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Thesis & Dissertation Deposit

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• 2023 Thesis Show
• 2022 Thesis Show
• 2021 Thesis Show
• 2020 Thesis Show

Submitting Your Thesis or Dissertation to CMU Libraries Online

This session will walk through the steps for depositing your thesis or dissertation to KiltHub , the CMU institutional repository and the thesis and dissertation database, ProQuest. We will also cover intellectual property considerations like embargos, licensing, and how to manage published materials that are part of the thesis.

Related LibGuide: Theses & Dissertations by Katie Behrman

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Thesis and dissertation standards

The thesis or dissertation must be a document of the best professional standards. It is also good practice for the student to prepare a document that meets the criteria for publication in the relevant professional journals. As the original copy of the thesis or dissertation will be kept in the University Libraries, and copied for microfilming and other purposes, the paper and the production must conform to standards of long archive life and clear reproducibility. In addition, an electronic copy of the thesis or dissertation is required by the College of Engineering for archiving by the department. These instructions provide a guide for the production of a high-quality thesis or dissertation document, and formatting specifications to ensure some basic consistency among engineering theses and dissertations. It is the responsibility of the student to see that these guidelines are met, and the responsibility of the department to confirm this before submitting the thesis or dissertation to the College of Engineering Dean for approval. While preparing your thesis or dissertation the guidelines in the booklet, Publishing Your Doctoral Dissertation with UMI Dissertation Publishing, provided by ProQuest/UMI should be followed. Download a copy of this booklet, or obtain it from the department's graduate coordinator. Other requirements specific to the college are provided below.

For the latest and most complete graduate policies, please view or download the graduate student handbook .

M.S. thesis and Ph.D. dissertation due dates

Submission procedure, required documentation, manuscript format requirements.

August and December graduates

Theses and dissertations must be submitted to the department ten days before the Final Grades Due date. The department must submit the thesis or dissertation and documentation to the Dean by the Final Grades Due date.

May graduates

Theses and dissertations must be submitted to the department ten days before the Final Grades for Graduating Students Due date. The department must submit the thesis or dissertation and documentation to the Dean by the Final Grades for Graduating Students Due date.

The College of Engineering requires that all theses and dissertations be submitted to both the Carnegie Mellon University Institutional Repository and the ProQuest ETD Administrator Repository. This can be accomplished through the ProQuest ETD Administrator website .

ProQuest offers two publishing options: Traditional Publishing and Open Access Publishing PLUS. In all types of publishing, you will retain the copyright to your work . For a fee, ProQuest will officially register a student’s copyright with the U.S. Copyright Office. Official registration is not required to maintain the copyright, but registration may provide certain legal benefits. For more information, view the UMI Copyright Guide .

• Traditional publishing Students enter into an agreement granting ProQuest the non-exclusive license to publish their abstract and to duplicate and distribute their dissertation. The abstract, bibliography, and other metadata of the thesis or dissertation will be included in the ProQuest Dissertations and Theses database (PQDT). ProQuest pays authors a 10% royalty on any sales of their work.
• Open access publishing through ProQuest PLUS Students enter into an agreement granting ProQuest the non-exclusive license to publish their work on the ProQuest Dissertations and Theses Open Database and make it available for free download. Students do not receive royalties with this option. There is a one-time upfront fee. View more information on Open Access Publishing PLUS . 

Carnegie Mellon University Institutional Repository

During the ProQuest ETD Administrator submission process, students will be required to publish their thesis or dissertation at the Carnegie Mellon University Institutional Repository. This repository, supported by the Libraries, will provide online, open access to work produced by Carnegie Mellon University faculty and students. There is the option to restrict the thesis or dissertation to only campus (archival) access. There is no fee to submit.

Publishing to the Institutional Repository does not affect authorial copyright ownership. All embargo options will be honored.

Embargo options

An embargo is the ability to delay the release of a thesis or dissertation for a limited period of time, often due to pending patents, material within the work that cannot be released due to copyright, or a desire to publish all or part of the work in a journal or book.

Supplementary materials

Supplementary materials, such as the raw data underlying the research, should be uploaded during the submission to the ProQuest ETD Administrator process. The materials will be made available online with the thesis or dissertation in the Institutional Repository or available on a CD or DVD if a printed copy is requested.

Departmental copies

The thesis or dissertation may be archived by the department on a non-public server. In some departments, the author will have the option to post the thesis or dissertation on a publicly accessible internet site maintained by the department. Review the departmental handbook for more information.

