cmu phd thesis

Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Neural processes underlying cognitive control during language production (unavailable) Tara Pirnia, 2024

The Neurodynamic Basis of Real World Face Perception Arish Alreja, 2024

Towards More Powerful Graph Representation Learning Lingxiao Zhao, 2024

Robust Machine Learning: Detection, Evaluation and Adaptation Under Distribution Shift Saurabh Garg, 2024

UNDERSTANDING, FORMALLY CHARACTERIZING, AND ROBUSTLY HANDLING REAL-WORLD DISTRIBUTION SHIFT Elan Rosenfeld, 2024

Representing Time: Towards Pragmatic Multivariate Time Series Modeling Cristian Ignacio Challu, 2024

Foundations of Multisensory Artificial Intelligence Paul Pu Liang, 2024

Advancing Model-Based Reinforcement Learning with Applications in Nuclear Fusion Ian Char, 2024

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

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, 2023

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

• Borrow & Request

Graduate Students & Postdocs

We offer a variety of services designed to make your learning, research, and teaching more effective., borrow from another institution.

If you need access to materials that CMU does not own, you may be able to request materials from other institutions using the ILLiad or E-Z Borrow Services.

• Learn more about Requesting from Other Libraries

Secure & Backup Your Data

Your data is the basis of your research. If lost, its recovery could be slow, costly, or impossible. Secure, store, and backup your data on a regular basis.  

• Learn more in the Data Security and Backup Research Guide

Dissertation and Thesis Resources

Most graduate students are required to submit copies of their theses and dissertations to the University Libraries.  The Libraries maintains  KiltHub , a free, open access repository of CMU research, and provides access to and assistance with  ProQuest Dissertations & Theses , a commercial repository and database of dissertations from institutions around the world.

Learn more in the Deposit your Dissertation or Thesis  Research Guide.

Scholarly Research

Whether you want to navigate the many options for publishing your research to optimize its accessibility and impact; identify when and where your research has been cited, as well as the scope of its reach; or uncover potential collaborators and the funding to support your project, the Libraries can support you at every stage of your research process.

Learn more about our support for scholarly research .

Instruction Assistance

Our teaching and learning services include in-person and online support to teach critical research and information literacy skills that are directly relevant to your course. We can support your students through the development of research assignments, online modules, and supplemental online learning tools.

Learn more about instruction assistance .

Workshops & Training

Have you ever wanted to learn a skill, but haven’t found the time? Interested to learn more about library resources, straight from a librarian? Workshops at the Libraries are designed to help attendees learn a new skill, deepen their understanding of a specific topic, or gain exposure to an unfamiliar technology, tool, or technique.

• Explore our latest workshops and training opportunities

Connect from Off Campus

Working remotely? Current CMU students, faculty, and staff have access to all electronic library materials from off campus.

• Learn about connecting from off campus

Video Tutorials

Video tutorials created by Libraries staff and faculty walk you through how to use our services, step by step.

• Watch video tutorials  

Carnegie Mellon University School of Computer Science

Doctoral programs.

In any of the Ph.D. programs across our seven departments, you'll be matched with an advisor based primarily on mutual research interests and begin a research project on day one. All our Ph.D. students receive full financial support while in good academic standing, which helps ensure freedom to explore regardless of funding hurdles. We also believe that it's vital for advisors and students to work as peers, and the inherent flexibility of our programs means students often work with more than one faculty member and many other students during their time in SCS.

Together, our research environment and interdisciplinary mindset produce graduates who emerge into the world ready to tackle its biggest problems.

• Doctoral Programs Home

Interested in Applying?

• Graduate Admissions Overview
• Frequently Asked Questions

Program Contact

Robert Frederking Associate Dean for Doctoral Programs

Explore Our Ph.D. Programs

Ray and stephanie lane computational biology department, computer science department, human-computer interaction institute.

