Computer Systems; distributed systems; File Systems; Virtualization)
• Kiczales, Gregor (MOOCs, Blended Learning, Flexible Learning, University Strategy for Flexible and Blended Learning, Computer Science Education, Programming Languages, Programming languages, aspect-oriented programming, foundations, reflections and meta programming, software design)
• Lakshmanan, Laks (data management and data cleaning; data warehousing and OLAP; data and text mining; analytics on big graphs and news; social networks and media; recommender systems)
• Lecuyer, Mathias (Machine learning systems; Guarantees of robustness, privacy, and security)
• Lemieux, Caroline (Programming languages and software engineering; help developers improve the correctness, security, and performance of software systems; test-input generation; specification mining; program synthesis)
• Leyton-Brown, Kevin (Computer and information sciences; Artificial Intelligence; Algorithms; theoretical computer science; Resource Allocation; Computer Science and Statistics; Auction theory; game theory; Machine Learning)
• MacLean, Karon (Computer and information sciences; Information Systems; design of user interfaces; haptic interfaces; human-computer interaction; human-robot interaction)

Doctoral Citations

Sample thesis submissions.

• Discrete optimization problems in geometric mesh processing
• Towards alleviating human supervision for document-level relation extraction
• Methods for design of efficient on-device natural language processing architectures
• A formal framework for understanding run-time checking errors in gradually typed languages
• Understanding semantics and geometry of scenes
• Computational tools for complex electronic auctions
• From videos to animatable 3d neural characters
• Structured representation learning by controlling generative models
• Versatile neural approaches to more accurate and robust topic segmentation
• Machine learning for spectroscopic data analysis : challenges of limited labelled data
• Enriching block-based end-user programming with visual features
• Accelerating Bayesian inference in probabilistic programming
• Computationally efficient geometric methods for optimization and inference in machine learning
• Processing freehand vector sketches
• Exploration on the synergy between discourse and neural summarizers

Related Programs

Same specialization.

• Master of Science in Computer Science (MSc)

Same Academic Unit

• Master of Data Science (MDS)

At the UBC Okanagan Campus

Further information, specialization.

Computer Science covers Bayesian statistics and applications, bioinformatics, computational intelligence (computational vision, automated reasoning, multi-agent systems, intelligent interfaces, and machine learning), computer communications, databases, distributed and parallel systems, empirical analysis of algorithms, computer graphics, human-computer interaction, hybrid systems, integrated systems design, networks, network security, networking and multimedia, numerical methods and geometry in computer graphics, operating systems, programming languages, robotics, scientific computation, software engineering, visualization, and theoretical aspects of computer science (computational complexity, computational geometry, analysis of complex graphs, and parallel processing).

UBC Calendar

Program website, faculty overview, academic unit, program identifier, classification, social media channels, supervisor search.

Departments/Programs may update graduate degree program details through the Faculty & Staff portal. To update contact details for application inquiries, please use this form .

Geoffrey Woollard

I applied to UBC in 2020, during the pandemic. It was a close call between working with Marcus Brubaker, who co-founded my former employer Structura Biotechnology, before becoming an Assistant Professor at York University, and working with Khanh Dao Duc at UBC. Khanh introduced me to his...

Michael Yin

I love Vancouver! It's the greatest city in the world. I love the integration of nature into the city; it has all of the mountains, forests, and oceans. In addition, the city is a melting pot of cultures, and that's definitely reflected at UBC. It feels like there's a place for everyone at UBC....

Baraa Orabi

I think three factors had a differentiating effect on this decision: UBC's unique multidisciplinary environment which is key to my research as a computer scientist and bioinformatician. UBC being on the West Coast generally and Vancouver specifically and the amazing weather and nature that comes...

The city and the sea

Take a break from studying with opportunities at your fingertips. Whether you want to settle down in a café or take your research outdoors, we have a place for you.

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• Reimagining Graduate Education
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M.S. in Data Science

The m.s. in data science allows students to apply data science techniques to their field of interest..

Ours is one of the most highly-rated and sought-after advanced data science programs in the world.

Program Highlights

Columbia data science students have the opportunity to conduct original research, produce a capstone project , and interact with our industry partners and world-class faculty.

This program is jointly offered in collaboration with the Graduate School of Arts and Sciences’ Department of Statistics, and The Fu Foundation School of Engineering and Applied Science’s Department of Computer Science and Department of Industrial Engineering and Operations Research.