University Libraries repository

ProQuest ETD Administrator website

The following documents must be submitted the College of Engineering Graduate School in additional to the uploaded submission of the dissertation:

• A pdf of the completed document
• A Signature Page, signed by the advisor(s) and Department Head(s)
• A Committee Page, signed by all committee members
• A Submission Checklist that confirms proper formatting of the document and copyright assertion decision

Signature page

Separate from the pdf of each thesis or dissertation must be a Signature Page and must follow the format specified for   regular degrees and joint degrees. The original signature page must be signed by the thesis or dissertation advisor(s), the department head, and the Dean or Associate Dean for Graduate and Faculty Affairs of the College of Engineering. Only one original signature page should accompany the thesis or dissertation submitted to the department head and Dean for review and approval.

Committee page

Separate from the pdf of each thesis or dissertation must be a Committee Page. The original Committee Page must be signed by all members of the committee for doctoral works and all readers for master’s works.

Submission checklist

Separate from the pdf of each thesis or dissertation must be a Submission Checklist . The Submission Checklist should be thoroughly reviewed to ensure all requirements for submission have been met. The Checklist must be completed and signed by the student.

Title page template

Copyright page

Committee sheet

Regular degree signature page

Regular degree co-advisor signature page

Joint degree signature page

Joint degree co-advisor signature page

Thesis submission checklist

Except as specifically superseded by directions from the candidate's major department and ProQuest, the general rules with respect to form shall follow those provided below. Some of the guidance is from K. L. Turabian,  A Manual for Writers of Term Papers, Thesis and Dissertations,  8th Edition, University of Chicago Press, Chicago, 2013.

The preparation of the thesis and dissertation and copies are the student's responsibility unless departmental policies dictate otherwise.

Your title is the first thing your readers read. It should announce the topic and communicate the conceptual framework of the thesis or dissertation using keywords that provide information to both the reader and potential search algorithms.

Choose a single, readable and widely available typeface/font, such as Times New Roman, Arial or Helvetica. If using a less common typeface, embed the font in the electronic file. Avoid ornamental typefaces. In general, use at least ten-point or twelve-point font for the body of the text.

The first page of the pdf will be a title page. The title page of the dissertation should follow the format specified in the  template . Note that the title page must follow the template and not include additional information.

If the student asserts their copyright, then the second page of the pdf will be the copyright page, according to the template . If the student does not wish to assert copyright, then they must indicate that choice on the submission checklist page .

Acknowledgements page

All theses and dissertations must include an Acknowledgments section. This section is used to thank mentors and colleagues or name the individuals or institutions that supported your research or provided special assistance, such as consultation or aid. Acknowledge any owners of copyrighted materials that have granted you permission to reproduce their work. Describe all sources of funding from outside grants, fellowships, awards, or self-supported funding. For any grants, include the identifying number. Acknowledgment of the source(s) of support is important ethically in all research publications and presentations, including theses, to give the sponsors the recognition they deserve, and also to disclose publicly the organization or persons funding the research.

For doctoral submissions, the doctoral committee must also be listed in the Acknowledgments, and the chair of the committee must be identified.  The doctoral committee should not be listed on the title page.  

The abstract will be made available in the ProQuest Dissertations and Theses database (PQDT). Do not include footnotes, references, or unexplained abbreviations. There is no word limit on the abstract, however it should be concise.

Table of contents

The table of contents should include page references.

List of tables

Include titles and page references.

List of figures and illustrations

The body of the thesis or dissertation should be broken into the following sections:

• Introduction
• Main body— with larger divisions and more important minor divisions indicated by suitable, consistent headings
• Summary and conclusions— highlighting the key findings and conclusions of the work presented. For engineering and science theses and dissertations, this section often also includes recommendations for follow-up research.
• References— see below
• Appendices— each appendix should have a title and be listed in the Table of Contents

Each page in a thesis or dissertation should be assigned a number. The following plan of page numbering generally is accepted:

• Do not number the Title or Copyright Page , although these pages will be included in the page count
• Preliminaries : Use small Roman numerals (i, ii, iii, iv, etc.). The numbering begins with iii; the title page counts as page I and the copyright page as ii, but the number does not appear.
• Rest of the thesis or dissertation – the body of the thesis, including text, illustrations, appendices, and bibliography, use Arabic numerals (1, 2, 3, 4, etc.). Each page must be numbered. Try to avoid use of letter suffixes such as 10a, 10b. The numbering begins with 1 and runs consecutively to the end of the dissertation.