Ph.D. in Human-Computer Interaction

Language Technologies Institute

Ph.D. in Language and Information Technologies

Machine Learning Department

Robotics institute.

Ph.D. in Robotics

Software and Societal Systems Department

Ph.D. in Societal Computing (SC) Ph.D. in Software Engineering (SE)

Dual Degree Ph.D. Programs

The carnegie mellon portugal program (cmu portugal), ph.d. in computer science/dual degree portugal, ph.d. in human-computer interaction/dual degree portugal, ph.d. in language and information technologies/dual degree portugal, ph.d. in robotics/dual degree portugal, ph.d. in societal computing/dual degree portugal, ph.d. in software engineering/dual degree portugal.

Engineering Resources: Theses and Dissertations

• Conferences
• Patents and Trademarks
• Technical Reports
• Theses and Dissertations
• Manufacturer Directories
• Standards This link opens in a new window

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.

• << Previous: Technical Reports
• Next: Manufacturer Directories >>
• Last Updated: Jan 9, 2024 3:05 PM
• URL: https://guides.library.cmu.edu/engresources

Popular Searches

• Master’s of AI Engineering
• Engineering Magazine
• graduate programs
• Manufacturing Futures Institute
• student organizations
• Rethink the Rink

Social Media

• @CMUEngineering
• CMUEngineering
• College of Engineering
• Graduate studies

Graduate programs

The College of Engineering provides a range of graduate programs that are both traditional and interdisciplinary. The programs are designed to foster a maker ecosystem where students and faculty from different disciplines collaborate to create solutions to complex problems in industry, government, and academia.

Graduates of these programs have excellent prospects in various fields, including industry, government, academia, and the corporate world. The college’s global alumni network is transforming the engineering profession by using an authentic interdisciplinary approach to problem-solving, which is the hallmark of Carnegie Mellon. Every day, our engineers are making a significant impact.

Biomedical Engineering

Chemical Engineering

Civil and Environmental Engineering

Electrical and Computer Engineering

Engineering and Public Policy

Information Networking Institute

Integrated Innovation Institute

Materials Science and Engineering

Mechanical Engineering

Carnegie Mellon University Africa

Carnegie Mellon Silicon Valley

Master’s of Artificial Intelligence​ Engineering

The​ Master of Science in Artificial Intelligence​ Engineering degrees (MS AIE) take AI and embed it into engineering frameworks, including engineering representations, applications within engineered systems, and discipline-specific interpretations of system outcomes. Within these frameworks, students will learn to invent, tune, and specialize AI algorithms and tools for engineering systems.

• Learn more about our MS AIE degrees

Dual-degree Ph.D. program with Howard University

Carnegie Mellon University’s College of Engineering and Howard University’s College of Engineering and Architecture jointly offer a dual Ph.D. program that grants doctoral degrees from both institutions. This unique program provides students with two academic advisors and access to an extensive range of courses and research facilities. Moreover, students benefit from being part of vibrant research communities in two major metropolitan areas: Pittsburgh, PA, and Washington, DC.

Learn more about the Howard Dual-degree program

The student perspective

Artificial Intelligence

Emotion detection system puts a smile on their face

Silicon Valley students use AI to develop an emotion detection system that can help job seekers improve performance and build confidence.

Faculty at Carnegie Mellon University Africa hosted workshops to give educators important classroom resources, while Mastercard Foundation Scholars visited secondary school students to build their programming and computer skills. The goal is to get students interested in ICT early on so they can ultimately pursue careers in the field.

Refugee outreach welcomes students to the ICT space

New course harnesses AI to kindle creativity

New course, AI for Humanities, offers a unique perspective on how AI can revolutionize our perception and interaction with creative expressions.

• Research paper

News & Events

Engineering students awarded Fulbright Scholarships

Six students and alumni from the College of Engineering will research and study abroad on Fulbright program scholarships.

Siddiqui receives Udall Scholarship

Aleena Siddiqui has received the Udall Scholar award, which recognizes future leaders in environmental, Tribal public policy, and health care fields.