Some students are primarily concerned about data ethics, others are excited about data science as a new evolution in knowledge, but all are interested in how data science is changing our everyday lives.

Where are columbia data science graduates now*.

  *A partial list as of May 2022

Computer Science

Prerequisites: Students are expected to have solid programming experience in Python or with an equivalent programming language. This class is intended to be accessible for students who do not necessarily have a background in databases, operating systems or distributed systems. The goal of this class is to provide data scientists and engineers that work with big data a better understanding of the foundations of how the systems they will be using are built. It will also give them a better understanding of the real-world performance, availability and scalability challenges when using and deploying these systems at scale. In the course we will cover foundational ideas in designing these systems, while focusing on specific popular systems that students are likely to encounter at work or when doing research. 

Spring Semester: 3 credits

COMS 4721 is a graduate-level introduction to machine learning. The course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms. Additional topics, such as representation learning and online learning, may be covered if time permits.

Prerequisites: Background in linear algebra and probability and statistics.

Methods for organizing data, e.g. hashing, trees, queues, lists, priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.

Prerequisites: basic knowledge in programming (e.g., at the level of COMS W1007), a basic grounding in calculus and linear algebra.

Engineering

Prerequisites: CSOR W4246 Algorithms for Data Science, STAT W4105 Probability, COMS W4121 Computer Systems for Data Science, or equivalent as approved by faculty advisor. Co-requisites: to be completed alongside or after: STAT W4702 Statistical Inference and Modeling, COMS W4721 Machine Learning for Data Science, STAT W4701 Exploratory Data Analysis and Visualization, or equivalent as approved by faculty advisor.

This course provides a unique opportunity for students in the M.S. in Data Science program to apply their knowledge of the foundations, theory and methods of data science to address data science problems in industry, government and the non-profit sector. The course activities focus on a semester-length data science project sponsored by a faculty member or local organization. The project synthesizes the statistical, computational, engineering challenges and social issues involved in solving complex real-world problems.

Fall and Spring Semesters: 3 credits

This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.

Prerequisite: Calculus.

Prerequisite: Programming, fundamentals of data visualization, layered grammar of graphics, perception of discrete and continuous variables, introduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.

Course covers fundamentals of statistical inference and testing, and gives an introduction to statistical modeling. The first half of the course will be focused on inference and testing, covering topics such as maximum likelihood estimates, hypothesis testing, likelihood ratio test, Bayesian inference, etc. The second half of the course will provide introduction to statistical modeling via introductory lectures on linear regression models, generalized linear regression models, nonparametric regression, and statistical computing. Throughout the course, real-data examples will be used in lecture discussion and homework problems.

Prerequisites: Working knowledge of calculus and linear algebra (vectors and matrices) and STAT GR5203 or equivalent.

In addition to the 21 credits of core classes, M.S. in Data Science students are required to complete a minimum of nine (9) credits of electives. In addition to the DSI elective courses, MS students are encouraged to explore courses offered across the university and take advantage of the expertise in a wide range of disciplines at Columbia. Prior to registration, students receive advisement to determine if a course of interest is relevant and meets the criteria of a 4000-level or higher, technical course completed for a letter grade. You are welcome to explore the  Columbia Directory of Classes  for possible courses.

The following courses are examples of classes that MS students have used for elective credit. Elective courses and schedules are dependent on faculty availability and may vary each semester. Past course offerings are not guaranteed to be offered in the future.

Please note that many departments, including DSI, give registration priority to their students. Space permitting, courses are then opened up to students outside the department.

This class offers a hands-on approach to machine learning and data science. The class discusses the application of machine learning methods like SVMs, Random Forests, Gradient Boosting and neural networks on real world dataset, including data preparation, model selection and evaluation. This class complements COMS W4721 in that it relies entirely on available open source implementations in scikit-learn and tensor flow for all implementations. Apart from applying models, we will also discuss software development tools and practices relevant to productionizing machine learning models.

This course provides a practical, hands-on introduction to Deep Learning. We aim to help students understand the fundamentals of neural networks (DNNs, CNNs, and RNNs), and prepare students to successfully apply them in practice. This course will be taught using open-source software, including TensorFlow 2.0. In addition to covering the fundamental methods, we will discuss the rapidly developing space of frameworks and applications, including deep learning on the web. This course includes an emphasis on fairness and testing, and teaches best practices with these in mind.