If footnotes are needed, they should be placed at the bottom of the page below a 1.5-inch underscore (starting at the left border). The first line of each footnote should be indented 0.5 inches and identified by a raised numeral corresponding to that used in the test. Footnotes should be numbered consecutively throughout each chapter. 

Reproduction of data

The data on which the thesis or dissertation is based should be made accessible to the reader in substantially complete form. Generally, this means that raw data should be reproduced in a convenient manner in one or more appendices to the main document and made available in the Carnegie Mellon repository, on a website that will be supported by the advisor or department, or an external repository related to the pertinent field. In the case of extensive data gathered from readily available published sources, specific detailed citations will suffice, provided that that data is included in an Appendix of the document pdf, if available, or otherwise a stable url is included. Deviations from a procedure of full disclosure must be specifically approved by the M.S. thesis advisor(s) or Ph.D. Dissertation Committee and explained fully in the thesis or dissertation.  

Reproduction of materials

All instruments, analytic procedures, apparatus, or other critical elements in the execution of the study should be described in detail. Apparatus normally should be described in an engineering drawing and by photograph. Instruments normally should be reproduced in full in pictures or drawings, unless they are easily available from other sources. Procedures of analysis should be specified fully either by citation or by detailed discussion in one or more appendices. Computer calculations that are essential to the central arguments of the research must be fully and clearly explained. If the computer programs which provide the basis for these calculations are originated by the student, the student is required to provide a program listing and minimal documentation on the program in the thesis or dissertation.

The program listing and documentation normally would be included in a separate appendix to the thesis or dissertation. However, in the case of extensive computer work considered by the student and his or her advisor to be too long to include in the thesis or dissertation, a presentation in the form of tables elucidating important components is acceptable. In this case, the student is advised to submit a separate internal report giving further details. Standard subroutines or packaged programs that are routinely included as software support to a computer installation and which can be readily obtained are exempted from this requirement, but these should be clearly cited, and the source of these programs made apparent in the thesis. 

Citations of the professional literature should be standardized throughout the thesis or dissertation. The form of citation should be consistent with the form used in a standard professional journal of the candidates' field. The Harvard Citation Style is used commonly in engineering and science. The following journals are recommended as samples in each field of engineering:

• Biomedical Engineering— Annals of Biomedical Engineering, Journal of Biomechanical Engineering
• Chemical Engineering — Langmuir, Optimization and Engineering
• Civil and Environmental Engineering — ASCE journals, e.g., Journal of Environmental Engineering, Journal of Transportation Engineering.
• Electrical and Computer Engineering — Proceedings of the IEEE.
• Engineering and Public Policy — Science ,  Proceedings of the IEEE.
• Mechanical Engineering — Transactions of the American Society of Mechanical Engineers.
• Materials Science and Engineering — Metallurgical Transactions.

Additional guidance

Refer to the ProQuest document “ Guide 1: Preparing Your Manuscript for Submission to ProQuest ” for margins, paper type, line spacing, and additional formatting guidelines that have not been noted above.

Example of Thesis Format

Engineering and Public Policy

College of engineering.

Thesis Formatting and Completion Guidelines

Dissertation Formatting Guidelines and Miscellaneous Completion Information

Contact the department's Graduate Program Administrator (Vicki Finney) for questions on this information.

I. Defense announcement posting (required)

College rules state that a Notice of Final Public Oral Examination (defense announcement) must be posted no less than two weeks before a defense takes place. Students are required to complete the defense announcement template found on the EPP doctoral studies Canvas site and submit it electronically to Vicki Finney ([email protected]) for posting.  

II. Formatting your dissertation

Students will need to adhere to the formatting guidelines found on the Thesis and Dissertation Standards page of the College of Engineering website.

These College guidelines provide templates for the Thesis Title Page, Thesis Signature Page, Copyright Page, Thesis Committee Signature Page, and a Thesis Submission Checklist.  You are only responsible for preparing your Thesis Title Page as indicated in the guidelines, and completing the Thesis Submission Checklist when you are ready to submit your final thesis.   Vicki will prepare the Thesis Signature Page and Thesis Committee Page before your defense and email a copy to you and your thesis chair.  To prepare the Thesis Signature Page, you will need to send her your final thesis title and indicate how your name will appear on your title page. 