Advanced Manufacturing

NASA mentor guides student’s career trajectory

Campus research experience, NASA internship, and advice from a mentor propel material science and engineering student, Lauren Fitzwater, to pursue a minor in additive manufacturing.

Amateur radio for aspiring professionals

Introduction to Amateur Radio course teaches history, culture, and science of radio technology.

Reeja Jayan designed a syllabus that incorporates Minecraft to give students a hands-on approach to learning about materials science concepts like chemical vapor deposition polymerization without setting foot in an actual lab.

Visit the virtual lab

Undergraduates present research at Meeting of the Minds 2024

Engineering undergraduate students had a wonderful showing at Meeting of the Minds, displaying posters, giving presentations, and demonstrating projects they have worked on this past academic year.

Presidential and graduate fellowships awarded for 2024-25

College of Engineering graduate students have been awarded fellowships for the 2024-25 academic year.

Behring Foundation gift supports international undergrads in tech

Two students have received the Behring Scholarship, a scholarship to support students from Brazil pursuing tech-related undergraduate degrees at Carnegie Mellon University.

Parry receives Goldwater Scholarship

Katherine Parry, a junior in electrical and computer engineering, has received the 2024 Barry Goldwater Scholarship to support her pursuit of a research career.

Six members from the College of Engineering were recognized at CMU’s annual Celebration of Education Awards.

Honoring our educators

Engineering faculty awarded professorships

Carnegie Mellon University has awarded professorships to five exceptional faculty members in the College of Engineering.

Machine learning and extended reality used to train welders

Researchers apply machine learning to a lightly-modified off-the-shelf welding helmet and torch integrated with a Meta Quest headset and controller to train welders.

Infrastructure as a social sensor

A recent study examines the relationship between having access to broadband internet and unemployment during the pandemic, treating infrastructure as a proxy for bias rather than access.

Energy & Environment

Doctoral researchers shine in 3MT championship

College of Engineering students explained complex research and its importance in under three minutes in the annual Three Minute Thesis competition.

One student, two continents

CMU-Africa student Farida Eleshin (MSIT ’24) is concluding her master’s program with a final semester in Pittsburgh, where she’s working on several research projects in the CHIMPS Lab that focus on privacy and security.

Language Technologies Institute

School of computer science.

The Ph.D. degree is the highest form of academic accomplishment. The Ph.D. dissertations below present some of the most advanced research being done at the time of their publication.

Present - 2022

2021 - 2017, 2016 - 2012 (this page), 2011 - 2007, 2006 - 2002, 2001 - 1997.

PhD of Architecture–Engineering–Construction Management

The PhD of Architecture–Engineering–Construction Management (PhD-AECM) focuses on the integration of design and technology, particularly advanced information systems, as a means of both improving building performance and enhancing environmental sustainability.

Joshua D. Lee

Associate Teaching Professor & AECM Track Chair

Program Overview

The PhD of Architecture–Engineering–Construction Management (PhD-AECM) Program is  jointly offered by the School of Architecture and the Department of Civil & Environmental Engineering . 

The PhD-AECM degree program is intended for practitioners, researchers and educators in engineering, architecture, construction management fields, and other professionals in the building industry who wish to be pioneers and advanced leaders in management technologies and their application to the built environment. The nominal length of the program is five years. Advanced standing during the admissions process can alter the expected time period.

Please feel free to contact Track Chair Joshua Lee with questions about the PhD-AECM program.

Program Curriculum

The first two years are intended for the tooling up of candidates to undertake research in an academic setting and their intended areas of research, by taking classes, participating in research projects, selecting their advisory committee members, completing their “game plan” and taking the qualifier exam. This exam is administered by the candidate’s advisory committee and has two parts: written and oral. Candidates take courses tailored to their direction of research and background knowledge, as determined through conversations with their principal advisor. See the curriculum below for a checklist of subjects to be covered. For additional and up-to-date information on these and other course offerings (course descriptions, schedules, instructors, etc.) please visit the University’s Schedule of Classes (SOC) webpage .

