Graduate Students

• I consider myself to be unusually fortunate to have the opportunities to work with so many fantastic individuals. It gives me utmost pleasure seeing them doing extremely well in their careers, and lives in general.

Completed Ph.D. Theses

• Eric Gilleland (Ph.D., 2005; co-supervision with Douglas Nychka): Statistical Models for Quantifying the Spatial Distribution of Seasonally derived Ozone Standards; now a Project Scientist at National Center for Atmospheric Research (NCAR)
• Curtis Storlie (Ph.D., 2005; co-supervision with Jan Hannig): Tracking of Multiple Merging and Splitting Targets with Application to Convective Systems; now an Associate Professor at Mayo Clinic
• Kelly McConville (Ph.D., 2011; co-supervision with Jay Breidt): Improved Estimation for Complex Surveys Using Modern Regression Techniques; now a Senior Lecturer at Harvard University
• Ming Zhong (Ph.D., 2012; co-supervision with Alexander Aue): Break Point Estimation and Variable Selection in Quantile Regressions; now at Apple
• Raymond K. W. Wong (Ph.D., 2014): On some Complex and Massive Data Problems; now an Associate Professor at Texas A & M University
• Randy C. S. Lai (Ph.D., 2015): Generalized Fiducial Inference and its applications to High Dimensional and Massive Data Problems; now at Google
• Rex C. Y. Cheung (Ph.D., 2017; co-supervision with Alexander Aue): Statistical Machine Learning Applications in Time Series, Network, and Partition-wise Models; now an Assistant Professor of Decision Sciences at San Francisco State University
• Minjie Fan (Ph.D., 2017; co-supervision with Debashis Paul): Modeling Vectorial and Non-Gaussian Random Fields on a Sphere; now at Meta
• Qi Gao (Ph.D., 2017): Some Contributions to Statistical Signal Processing and Machine Learning; now at Faire
• Justin Wang (Ph.D., 2018): Statistical Machine Learning Approaches in Photographic and Social Science Applications; now at Amazon
• Chunzhe Zhang (Ph.D., 2018): Uncertainty Quantification and Sensitivity Analysis in Statistical Machine Learning; now at LinkedIn
• Suofei Wu (Ph.D., 2019): Some Contributions to Random Forests and High-dimensional Principal Component Regression; now at Adobe
• Seung Yong Hwang (Ph.D., 2020; co-supervision with Jie Peng): Nonlinear Statistical Methods and Applications to Biomedical Data; now at GRAIL
%now a postdoctoral researcher at Stanford University
• Amy Taeyen Kim (Ph.D., 2020; co-supervision with Debashis Paul): Modeling Data Observed on Spheres and Graphs; now a Statistics Unit-18 Lecturer at the University of California, Davis
• Yao Li (Ph.D., 2020; co-supervision with Cho-Jui Hsieh): On Robustness and Efficiency of Machine Learning Systems; now an Assistant Professor of Statistics at the University of North Carolina, Chapel Hill
• Franco Liang (Ph.D., 2020; co-supervision with Cho-Jui Hsieh): On Accuracy, Efficiency and Fairness in Classification; now at LinkedIn
• Yi Su (Ph.D., 2020): Statistical Inference for Network Data Problems; now at LinkedIn
• Tongyi Tang (Ph.D., 2021; co-supervision with Debashis Paul): Multiscale Statistical Analysis of Vector Fields on a Sphere with Applications to Geophysics; now at Meta
• Zhenyu Wei (Ph.D., 2021): Some Contributions to High-dimensional Statistical Machine Learning; now at Meta
• Cong Xu (Ph.D., 2021): Change Point Detection for Image, Graph and Network Data; now at Aurora
• Wei Du (Ph.D., 2024): Streamlined Generalized Fiducial Inference for Modern Statistical Problems; now at eBay
• Yi Han (Ph.D., 2024): Some Contributions to Uncertainty Quantification and Change Point Detection in Dynamic Systems; now at Samsung
• Yue Kang (Ph.D., 2024; co-supervision with Cho-Jui Hsieh): Explorations in Stochastic Bandit Problems: Theory and Applications; now at Mircosoft
• Jue Wang (Ph.D., 2024): Some Contributions to Uncertainty Quantification and Fairness in Statistical Machine Learning; now at LinkedIn
• Xiawei Wang (Ph.D., 2024; co-supervision with James Sharpnack): Statistical Innovations in Health and Data Security: Lung Cancer Diagnosis, Microbiome Community Detection, and Adversarial Attack Analysis; now at Microsoft

Completed Master Theses

• Stephan D. Whitaker (Master, 2003): A Comparison of Estimation Techniques for Mixtures of Normal Densities
• Andrea Nibbe (Master, 2004): Improved Nonparametric Radial Basis Function Regression using Genetic Algorithms and Model Combination
• Troy Orwan (Master, 2004): Simultaneous Variable Selection and Outlier Identification using the Minimum Description Length Principle
• Courtney A. Sykes (Master, 2006; co-supervision with Hari Iyer): Bathymetry: Estimation of Coastal Water Depths and Expression of Uncertainty using the NIST Standardized Approach
• Stuart Kilzer (Master, 2008): Estimating the Position of a Mobile using Least Squares and Smoothing Techniques

In Progress

• Wancheng Cai (Ph.D.)
• Yishan Huang (Ph.D., co-supervision with Alexander Aue)
• Qiqi Liu (Ph.D.)
• Zhongxuan Liu (Ph.D.)
• Zijie Tian (Ph.D.)
• Yi-Syuan Yen (Ph.D.)