Data scientists often have to answer questions that will lead to decisions about actions a company might take. Often, they will be able to run an experiment, and see the effect the decision might have by testing it first.  Other times, they will only have observational data at their disposal. In both cases, they need to infer the causal effect of an action on some outcomes of interest. Causal inference is an essential skill for a data scientist. Without a proper understanding, potential biases as large as 1000% have been observed in practice! This course will cover the basics of the potential outcomes framework, the Pearlian framework, and a collection of methods for observational and experimental causal inference. We’ll use examples from industry applications throughout the course, especially focused on web applications.

“Data analytics pipeline” focuses on the intersection between data science, data engineering, and agile product development. In this course you’ll learn some common data generating processes, how the data is transported to be stored, how analytics and compute capabilities are built on top of that storage, and how production machine learning and modeling platforms can be built in that context. There is a strong focus on good architecture design patterns, and practical implementation considerations that focus on delivering results over building perfect systems

This course is designed as an introduction to elements that constitutes the skill set of a data scientist. The course will focus on the utility of these elements in common tasks of a data scientist, rather than their theoretical formulation and properties. The course provides a foundation of basic theory and methodology with applied examples to analyze large engineering, business, and social data for data science problems. Hands-on experiments with R or Python will be emphasized.

The world is full of noise and uncertainty. To make sense of it, we collect data and ask questions. Is there a tumor in this x-ray scan? What affects the quality of my manufacturing plant? How old is this planet I see through the telescope? Does this drug actually work? To pose and answer such questions, data scientists must iterate through a cycle: probabilistically model a system, infer hidden patterns from data, and evaluate how well our model describes reality. By the end of this course, you will learn how to use probabilistic programming to effectively iterate through this cycle. Specifically, you will master modeling real-world phenomena using probability models, using advanced algorithms to infer hidden patterns from data, and evaluating the effectiveness of your analysis. You will learn to use (and perhaps even contribute to) Edward throughout this course.

This applied Natural Language Processing course will focus on computational methods for extracting social and interactional meaning from large volumes of text and speech (both traditional media and social media). Topics will include:

• Sentiment Analysis: automatic detection of people’s sentiment towards a topic, event, product, or persons. Practical applications in various domains will be discussed (e.g., predicting stock market prices, or presidential elections)
• Emotion and Mood Analysis: automatic detection of people’s emotions (angry, sad, happy) by analyzing various media such as books, emails, lyrics, online discussion forums. Practical applications in various domains (such as predicting depression, categorization of songs)
• Belief Analysis and Hedging: automatic detection of people’s beliefs (committed belief and non-committed beliefs) from social media. Analysis of the use of hedging as a communicative device in various media: online discussions, scientific writing or legal discussions.
• Deception Detection (e.g., detecting fake reviews online, or deceptive speech in court proceedings)
• Argumentation Mining: automatic detection of arguments from text, such as online discussion or persuasive essays. Practical application for various domains (e.g., political, legal or education (e.g., improving students’ skills in writing persuasive essays)
• Social Power: automatic detection of power structure in organizations by analyzing people’s communications such as emails.
• Extracting Social Networks from text, such as networks of characters from novels, or networks from social media (e.g., people holding particular opinions, or network of friends).
• Personality and Interpersonal Stance

Contact DSI at [email protected] for more information about this course.

Personalization is a key tool for enhancing customer experience across industries, thereby driving user loyalty and customer value. It is therefore no surprise that creating and enhancing personalization systems is also increasingly one of the core responsibilities of data science teams, and a key focus for many of the machine learning algorithms in the sector. This course will focus on common personalization algorithms and theory, including behavior-based and content-based recommendation, commonly encountered issues in scaling and cold-starts, and state of the art research. It will also look at how businesses use, and misuse, these techniques in real world applications.