The third year of the PhD program is devoted to the development of the PhD dissertation proposal based on the work completed in the first two years. In the fourth year, the proposal is publicly defended and work on the dissertation commences. In the fifth year, the dissertation work is completed and publicly defended. Approvals of these milestones are judged by the candidate’s advisory committee and approved by the Carnegie Mellon Architecture Graduate Committee.

For details and regulations about the Game Plan, Qualification, Proposal and Dissertation processes, as well as additional details and regulations, please see the PhD Student Handbook .

The PhD-AECM curriculum is customized based on the experience and needs of individual students. Please contact Track Chair Joshua Lee with questions about the PhD curriculum.

View the PhD-AECM Curriculum

PhD-AECM Curriculum 2021 & Earlier

PhD-AECM Dissertation Topics

Current students.

Nester, Yael (Expected December 2026). Mass Customization of Affordable Modular Housing through AI-enabled design and manufacturing . Committee: Pingbo Tang (Chair), Joshua Lee.

Ken-Opurum, Waku. (Expected May 2026). Using AI to facilitate health care in the global south .

Murray, Joseph (Expected May 2026). Developing a Continuum of Knowledge Transfer: Architectural User Interfaces in Adaptable Buildings . Committee: Joshua Lee (Chair).

Afshar Bakeshloo, Tannaz (Expected December 2025). The Stakeholder Experience of Deconstructing Condemned Buildings in Pittsburgh . Committee: Joshua Lee (Chair).

Pathak, Nihar. (Expected May 2025). Lifecycle Analysis of Emergent Bio-based Materials for Hospitality Environments . Committee: Erica Cochran Hameen (Co-Chair), Joshua Lee (Co-Chair).

Priyadarshini, Shalini. (Expected May 2024).  Health, Safety and Comfort of On-site Workers in Construction . Committee: Erica Cochran Hameen (Chair), Burcu Akinci, John Mendeloff, Shailendra Singh.

Saadatifar, Sanaz. (Expected May 2024). Occupant-Centric Digital-Twin: An interactive real-time display, influencing human perception factor in thermal satisfaction decisions . Committee: Azadeh O. Sawyer (Chair), Daragh Byrne, Pingbo Tang.

Varma, Kushagra. (Expected May 2024). A 4-D interactive online tool to visualize urban building environmental assessment with an integrated retrofit recommendation generator . Committee: Erica Cochran Hameen (Chair), Kristen Kurland, Peter Scupelli, Ellyn A. Lester.

Completed Dissertations

Swarup, Lipika. (2023).  Combined Affects of Project Priority and Efficiency Factors on Project Outcomes in a Group of Multiple Projects . Committee: Erica Cochran Hameen (Chair), Matthew Mehalik, Peerasit Patanakul, Sinem Mollaoglu.

Ken-Opurum, Bobuchi. (2022).  Re-HOUSED Decision Support Toolkit: Promoting Flood and Heat Stress Resilience in Self-build Housing - A Coastal Nigeria Case Study . Committee: Erica Cochran Hameen (Chair), Joshua Lee, Jared Cohon.

Muñoz Muñoz, Alejandra. (2021).  A Tool for Sustainable Residential Water Management . Committee: Ramesh Krishnamurti (Chair), Ömer Akin, David Dzombak, Jared Cohon.

Ben-Alon, Rachel "Lola". (2020).  Natural Buildings: Integrating Earthen Building Materials and Methods Into Mainstream Construction . Committee: Vivian Loftness (Chair), Kent Harries, Erica Cochran Hameen.

Eldaher, Nizar. (2019).  Green Storm-water Infrastructure Strategy Generation and Assessment Tool For Site Scale and Urban Planning . Committee: Erica Cochran Hameen, Jared Cohon, Chris T. Hendrickson, Ömer Akin.