Some key items when formatting your thesis:

• Acknowledge your financial support on the Acknowledgements page.
• Only list your thesis committee members on the Acknowledgements page.
• If copywriting your thesis, include a separate page with the copyright information. As noted above, a copyright template is provided in the College guidelines. If you do not wish to copyright, you must indicate this on the Thesis Submission Checklist.  Co pyright and publishing options information is provided in Items III and IV below and also in the College guidelines.
• On your Title page use only the date (date = month & year) that your PhD will be officially conferred.  For example, May 2021 graduates will use “May, 2021” even if the final thesis was submitted early in the semester. The only official months that Carnegie Mellon confers degrees are May, August, and December, thus only these months may be used on the Title Page.
• Once you have formatted your thesis according to the College guidelines, it is recommended that you email it (or the first few pages) to Vicki to review formatting.

III. Submittal of your final dissertation to the department and required documentation

Once the defense is over, the following documentation must be submitted electronically on or before the thesis submission deadline as follows:

• the pdf file title should be as follows:  LastNameDepartmentYear (e.g., SmithEPP2021)
• the dissertation should begin with your title page
• A pdf of the final thesis to the ProQuest ETD Administrator Repository .  When you click on the ProQuest link, follow the instructions and complete the information as requested.   Choose "Carnegie Mellon College of Engineering" for your submission.  Once in ProQuest you will need to indicate that you also wish to deposit your dissertation with the CMU Institutional Repository (KiltHub).  In both repositories you will need to choose publishing options.  Traditional publishing is free in both repositories.  Open Access is free in KiltHub, but there is a $95 fee in ProQuest.  Note, you do not need to choose the same publishing option in both repositories.
• The Thesis Submission Checklist emailed to Vicki.  As noted above, the checklist is found on the College guidelines (Thesis and Dissertation Standards)  webpage.

IV.  Information on Copyrighting, ProQuest and Kilthub Repositories, and Publishing Options

The CMU Libraries website provides excellent guidance on copyrighting your thesis  (determining copyright ownership, copyright permissions, registering copyright).  If you have specific questions on copyright, placing embargoes on your work, the benefits of depositing your thesis into both the ProQuest Repository and the CMU Institutional Repository (KiltHub) , contact our CMU library liaison,  Jessica Benner . 

V. Students in joint degree programs 

Students pursuing a joint degree with EPP within the College of Engineering, whose home department is EPP, should follow EPP’s completion guidelines and deadlines.

Students pursuing a joint degree with EPP within the College of Engineering, whose home department is not EPP should follow their home department’s completion guidelines and deadlines.  However, EPP requires the following:   (a) a copy of your signed thesis Committee Sheet; (b) a copy of your thesis Signature page; and, (c) a pdf of your final thesis emailed to Vicki.

Students pursuing a joint degree with EPP outside of the College of Engineering, should meet with Vicki prior to thesis submission.

VI.  Dissertation Submissio n Dates

Final dissertation submission deadlines, as noted in the College guidelines, are:

May graduation:  The final (electronic) dissertation, Thesis Checklist, and Survey of Earned Doctorates Completion Certificate, must be submitted to the department's graduate program administrator (Vicki) 10 business days before the final grade deadline; and to the Dean's Office on the final grade deadline.

August and December graduation:  The documents noted above must be submitted to the department administrator two (2) days before the final grade deadline; and to the Dean's Office on the final grade deadline.

Specific deadlines for EPP's thesis submission are found here .

VII. Migrate Your CMU Computing Resources

Soon after graduation, you’ll lose access to your CMU accounts. Migrate your email, cloud storage files, personal websites, and other work so that you have what you need to succeed in your next steps.

Before You Graduate:

• Transfer Files to a Personal Account
• Uninstall CMU-Licensed Software
• Update Personal Account Emails
• Set Up Your Alumni Email
• Export Your Google Data

To learn more about how to migrate your computing resources, review  Move your Student Services. If you have questions or need assistance, contact the Computing Services Help Center at  [email protected]  or 412-268-4357 (HELP).

VIII.  Dissertation Copies & Binding

Once the Dean’s Office has approved your final thesis, Vicki will inform you via email and include a copy of the dean-signed Committee and Signature pages for your records. If you would like to receive a bound copy of your thesis, the department will provide you with one. You will need to provide an unbound, double-sided copy of your thesis. So that there is no expense to you, you may print the copy on a department printer. See Vicki to obtain the high-quality paper needed for printing. If you prefer, we can handle the printing for you in the department.  Inform Vicki of your preference.

The copy will be sent for binding, and when returned, we will contact you for a shipping address.  Please note that we can only ship within the U.S. The department will only provide a copy for your primary thesis advisor upon request. If your advisor wishes to receive a bound copy, please let Vicki know. Additional bound copies can be purchased through ProQuest ETD.  