The vast proliferation of data and increasing technological complexities continue to transform the way industries operate and compete. Over the last two years, 90 percent of the data in the world has been created as a result of the creation of 2.5 quintillion bytes of data on a daily basis. Commonly referred to as big data, this rapid growth and storage creates opportunities for collection, processing and analysis of structured and unstructured data. Financial services, in particular, have widely adopted big data analytics to inform better investment decisions with consistent returns. In conjunction with big data, algorithmic trading uses vast historical data with complex mathematical models to maximize portfolio returns. The continued adoption of big data will inevitably transform the landscape of financial services. However, along with its apparent benefits, significant challenges remain in regards to big data’s ability to capture the mounting volume of data. The increasing volume of market data poses a big challenge for financial institutions. Along with vast historical data, banking and capital markets need to actively manage ticker data. Likewise, investment banks and asset management firms use voluminous data to make sound investment decisions. Insurance and retirement firms can access past policy and claims information for active risk management. The course will be a mix of Theory and practice with real big data cases in finance. We will invite guest lecturers mostly for real Big Data Finance Applications. We will give MATLAB, R, or Python examples.

The course focuses on translating technical expertise into work-place solutions by teaching students to: (1) identify relevant shortfalls in traditional processes; (2) precisely match datasets and machine learning features to overcome these shortfalls; (3) narrowly define value to fit work place processes, analytical framework, and bottom line.  Each class will be structured as an actual end-to-end work-place project and use concrete examples to teach students to design, build and deliver solutions that integrate these considerations. A combination of assignments, presentation, and research paper will be sued to evaluation students’ progress in bridging technical and applied solutions with evaluation criteria matching those of a work-place project.

Images are everywhere. How to deal with image data, especially with big data, is an urgent problem for data analysts.  Machine learning has proven to be a powerful technology to process and analyze such big data.  The course will discuss how machine learning methods are use in the field of image analysis, including biometrics (iris and face recognition), natural images (object identification/recognition), brain images (encoding and decoding), and handwritten digit recognition.  Students will learn how to sue traditional machine learning methods in image data processing and analysis, and develop techniques to improve these methods.  The aim of this course is to prepare students with basis knowledge and skills to explore opportunities using machine learning in the field of image analysis.

Cross-Registration

• Instructions for Non-Data Science Students Please note that Data Science students have priority registration, so enrollment will be dependent on the space available after our student registration. Non-Data Science students will be able to register/join a waitlist via SSOL starting  August 29th  for  Fall 2023 .  Please be sure to obtain your program advisor approval before enrolling.  The Fall 2023 Change of Program Period is Tuesday, September 5, 2023 through Friday, September 15, 2023

I wanted to be at an institution that would truly challange me and put me at the forefront of growing areas of research in data science. Columbia promised the most rigorous and innovative curriculum on the planet...I knew the educational experience here would be like no other.

Kevin Womack

Class of 2021

Explore More from Master of Science in Data Science

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Course Inventory

The number of data science-related courses available to Columbia students is growing. Here's an updated list.

Capstone Projects

All of our students complete capstone projects with industry affiliates and thought leaders.

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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Degree Requirements

All students completing a PhD degree must fulfill the following minimum requirements:

Complete all of the computer science (CS) course work requirements of the Master’s degree in CS or have an MS degree in CS from another institution.  The required 15 hours at the 8000- level must be regular CS courses, excluding Research or Problems courses.  The student must maintain an overall GPA of at least 3.4/4.0 in their graduate level course work (excluding Research and Problems courses). Earn a minimum of 72 credit hours of course work and research past the student’s Bachelor's degree.

Qualifying and Comprehensive Examination Process

Students will need to pass a qualifying examination process to be admitted to candidacy in the CS PhD program within two years of program enrollment and will need to pass a comprehensive examination covering their areas of expertise within five years of program enrollment.

Students will complete a doctoral dissertation on a topic approved by the candidate’s advisory committee and defend the dissertation in a final oral examination.

Students will have at least one journal paper submitted, accepted or published, as approved by the advisor.

Students will present on a research topic as part of the CS Seminar Series at some point between passing the qualifying exam and the dissertation defense. This policy is effective for entering PhD students in Spring 2013 and after.

Seminar Attendance

The approval of the D4 form is tied to the attendance records for the department's seminar series. PhD students are required to attend a total of at least twenty EECS Seminar Series presentations. Master's students who add the PhD program can apply their seminar attendance as part of their Master's program toward the attendance requirement for doctoral students. Students will submit a list of research publications with the D4 form.

After the successful completion of the Qualifying Process, the D1 Qualifying Exam Results and Doctoral Committee Approval form should be submitted to the Graduate School, followed by the D2 Plan of Study for the Doctoral Degree form. The D3 form Doctoral Comprehensive Examination Results is submitted when the student has completed the Comprehensive Exam. Graduate School policy requires the completion of the Comprehensive Exam within five years of starting the PhD program. At least seven months must pass between the Comprehensive Exam and the dissertation defense, which is followed by submission of the D4 Dissertation Defense form. PhD students must submit a list of publications to the graduate committee prior to the dissertation defense.