Awomolo, Olaitan. (2017). Exploring Communication in Multidisciplinary Building Design Teams . Committee: Ramesh Krishnamurti, Ömer Akin, Molly Wright Steenson, Erica Cochran Hameen.

Biswas, Tajin N. A. (2015).  Towards a Framework for Supporting Sustainable Building Design: A case study of two credits over evolving rating standards . Committee: Ramesh Krishnamurti, Burcu Akinci, Cliff Davidson.

Bello, Mustapha A. (2012).  Minimizing Impediments to Design for Construction Safety (DFCS) Implementation on Capital Projects .  Committee: Ömer Akin, Burcu Akinci, Chimay Anumba.

Program Faculty

The Architecture–Engineering–Construction Management faculty is comprised of experienced educators from the School of Architecture, the Department of Civil & Environmental Engineering and the Heinz College of Public Policy & Management. This interdisciplinary faculty provides a strong foundation for gaining both breadth and depth of knowledge in this multifaceted program.

For more information about CMU's PhD-AECM program, please contact Joshua Lee , PhD-AECM Track Chair.

Burcu Akinci

Affiliated Faculty, Professor & CEE Department Head

William J. Bates

Adjunct Faculty

Daragh Byrne

Associate Teaching Professor, Interim CD Track Chair

Erica Cochran Hameen

Associate Professor, DEI Director & DDes Track Chair

Donald Coffelt

Affiliated Faculty, AVP Facilities Management & CEE Adjunct Professor

Susan Finger

Affiliated Faculty, CEE Professor & IDeATe Associate Dean

Najeeb Hameen

Kristen Kurland

Teaching Professor

T. David Fitz-Gibbon Assistant Professor of Architecture & Regenerative Structures Laboratory Director

Destenie Nock

Affiliated Faculty & CEE Assistant Professor

Stephen Quick

Azadeh O. Sawyer

Assistant Professor in Building Technology & BPD Track Chair

Nathan Sawyer

Special Faculty & Facilities Director

Pingbo Tang

Affiliated Faculty & CEE Associate Professor

Admissions Resources

Are you a current student looking for resources? Handbooks, procedures and other information can be found on the Student Resources page .

University Policies

Doctoral student status .

Policy Statement

The university has a policy that covers: time limits on doctoral student status, a definition of All But Dissertation status, a definition of In Residence and In Absentia status for doctoral students and the tuition and fees charged for students In Residence and students In Absentia . These rules apply to all doctoral students. Students who began their doctoral studies prior to the date of this policy’s revision may follow time-to-degree requirements from the previous policy, but all other rules set forth in this policy will apply immediately to all doctoral students.

Time to Degree

Students will complete all requirements for the Ph.D. degree within a maximum of ten years from original matriculation as a doctoral student, or less if required by a more restrictive department or college policy. Once this time-to-degree limit has lapsed, the person may resume work towards a doctoral degree only if newly admitted to a currently offered doctoral degree program under criteria determined by that program. Under extraordinary circumstances, such as leave of absence, military or public service, family or parental leave, or temporary disability, a school or college may, upon the relevant department's recommendation and with the written approval of the dean, defer the lapse of All But Dissertation status for a period commensurate with the duration of that interruption. Students, who are pursuing the Ph.D. degree as part-time students for all semesters of their program, as approved by their program, may also appeal to their program or department for extension of the time to degree limit.

All But Dissertation Status

All But Dissertation, ABD, status is intended for students whose only remaining requirements are the completion and defense of their dissertation. Once a student meets the departmental criteria [1] , All But Dissertation status must be approved by the department by submitting the appropriate form to [email protected] . 