Page sections

V. Students in joint degree programs

VII.  Migrate Your CMU Computing Resources

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Thesis/Dissertation Submission Procedures

Students in the Master of Science (MS) and doctoral (PhD) programs at Carnegie Mellon Architecture must complete the following steps to be certified for their degree:

• Download and complete the Dissertation and Thesis Repository Submission Checklist .
• Name the completed checklist document using the following naming convention: AndrewID_Checklist_Department_Year
• Name the approved thesis/dissertation document using the following naming convention: AndrewID_DegreeType_Department_Year
• Email these two files as PDF attachments to Alison Petrucci , Graduate Academic Advisor; your thesis/dissertation advisor; and your program’s Track Chair (if that person is different than your advisor) prior to the Final Grades deadline .

The thesis/dissertation document should include the committee members’ signatures page.

Download the Thesis/Dissertation Signatures Page Template

Please contact your track chair or the graduate coordinator with any questions.

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Engineering Resources: Theses and Dissertations

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About Theses and Dissertations

A dissertation or thesis is a document submitted in support of candidature for a degree or professional qualification presenting the author's research and findings.  (International Standard ISO 7144: Documentation — Presentation of theses and similar documents ).

For most universities in the U.S., dissertation is the term for the required submission for the PhD, and thesis refers only to the master's degree requirement.

Other Universities

T he best source to find theses is ProQuest Dissertations & Thesis Global .  Policies regarding theses and dissertation collections largely vary between universities.  So check the library website of the university of interest.

Carnegie Mellon University

Carnegie Mellon theses are now ONLINE and can be searched through the ProQuest database Dissertations & Theses @ Carnegie Mellon University that enables access to citations and abstracts of all dissertations and theses, as well as the fulltext in PDF format.  Scroll down and select Dissertations & Theses, then do a regular search. Print versions are also available in the libraries collection.

The Carnegie Mellon Library catalog , uses the term THESIS to denote both masters' theses and dissertations.  However, the number of master's theses is limited.  Within the libraries, theses are located in designated areas and are shelved in alphabetical order by the author's last name.  The catalog treats theses and dissertations like books and they can be borrowed as such.  Theses may be in print, microfiche, or microform.

• In the catalog use the Advanced Search :  search by author, title, or keyword limiting to type THESIS.
• For a list of theses from a specific department, use Advanced Search to combine a keyword search for the name of the department with location THESES.  E.g., search for "Dept. of Computer Science" with THESES as the location.
• For a reasonably complete list of theses at Carnegie Mellon, use Advanced Search to search Carnegie Mellon University Dissertations in the Subject line.  

Other Countries

Center for Research Libraries:  Foreign Doctoral Dissertations CRL has more than 800,000 cataloged foreign doctoral dissertations from more than 90 countries and over 1200 institutions.

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Language Technologies Institute

School of computer science.

Master of Language Technologies

The Master of Language Technologies (MLT) program is a two-year, research-focused Master's program intended to prepare students for a Ph.D. program, whether it be at the LTI or elsewhere.

Requirements

The MLT program lasts two years (24 months), and students must complete two summers of research. Students should usually expect to graduate in August of their second year.

MLT students take 120 or more course units (about 10 courses), at least 72 of which are LTI courses, and 24 of which are School of Computer Science (SCS) courses. Most of these are 12-unit courses, although lab courses are typically 6 units. Our courses generally assume knowledge of programming and data structures. The remaining units may also be taken from the LTI, or with approval from the faculty advisor, any other senior- or graduate-level course offered at CMU or Pitt.

Directed research is another integral part of the MLT program; MLT students carry out directed research during their studies, with guidance from their faculty advisors.

Students may also choose to complete an optional MLT thesis. Guidelines can be found in the MLT Handbook.

Here's an example of how your two years in the MLT program may break down.

Carnegie Mellon's School of Computer Science has a centralized  online application process . Applications and all supporting documentation for fall admission to any of the LTI's graduate programs must be received by the application deadline. Incomplete applications will not be considered. The application period for Fall 2024 is now closed. Information about the Fall 2025 admissions cycle will be available in summer 2024.

*Please note, we no longer require mailed, hard versions of transcripts or test scores at the time of application. Do not mail anything to the admissions office. If you are accepted to a program, you will be given instruction to then mail your materials.

Application Deadlines

Fee Waivers

Fee waivers may be available in cases of financial hardship, or for participants in select "pipeline" programs. For more information, please refer to the  School of Computer Science Fee Waiver page .

