# PhD Dissertations

## [All are .pdf files]

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 (Unavailable)

Akash Umakantha, 2021

Structure and time course of neural population activity during learning (Unavailable)

Jay Hennig, 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 (unavailable)**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**

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

Yang Xu, 2013

Yang Xu, 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