In Residence Versus In Absentia

Once students achieve All But Dissertation status, they must choose whether to complete their dissertation In Residence or In Absentia . A doctoral student In Residence maintains student status and all consequent student privileges and continues to be actively engaged with the university. A doctoral student In Absentia status  is one who has left the university with the intent of completing their dissertation but is not actively engaged with the university and does not require university resources. When a student decides whether to pursue All But Dissertation In Residence or In Absentia, they must complete a Doctoral Student Status Agreement form, which is available through their academic department or on the HUB website. Once the agreement has been approved by the student’s department, the student may change their status between  In Residence  and  In Absentia multiple times with approval. A student In Residence or In Absentia must meet the specific criteria noted later in this policy. Students  In Absentia will not be verified by the university as an enrolled "student" for immigration or loan purposes. All But Dissertation students in J1 or F1 immigration status must continue to follow the Department of Homeland Security (DHS) regulations [2] .

All But Dissertation Students In Residence

All But Dissertation students In Residence receiving any financial support (such as tuition, stipend, fees or health insurance, whether full or partial), tied to activities that are integral to their doctoral program that is  paid by or administered by the university must be enrolled for at least thirty-six units to maintain full time student status and all subsequent student privileges. Exceptions to the thirty-six unit enrollment requirement may be granted by the Provost [3] . All But Dissertation students In Residence who are not receiving any financial support (such as tuition, stipend, fees or health insurance, whether full or partial), from the university tied to activities that are integral to their doctoral program should consult their college policy to determine the number of units for which they must be registered in order to maintain full-time student status and all subsequent privileges. All But Dissertation students In Residence who are pursuing their doctoral degree on a part time basis and are not receiving any financial support (such as tuition, stipend, fees or health insurance, whether full or partial), from the university tied to activities that are integral to their doctoral program should consult their college policy to determine the number of units they must be registered for in order to maintain part time student status and all subsequent privileges. Note that doctoral students must be a full time graduate student for at least one academic year or more if required by the student’s home college. All But Dissertation students who are employed by the university in a capacity independent of their educational program and are pursuing a doctoral degree part time, may register for the number of units required by their department in order to remain in part time status so long as they are not receiving any financial support (such as tuition, stipend, fees or health insurance, whether full or partial), tied to activities that are integral to their doctoral program by their college, school or department. Questions about eligibility for tuition benefits should be referred to the Benefits Department.

Final Semester Tuition for All But Dissertation Students In Residence

Students who are supported by the university must be registered for thirty-six units for the entirety of their final semester and will be assessed their college’s full-time tuition.

Full-Time Students

If a student completes all Ph.D. degree requirements and is certified by:

• September 30th (in the fall), or February 28th (in the spring), tuition will be adjusted to $0; however, they will remain enrolled for thirty-six units for the semester.
• October 31st (in the fall), or March 31st (in the spring), tuition will be adjusted to 50% of the full-time tuition; however, they will remain enrolled for thirty-six units for the semester.
• After October 31st (in the fall), or after March 31st (in the spring), but BEFORE the first day of the next semester, tuition will not be adjusted, and they will remain enrolled for thirty-six units for the semester.
• Fees will not be adjusted after the semester course add deadline.
• Tuition will not be assessed in the summer, except for students who return from All But Dissertation In Absentia status and who are registered for thirty-six units. For those students who are certified by June 15th tuition will be adjusted to $0; for those who are certified by July 15th tuition will be adjusted to 50% of the full-time tuition. For those who are certified after July 15th but BEFORE the first day of the next semester, tuition will not be adjusted, and they will remain enrolled for thirty-six units for the semester.

Part-time Students

Students registered for fewer than thirty-six units are not eligible for a tuition adjustment, regardless of their certification date. Fees will not be adjusted.

All But Dissertation Students In Absentia

An All But Dissertation doctoral student may, upon departmental approval, be regarded as In Absentia when, and so long as, the following three conditions apply:

• The student has been enrolled as a full-time graduate student at Carnegie Mellon University for at least one academic year or more if required by the student's home college. Part-time graduate enrollment may, at the department's discretion, be counted pro-rata toward this requirement.
• The student does not receive any financial support (such as tuition, stipend, fees or health insurance) tied to activities that are integral to their doctoral program that is paid by or administered by the university.
• The student does not require substantial use of university resources. Departmental approval of this condition shall be subject to guidelines established by the school or college.

