PhD Alumni

2019

Advisor: Bin Yu
Dissertation: Fast MCMC algorithms, Stability and DeepTune
Advisor: Michael Jordan and Jon McAuliffe
Dissertation: On the Local Sensitivity of M-Estimation: Bayesian and Frequentist Applications
Advisor: Bin Yu
Dissertation: Domain-inspired machine learning for hypothesis extraction in biological data
Advisor: Bin Yu
Dissertation: Interpretable deep learning for natural language processing
Advisor: Philip Stark
Dissertation: Classical Nonparametric Hypothesis Tests with Applications in Social Good
Advisor: Alan Hammond
Dissertation: Last Passage Percolation and the Slow Bond Problem

2018

Advisor: Michael Jordan and Ben Recht
Dissertation: Sets as Measures: Optimization and Machine Learning
Advisor: Yun S. Song
Dissertation: Statistical Methods and Analyses in Computational Genomics: Explorations of Eukaryotic Transcription
Advisor: Deborah Nolan
Dissertation: Behavioral Types: A New Perspective on Estimating Treatment Effects in Social Science Experiments with Binary Responses
Advisor: Peter Bickel
Dissertation: On Some Inference Problems for Networks
Advisor: Bin Yu & Aditya Guntuboyina
Dissertation: Information Theory, Dimension Reduction and Density Estimation
Advisor: Pieter Abeel and Peter Bartlett
Dissertation: Learning as a Sampling Problem
Advisor: Martin Wainwright & Adityanand Guntuboyina
Dissertation: A geometric perspective on some topics in statistical learning
Advisor: Michael I. Jordan and Ben Recht
Dissertation: Lyapunov Arguments in Optimization

2017

Advisor: Steve N. Evans
Dissertation: The Doob-Martin compactification of Markov chains of growing words
Advisor: Bin Yu & Jasjeet Sekhon
Dissertation: Predictive and Interpretable Text Machine Learning Models with Applications in Political Science
Advisor: David Aldous
Dissertation: Inference on Graphs: From Probability Methods to Deep Neural Networks
Advisor: Jim Pitman
Dissertation: Continuous paths in Brownian motion and related problems
Advisor: Yun S. Song
Dissertation: Demographic Inference from Large Samples: Theory and Methods
Advisor: Allan Sly
Dissertation: Phase Transitions of Random Constraints Satisfaction Problem

2016

Advisor: Bin Yu
Dissertation: Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics
Advisor: Allan Sly
Dissertation: Maximal Inequalities and Mixing Times
Advisor: Haiyan Huang, Elizabeth Purdom
Dissertation: Statistical Modeling and Analysis for Biomedical Applications
Advisor: Jon McAuliffe
Dissertation: Topics in large-scale statistical inference
Advisor: Benjamin Recht
Dissertation: Sparse Inverse Problems: The Mathematics of Precision Measurement
Advisor: Cari Kaufman and Deborah Nolan
Dissertation: Forecasting high-dimensional state-spaces in the presence of model error
Advisor: Bin Yu
Dissertation: Dictionary learning: analysis of spatial gene expression data and local identifiability theory

2015

Advisor: Allan Sly
Dissertation: Lipschitz Embeddings of Random Objects and Related Topics
Advisor: Yun Song
Dissertation: One and Two Locus Likelihoods Under Complex Demography
Advisor: Bin Yu
Dissertation: Theoretical Analysis and Efficient Algorithms for Crowdsourcing
Advisor: Peter J. Bickel
Dissertation: Some Inference Problems in High-Dimensional Linear Models
Advisor: Elchanan Mossel
Dissertation: Influences in Voting and Growing Networks
Advisor: Haiyan Huang, Peter Bickel
Dissertation: Problems in Network Modeling: Estimating Edges and Community Detection

2014

Advisor: Bickel and El Karoui
Dissertation: Topics in high dimensional statistics
Advisor: Michael I. Jordan
Dissertation: Clusters and Features from Combinatorial Stochastic Processes
Advisor: Martin Wainwright
Dissertation: High-dimensional statistics with systematic corruptions
Advisor: Elizabeth Purdom
Dissertation: Shrinkage of dispersion parameters in the double exponential family of distributions, with applications to genomic sequencing
Advisor: Mark van der Laan
Dissertation: Subsemble: A Flexible Subset Ensemble Prediction Method