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Ph.D. in Computer Science and Engineering

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Doctor of Philosophy in Computer Science and Engineering

For Admission and Application Information .

Doctor of Philosophy in Computer Science and Engineering (Ph.D.)

• Ph.D. with Specialization in Biomedical Engineering
• Ph.D. with Specialization in Civil Engineering
• Ph.D. with Specialization in Computer Science
• Ph.D. with Specialization in Electrical Engineering
• Ph.D. with Specialization in Mechanical Engineering

About the Program

The Ph.D. program has an interdisciplinary focus with emphasis on design thinking and the ability to solve complex problems.

Doctoral research will focus on the interface between computer science and engineering including additive manufacturing, biomedical engineering, mechatronics, underwater communication, nanotechnology, simulation, modeling, smart grids, rehabilitation engineering, cyber security, big data analysis, robotics, geo-transportation, environmental engineering, and urban engineering. Ph.D. research thesis results will be published in peer reviewed publications aligning thesis outcomes with research priorities supported by NSF, DOD, NIST, DOE, and NIH.

For more information contact

Dr. Paul Cotae Director, SEAS Research Center Director, Ph.D. Program 202.274.6290 [email protected]

PhD in Computer Science and Engineering

• Specializations
• Presentations

Defense Presentations

• Juan F. Ramirez Rochac Wed., December 14, 2:00 PM
• Vincent Tanoe Wed., December 14, 9:00 AM
• Temechu Zewdie Tues., December 13, 10:00 AM
• John Irungu
• Juan F. Ramirez Rochac
• Vincent Tanoe
• Temechu Zewdie

PhD Program

The student must complete the following requirements for the PhD program:

• Pass the Research Proficiency Evaluation (RPE)
• Complete the Comprehensive Course Requirement
• Successfully defend the Thesis Proposal Exam
• Pass the Final Doctoral Examination
• Have the final thesis approved by Faculty of Graduate and Post Doctoral Studies

Completing the first two milestones fulfills the Faculty of Graduate and Post Doctoral Studies Comprehensive exam requirement. Completing the first three milestones enables the student to be admitted to candidacy. 

A student is expected to be admitted to candidacy within 36 months from the date of initial registration and to complete the PhD within 6 years. Extensions to these deadlines maybe granted under exceptional circumstances with the permission of the Graduate Affairs Committee and Faculty of Graduate and Postdoctoral Studies.

Research Supervisor and Supervisory Committee

Every incoming graduate student is assigned a faculty member as his/her advisor.  These assignments are made keeping in mind the research interests of the student and the workload of the faculty member. In the case of entering PhD students, the advisor is a member of the “offer sponsorship team,” which consists of one to three faculty. It is the student’s responsibility to formalize a supervisory relationship with a faculty member in his or her area of interest within one term. The advisor and any other sponsors are natural choices for this role, but other faculty may also be considered (if they are interested). Please refer to PhD Supervisory Committee for details.

Research Proficiency Evaluation (RPE)

Students admitted to the PhD program are required to demonstrate their research proficiency by completing a research project with under the supervision of a one or more faculty members, and presenting their results in a written report and oral examination before their RPE committee.  Please refer to RPE for details.

Comprehensive Course Requirement

All PhD students are required to submit the Comprehensive Course Requirement form   to the Graduate Affairs Committee within the first two months of the initial registration.  The objective of the comprehensive course requirement is to ensure that the student obtains a breadth of knowledge of computer science, as well as sufficient depth in a specific field.  Students should indicate what courses they will be taking or have taken that can satisfy the breadth and depth components of the comprehensive course requirement.  If the student has taken courses outside the department that can satisfy the breadth requirement, s/he must contact the faculty in the research area for approval.  Once the comprehensive course proposal is approved, the student can take the courses in accordance with the proposal.

Note that for courses not contributing to the comprehensive course requirement, a minimum of 68% (B-) must be achieved. Courses contributing to the comprehensive course requirement have a even higher requirement. Please refer to PhD Program Comprehensive Course Requirement for details.

Thesis Proposal

Having formalized a thesis supervisor and having successfully completed the RPE, the student will continue with the development of a PhD thesis proposal.  This proposal must be presented in written form to the supervisory committee by the end of the second year of the PhD program. Please refer to Thesis Proposal for details.