According to university guidelines, students In Absentia may [4] :

• Use University Libraries
• Use the university stores.
• Use computing facilities only for department communications and for dissertation text preparation.
• Enter university buildings for faculty/student consultations.
• Be eligible for student health insurance as determined on a case by case basis [5] .
• Use the Career and Professional Development Center.
• Become university employees.
• Be employed with a graduate student stipend [6] .
• Maintain legal F1 or J1 student status.
• Use University Health Services [5] .
• Buy parking permits [7] .
• Use athletic facilities [7] .
• Reside in university housing.

Employment of All But Dissertation Students In Absentia

As noted above, All But Dissertation students In Absentia are extended only minimum access to university resources. The student does not receive any financial support (such as tuition, stipend, fees, or health insurance, whether full or partial), tied to activities that are integral to their doctoral program paid by or administered by the university. An All But Dissertation student In Absentia cannot be hired for work by Carnegie Mellon University directly related to completing their dissertation and/or make substantial use of resources for work toward the doctorate as noted above [4] . In order to be in compliance with these policies, the university's employment policies and the Internal Revenue Service, an All But Dissertation student In Absentia may only be hired for university employment through the appropriate employment process. Questions should be referred to Human Resources.

Tuition and Fee Effects of In Absentia Student Status Including the Final Semester

While an All But Dissertation student is In Absentia , no tuition will be assessed. The student will, however, be responsible for all applicable fees.

An All But Dissertation student who is In Absentia , who returns to defend their dissertation has several options:

• A student who receives support (such as tuition, stipend, fees or health insurance, whether full or partial) paid for or administered by the university, must follow the policy for Final Semester Tuition for All But Dissertation Students  In Residence  (see above) and is eligible for the tuition to be pro-rated as identified in the schedule.
• A student returns to the university solely for the purpose of the defense and is In Residence for 10 or fewer days would pay the technology fee in addition to the tuition.
• A student returns to the university solely for the purpose of the defense and is In Residence for more than 10 days would pay the technology, transportation, and student activities fee in addition to the tuition.
• A student who is  In Absentia  may petition their program to complete and defend their dissertation without a return to campus. Such a student will be not be charged tuition but would be charged a Dissertation Completion Fee and technology fee. 

[1] General examples of having met All But Dissertation requirements may include completing all courses and passing qualifying exams; completing all courses and acceptance of dissertation proposal; etc. as defined by program, department or school. [2] The intent of the DHS regulations is that the student continues to pursue completion of the degree on a full-time basis under the jurisdiction of the university that will award the degree. International students who enter All But Dissertation status must remain In Residence and be registered full-time as defined in this policy to preserve F1 or J1 immigration status while they complete their degree. Questions about All But Dissertation status and immigration requirements should be addressed to the Office of International Education. [3] If granted exception results in the student’s enrollment being reduced to less than half time, tax consequences may apply. [4] An All But Dissertation student In Absentia may be hired as an university employee without switching to active student status so long as the hiring department certifies that the student is not hired at Carnegie Mellon for work directly related to his/her dissertation and that the student does not inappropriately make substantial use of resources for work towards the doctorate as noted above.  As an employee, an individual would be eligible for benefits that apply to his/her status as an employee, not as a graduate student. [5] University Health Services is not available to students in In Absentia status, except in an emergency, or on a case by case basis. All inquiries may be directed to the Manager of Business Operations, University Health Services. [6] Graduate students are not considered employees of the university as their primary affiliation with the university is as a student. [7] An individual whose primary relationship with the university is as an employee and who as Ph.D. student moves to the status of ABD In Absentia will be eligible for benefits that apply to his/her status as an employee.

• Articles of Incorporation
• Bylaws of the University