Alumni

Visit Stanford University Libraries to search for the PhD dissertations of our graduates.

Graduation Year
Student
Dissertation
Advisor
Committee Members

Some applications of graph limits in probability and statistics

Chatterjee

Montanari, Dembo

Some probabilistic aspects of Yang-Mills

Chatterjee

Diaconis, Dembo

Confident and reliable statistical predictions in changing environments

Duchi

Candes, Liang (CS)

Applications of Machine Learning to Some Statistical Inference Problems

Hastie

Tibshirani, Wager

Scalable Inference for Crossed Random Effects Models

Owen/Hastie

Hastie, Linderman, Athey (GSB)

Reliability and stability in statistical and machine learning problems

Duchi

Candes, Donoho, Rothenhaeusler

Nonparametric perspectives on empirical Bayes

Wager (GSB)

Candes, Tibshirani

A Neural Network with Feature Sparsity

Tibshirani

Duchi, Hastie

Causal and Selective Inference in Complex Statistical Models

Candes/Wager (GSB)

Hastie

Likelihood ratio testing in critically-spiked Wigner models

Johnstone

Owen, Dembo

Topics in multiple hypothesis testing and robustification of estimators through causal inference methods

Zou (BDS)

Candes, Owen

Phase Transitions of Disordered Systems and High Dimensional Inference

Dembo

Chatterjee, Montanari

Advances in multivariate statistics and its applications

Hastie

Tibshirani, Segal (UCSF)

Applied Statistical Methods for High-Dimensional Generalized Linear Models

Candes

Montanari, Taylor

Mallows permutation model: Sampling algorithms and probabilistic properties

Diaconis

Dembo, Siegmund

Topics in Exact Asymptotics for High-dimensional Regression

Montanari

Candes, Donoho

Minimax Regret Bounds for Stochastic Linear Bandit Algorithms

Bayati (GSB)

Lai, Candes

Model-Free Methods for Multiple Testing and Predictive Inference

Candes

Tibshirani, Owen

Advances in Contextual Multi-armed Bandits, Dynamic Treatment Strategies, and Type 1 Error Rate Guarantees in Confirmatory Adaptive Multi-arm Clinical Trials

Lai

Lavori (BDS), Lu (BDS), Palacios

Extending the Reach of the Lasso and Elastic Net Penalties: Methodology and Practice

Tibshirani

Art Owen, Guenther Walther

A Nonparametric Measure of Conditional Dependence

Chatterjee

Taylor, Bayati (GSB)

Causal Inference in Genetic Trio Studies

Candes

Tibshirani, Wager

Statistics and Uncertainty Quantification in Graphs and Networks with Biomedical Applications

Holmes

Friedman, Leskovec (CS)

Topics in Machine Learning for Causal Inference with Applications in Social Science

Athey (Econ)

Owen, Hastie

Statistical methods for adaptive data analysis

Taylor

Tibshirani, Romano

The Algebra and Machine Representation of Statistical Models

Candes

Hastie, Chambers, Musen (Med/BDS)

Large-scale and High-dimensional Statistical Learning: Methods and Algorithms

Hastie

Tibshirani, Rivas (BDS)

Making Causal Conclusions from Heterogeneous Data Sources

Owen/Baiocchi

Palacios

New Methods for Variable Importance Testing with Applications to Genetic Studies

Candes

Sabatti, Tibshirani

Sampling Methods for Perfect Bipartite Matchings

Diaconis

Chatterjee, Charikar (CS)

Asymptotic Theory for Large Random Matrices and its Applications

Dembo

Chatterjee, Montanari

Boosting Like Path Algorithms for L1 Regularized Infinite Dimensional Convex Neural Networks

Hastie

Taylor, Tibshirani

When do gradient methods work well in non-convex learning problems?

Duchi

Montanari, Candes

Topics in Selective Inference and its Application to Instrumental Variables

Taylor

Tibshirani, Lai

Modern Statistical Approaches for Randomized Experiments Under Interference

Ugander (MSE)

Walther, Palacios

Some hierarchical and group regularization methods and applications

Tibshirani

Taylor, Imbens (GSB)

Classification and testing under robust model assumptions

Tibshirani/Wong

Efron

Multiple Testing with Structure and Exploration

Sabatti

Candes, Montanari

A Computationally Conscious Search for Interactions

Tibshirani

Owen, Wager (GSB)

Health and loan default risk analytics: Prediction models and their evaluation

Lai

Walther, Lu (BDS)

Topics in Robust Mean Estimation

Diaconis

Chatterjee, Wong

Adapting local minimax theory to modern applications

Duchi

Johnstone, Candes

A Modern Maximum Likelihood Theory for High-Dimensional Logistic Regression

Candes

Montanari, Johnstone

Edgeworth approximations for spiked PCA models and applications

Johnstone

Owen, Donoho

Detection and validation of genomic structural variation from DNA sequencing data

Wong

Owen, Tang

Hypothesis Testing Using Multiple Data Splitting

Romano

Taylor, Tibshirani

Eigenvalues in multivariate random effects models

Montanari/Johnstone

Candes

Learning to generate text, programs (and beyond) from weak supervision

Liang (CS)

Wong, Mackey, Manning (CS)

Topics in statistical learning with a focus on large-scale data

Hastie

Efron, Taylor

An approximation-based framework for post-selective inference

Taylor

Tibshirani, Sabatti
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