# PhD Dissertations

## [All are .pdf files]

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**

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