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
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
Causal and Selective Inference in Complex Statistical Models
Candes/Wager (GSB)
Hastie
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
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
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
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)
New Methods for Variable Importance Testing with Applications to Genetic Studies
Candes
Sabatti, Tibshirani
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
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)
Health and loan default risk analytics: Prediction models and their evaluation
Lai
Walther, Lu (BDS)
A Modern Maximum Likelihood Theory for High-Dimensional Logistic Regression
Candes
Montanari, Johnstone
Detection and validation of genomic structural variation from DNA sequencing data
Wong
Owen, Tang
Learning to generate text, programs (and beyond) from weak supervision
Liang (CS)
Wong, Mackey, Manning (CS)
An approximation-based framework for post-selective inference
Taylor
Tibshirani, Sabatti