Completing the Research Program

Once the thesis proposal examination is passed, the student must carry out a research program in accordance with his or her research proposal under the supervisor’s guidance, with periodic reviews by the student’s committee. A thesis describing his or her research findings must be written by the student, approved by the committee and an external examiner, and defended at a final oral examination set up by the Faculty of Graduate and Postdoctoral Studies. A guide to the preparation of PhD theses is provided by the Faculty of Graduate and Postdoctoral Studies. The student has the final responsibility for meeting the requirements and deadlines of the Faculty of Graduate and Postdoctoral Studies.

Program Timeline for students starting in September and January

Applications for 2024 Columbia Summer Session programs are now open!

Student - April 17, 2024

An Opportunity to Take a Deep Dive Into the Field of Technology Management On Columbia’s Campus

• Technology Management

By Abhijit Rajkumar Patharkar, candidate for an M.S. in Technology Management

Every semester, the Columbia University School of Professional Studies M.S. in  Technology Management program hosts a four-day residency on Columbia’s New York City Morningside campus that gives students in the executive cohort the opportunity to come together, further relationships developed in the program, and take a deep dive into the field of technology management. For the Executive M.S. students who choose to take courses online, this residency offers opportunities for in-person interaction with faculty, insightful sessions and panels featuring industry leaders, and collaborative activities such as group discussions and debates that enrich the overall learning experience.

“Our residencies are a cornerstone of our program. It’s all about our community,” said Art Chang, associate program director. “Residencies build the social fabric of our student community and extend it to other important communities. They also help students envision their future selves, as represented by successful alumni and leading professionals who use technology innovation to advance business and society.”

Throughout the last residency, held in January, participants explored a comprehensive array of topics spanning strategic advocacy, leadership through storytelling, cybersecurity, product-market fit, artificial intelligence (AI), and data engineering. The experience ended with a session in which all executive students delivered pitches about their innovative business ideas to a distinguished panel of evaluators, including lecturers Stephano Kim, cofounder of Qonsent; Trace Wax, AI product and engineering trainer; and Dawn Barber, the program’s industry liaison.

The diverse curriculum provided a holistic perspective, equipping the participants with a multifaceted skill set essential for navigating the complexities of technology management. Each of the four days focused on one theme related to the ever-evolving field.

Day 1: Shaping Digital Leaders

Conrad Fernandes, a program lecturer who teaches Accounting & Finance, provided a refresher on course concepts. He urged students to delve into daily business news amidst the advancing tech landscape for a holistic understanding of the field. Art Chang talked about digital strategy and leadership, emphasizing the power of storytelling in leadership. He also explored the lean startup principles and stressed the importance of empathy. Alexis Wichowski, program director, challenged students with a simulated scenario, prompting 60-second elevator pitches evaluated by the faculty members. 

Day 2: Crafting Product Molecules

Janice Fraser, lecturer in the program, led the Executive Seminar 2, guiding students through "the product molecule." Hands-on work involved perfecting pitches on product market fit and a group critical thinking activity. Lecturer Amy Radin delved into strategic advocacy and navigating critical concepts, while Art Chang offered a tech introduction on infra origins, monoliths, distributed systems, N-tier architectures, and Open-Source.

Day 3: Unveiling Legacies and AI Insights

Lecturer Lauren Goodwin, Ph.D., explored the question "What's your legacy?" prompting students to share their aspirations. “AI does not replace our creativity,” said Goodwin. “It empowers it.” Focusing on machine learning (ML) and AI, she shared practical applications, including AI in construction, and talked about data engineering and cloud concepts, along with hosting a session on how to effectively choose a cloud ML platform.

Day 4: Business Ideas Take Center Stage

The final day featured students presenting their pitches  to a panel. The presentations on topics including finance, security, data engineering, marketing, and AI illustrated the innovation and entrepreneurial spirit within the Spring 2024 Executive Residency.

“In the swiftly evolving business landscape of the 21st century, visionary technology leadership stands at the forefront of innovation,” said student Michael Nicholas Colella. “Our residencies are a chance to unite some of the world’s most forward-thinking technology leaders and practitioners in the same room to ideate on how to turn challenges into triumph and help businesses thrive responsibly.”

