2020 to 2024 Ph.D. Dissertations

A Relevance-based Decision-making Model of Human Sparse, Overloaded, and Indirect Communication

Kaiwen Jiang; Ph.D., 2024

Causality in Point Processes

Ian McGovern; Ph.D., 2024

Change Point Detection for Dynamic Graphs and Dynamic Valued Networks Modeling

Yik Lun Kei; Ph.D., 2024

Energy-Based Model and its Applications

Yaxuan Zhu; Ph.D., 2024

Latent Neural Representations Analysis and Distillation for Transformers

Tianyang Zhao; Ph.D., 2024

Applications and Properties of Point Processes

Conor Joseph Kresin; PhD, 2023

Gene Selection Methods for Single-cell Sequencing Data Analysis

Kexin Li; Ph.D., 2023

Local Causal Structure Learning with the Coordinated Multi-Neighborhood Learning Algorithm

Stephen Smith; Ph.D., 2023

Revealed Preferences Models for Reconstructing and Analysing Partnerships in Two-Sided Matching Market

Shuchi Goyal; PhD, 2023

Selection into the Sample and into Treatment: Tools for Internally Valid Causal Inference

Adam Robert Rohde; PhD, 2023

Statistical Reconstruction of 20th Century Antarctic Sea Ice

Thomas Johannes Maierhofer; PhD, 2023

Statistical Simulation and Analysis of Single-cell RNA-seq Data

Tianyi Sun; Ph.D., 2023

Value Learning for Interactive Games, Embodied Artificial Intelligence, and Robotics

Yizhou Zhao; PhD, 2023

Bidirectional Mental State Alignment for Human-Machine Collaboration

Xiaofeng Gao; PhD, 2022

Capturing hidden covariates with linear factor models and other statistical methods in differential gene expression and expression quantitative trait locus studies

Heather J Zhou; Ph.D., 2022

Close the Loop of Neural Perception, Grammar Parsing, and Symbolic Reasoning

Qing Li; PhD, 2022

Constraint-Based Learning of Interventional Markov Equivalence Classes on High-Dimensional Data

Hao Wang; PhD, 2022

Deep Energy-Based Generative Modeling and Learning

Yifei Xu; PhD, 2022

Distance-oriented Space-filling Design Constructions for Gaussian Process Modeling

Yuhao Yin; PhD, 2022

Holistic Scene Understanding and Goal-directed Multi-agent Event Parsing

Yixin Chen; PhD, 2022

Learning Factor Analysis Structures: A Clique Search Method on Correlation Thresholded Graphs and a Piecewise Linear Spline Approach

Dale Kim; PhD, 2022

On Recursive and Hawkes Models for Forecasting the Spread of Epidemic Diseases

Andrew Kaplan; PhD, 2022

Plug-in Estimation Approaches to Causal Inference and Discovery

Gabriel Ruiz; PhD, 2022

Testing in Network Models with Community Structure

Linfan Zhang; PhD, 2022

The Imagined We: Shared Bayesian Theory of Mind for Modeling Communication

Stephanie Eu-Tien Stacy; PhD, 2022

A Hierarchical Computational Framework for Social Interaction Understanding: Interactiveness, Shared Attention, Gaze Communication and Triadic Belief Dynamics

Lifeng Fan; PhD, 2021

Advancements in the Design and Analysis of Order-of-Addition Experiments

Zachary Shane Stokes; PhD, 2021

Benchmarking Statistical and Machine-Learning Methods for Single-cell RNA Sequencing Data

Nan Xi; PhD, 2021

Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods

Yidan Sun; PhD, 2021

Causal Inference Outside of Randomized Trials with the Stability-Controlled Quasi-Experiment: Extensions and Considerations

David Wulf; PhD, 2021

Development and Benchmarking of Imputation Methods for Micriobome and Single-cell Sequencing Data

Ruochen Jiang; PhD, 2021

Effective Learning of Descriptive and Generator Models and Learning Representations for Grid Cells and V1 Cells

Ruiqi Gao; PhD, 2021

Evaluation and Construction of Space-Filling Designs Based on Stratification

Ye Tian; PhD, 2021

Falsification Testing for Causal Design Assumptions

Sydney Kahmann; PhD, 2021

Gaining Justified Human Trust by Improving Explainability in Vision and Language Reasoning Models

Arjun Reddy Akula; PhD, 2021

Graphon Estimation by Empirical Bayes Approach and Causal Discovery from Multiple Populations

Zhanhao Peng; PhD, 2021

Human-like Holistic 3D Scene Understanding

Siyuan Huang; PhD, 2021

Latent Space Energy-Based Model

Bo Pang; PhD, 2021

Learning Descriptive and Generative Models with Short-Run MCMC

Erik Lennart Nijkamp; PhD, 2021

Novel Approaches to Degeneracy in Network Models

Timothy Blackburn; PhD, 2021

Order-based Learning of Bayesian Networks: Regularized Cholesky Score and Distributed Data

Qiaoling Ye; PhD, 2021

Robot Learning from Interactions with Physics-realistic Environment: Constructing Big Task Platform for Training AI Agents

Xie Xu; PhD, 2021

Statistical Learning with Neural Networks Trained by Gradient Descent

Spencer Frei; PhD, 2021

Statistical Methods for the Social Sciences

Leonard Wainstein; PhD, 2021

Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions

Xinzhou Ge; PhD, 2021

Statistical criteria and procedures for controlling false positives with applications to biological and biomedical data analysis

Yiling Chen; PhD, 2021

Structural Learning of Gaussian DAGs from Network Data

Hangjian Li; PhD, 2021

Transparent and Robust Causal Inferences in the Social and Health Sciences

Carlos Leonardo Kulnig Cinelli; PhD, 2021

Advances in Non-Parametric Spatial Temporal Point Process Models with Applications to Crimes and Infectious Diseases

Junhyung Park; PhD, 2020

Joint and Post-Selection Confidence Sets for High-Dimensional Regression

Kun Zhou; PHD, 2020

Learning and Mapping Energy Functions of High-Dimensional Image Data

Mitchell Krupiarz Hill; PhD, 2020