The School of Computer Science requires the following for all graduate program applications:

• GRE scores: GREs are now optional, but if you want to submit GRE scores: These must be less than five years old. A GRE subject test in science, engineering, computer science, math, etc. is not required, but you may complete one and submit the scores if you wish. Our Institution Code is 2074; Department Code is 0402.
• TOEFL scores: Required if English is not your native language. No exceptions. These scores may be more than two years old if you have pursued or are pursuing a bachelor's or graduate degree in the United States. (While the TOEFL is preferred, the IELTS test may also be submitted.) Successful applicants will have a minimum TOEFL score of 100. Our Institution Code is 4256; the Department Code is 78.
• Official transcripts from each university you have attended, regardless of whether you received your degree there.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation.

For more details on these requirements, please see the   SCS Master's Admissions page .

In addition to the SCS guidelines, the LTI requires:

• A short (1-3 minute) video of yourself. Tell us about you and why you want to come to CMU. This is not a required part of the application process, but it's strongly suggested.
• Any outside funding you are receiving must be accompanied by an official award letter.

No incomplete applications will be eligible for consideration.

For specific application/admissions questions, please contact  Kate Schaich .

Tuition Rates

Tuition is set by the School of Computer Science and can vary by year. Current tuition rates can be found on the  Graduate Tuition  section of the  Student Financial Services website .

Financial Aid Resources

Research Assistant-ships are occasionally offered by research advisors to current MLT students. These are not guaranteed and vary from semester-to-semester. They fluctuate and are dependent on the funding source, research advisor and MLT student.

Student Financial Services  has additional information on financial aid and billing / payments. They have a detailed outline of how to apply for financial aid on the  Graduate Financial Aid Process  page.

Enrollment & Finances  has additional resource links to assist with financial aid and tuition payments.

Graduate Education  –  Financial Assistance  provides resources for current students regarding emergency loans and conference travel grants.

Program Contact

For more information about the MLT program, contact Kate Schaich.

Kate Schaich

Program handbook.

Computer Science Thesis Oral

April 5, 2024 2:00pm — 4:00pm.

Location: In Person and Virtual - ET - Reddy Conference Room, Gates Hillman 4405 and Zoom

Speaker: MATT BUTROVICH , Ph.D. Candidate, Computer Science Department, Carnegie Mellon University https://mattbutrovi.ch/

On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments

The rise in computer storage and network performance means that disk I/O and network communication are often no longer bottlenecks in database management systems (DBMSs). Instead, the overheads associated with operating system (OS) services (e.g., system calls, thread scheduling, and data movement from kernel-space) limit query processing responsiveness. User-space applications can elide these overheads with a kernel-bypass design. However, extracting benefits from kernel-bypass frameworks is challenging, and the libraries are incompatible with standard deployment and debugging tools. 

This thesis presents an alternative in user-bypass: a design that extends OS behavior for DBMS-specific features, including observability, networking, and query execution. Historically, DBMS developers avoid kernel extensions for safety and security reasons, but recent improvements in OS extensibility present new opportunities. With user-bypass, developers write safe, event-driven programs to push DBMS logic into the kernel and avoid user-space overheads. There are two ways to to invoke user-bypass logic: (1) when a DBMS in user-space invokes these programs, user-bypass provides behavior similar to a new OS system call, albeit without kernel modifications. In contrast, (2) when an OS thread or interrupt triggers these programs in kernel-space, user-bypass inserts DBMS logic into the kernel stack.

First, we present a framework that employs user-bypass to collect training data for self-driving DBMSs efficiently. User-bypass programs reduce the number of round trips to kernel-space to retrieve performance counters and other system metrics. Next, we present a database proxy that applies user-bypass to support features like connection pooling and workload replication while reducing data copying and user-space thread scheduling. User-bypass programs embed DBMS network protocol logic in multiple layers of the OS network stack, applying DBMS proxy logic in a kernel-space fast path. Lastly, we present an embedded DBMS for future user-bypass applications. We discuss the design decisions, environment challenges, and performance characteristics of a DBMS that offers ACID transactions over multi-versioned data in kernel-space. We also explore applications of this user-bypass DBMS and compare them to modern user-space systems.

The techniques proposed in this thesis show user-bypass benefits across multiple DBMS design disciplines and provide a template for future DBMS and OS co-design.

Thesis Committee:

Andrew Pavlo (Chair) Jignesh M. Patel Justine Sherry Samuel Madden (Massachusetts Institute of Technology)  

In Person and Zoom Participation.  See announcement.

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