About the Program

Columbia University's  Master of Science in Technology Management is designed to respond to the urgent need for strategic perspectives, critical thinking, and exceptional communication skills at all levels of the workplace and across all types of organizations.

Related News

An inside look at real madrid and spain’s most iconic sports properties students in the sports management program traveled to madrid for spring break as part of the program’s collaboration with universidad europea real madrid. student sustainability management student addresses gender disparity in the cop29 climate committee the absence of female representation in influential positions undermines the effectiveness and legitimacy of the cop process, writes m.s. in sustainability management student sanaya kriplani. student this former athlete is working toward a career in sports management after the pitcher’s mound, mo’ne davis makes the most of her time at columbia’s school of professional studies. all news footer social links.

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PhD students earn major NSF graduate research fellowships

Three Electrical, Computer and Energy Engineering graduate students have received 2024 National Science Foundation (NSF) Graduate Research Fellowships for their promising quantum and metameterial antennas research.  

This year, the NSF awarded 27 students from CU Boulder , including 18 from the College of Engineering and Applied Science with the 2024 graduate research fellowship, a prestigious award recognizing students in a wide variety of STEM disciplines, exploring some of the most pressing issues of our time. 

Each recipient will receive three years of financial support, including an annual stipend of $37,000, as well as professional development and research opportunities.

Aliza Siddiqui

Advisor: Joshua Combes Lab: Combes Group

Bio: Siddiqui is a first-year PhD student with a research concentration in Quantum Engineering and Architecture. She graduated from Louisiana State University, home of the Tigers, with a degree in computer science.

My proposal involves creating a new benchmarking/testing framework for the next generation of error-corrected quantum computers. Given the noise of physical qubits, recent work has suggested combining the state of several physical qubits to create a logical qubit. I will collaborate with Dr. Josh Combes and Sandia National Labs for my PhD. Through this work, the quantum community will have a tool-kit that will help us determine how well a quantum computer performs, diagnose what and where the issues are and create solutions to realize full-scale, error-corrected quantum systems. 

Dylan Meyer

Advisor: Scott Diddams Lab: Frequency Comb & Quantum Metrology Lab

Bio: Meyer is a first-year PhD student in the FCQM group. He received his undergraduate degree from the University of Alabama in Electrical Engineering.

My research proposal is the development of highly stable and robust millimeter wave time and frequency (T&F) transfer, supporting T&F transfer between atomic clocks. T&F transfer is used to create clock networks that are essential for positioning and navigation, such as GPS and essential infrastructure like the Internet and power grid. These technologies support up to $1 billion dollars of trade and financial transactions a day. In addition, these clock networks are capable of fundamental science experiments capable of probing new and exciting questions related to physics and geodesy.

Advisors:  Cody Scarborough and Robert MacCurdy Lab Groups:  EMRG and MAClab

Bio:  Pham received their Bachelor's and Master's degrees in Electrical & Computer Engineering from the University of Oklahoma, where he conducted research on RF filters. After graduating, he worked for 3 years in industry as an RF engineer developing radar systems. He will begin his PhD this fall 2024. 

My research proposal is on the application of multi-material additive manufacturing techniques for metamaterial antennas. Metamaterial antennas are capable of more sophisticated capabilities and unique form-factors compared to conventional antennas. By leveraging multi-material additive manufacturing, there are more degrees-of-freedom for the shape and composition of the metamaterials. This research would enhance the design flexibility and capabilities of next-generation antennas to meet the growing performance demands of future wireless systems.

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Departments

• Ann and H.J. Smead Aerospace Engineering Sciences
• Chemical & Biological Engineering
• Civil, Environmental & Architectural Engineering
• Computer Science
• Electrical, Computer & Energy Engineering
• Paul M. Rady Mechanical Engineering
• Applied Mathematics
• Biomedical Engineering
• Creative Technology & Design
• Engineering Education
• Engineering Management
• Engineering Physics
• Environmental Engineering
• Integrated Design Engineering
• Materials Science & Engineering

Affiliates & Partners

• ATLAS Institute
• BOLD Center
• Colorado Mesa University
• Colorado Space Grant Consortium
• Discovery Learning
• Engineering Honors
• Engineering Leadership
• Entrepreneurship
• Herbst Program for Engineering, Ethics & Society
• Integrated Teaching and Learning
• Global Engineering
• Mortenson Center for Global Engineering
• National Center for Women & Information Technology
• Western Colorado University