PhD Advisees Lijia Wang (co-advised) Graduated USC 2023. Thesis: Statistical citation network analysis and asymmetric error controls. First position: Assistant Professor at City University of Hong Kong, School of Data Science Julian Aronowitz Graduated USC 2020. Thesis: Finite sample bounds in group sequential analysis via Stein's method. First position: Statistician at Palo Alto Networks Current position: Data Scientist at Google Xinrui He Graduated USC 2019. Thesis: Asymptotically optimal sequential multiple testing with (or without) prior information on the number of signals. First position: Statistician at Acumen LLC/The SPHERE Institute Current position: Data Scientist at Google Mike Hankin Graduated USC 2017. Thesis: Sequential testing of multiple hypotheses with FDR control. First position: Statistical Data Scientist at Google Current position: Principal Data Scientist and Statistician at VideoAmp Jinlin Song Graduated USC 2013. Thesis: Sequential testing of multiple hypotheses. First position: Statistician at The Analysis Group My/our math genealogy can be found here. Classes Recently Taught at UT Spring 2025 SDS 321 - Introduction to Probability and Statistics Covers fundamentals of probability, combinatorics, discrete and continuous random variables, jointly distributed random variables, and limit theorems. Using probability to introduce fundamentals of statistics, including Bayesian and classical inference. Fall 2024 SDS 431 - Probability and Statistical Inference Upper division introduction to probability and statistical inference for SDS majors. Examine events and random experiments; basic rules of probability; joint, conditional, and marginal probability and independence; discrete and continuous random variables; random sampling and estimation; large-sample theory results and central limit theorem-based inferential summaries; and maximum likelihood estimation. DSC 384 - Design Principles and Causal Inference Explore the field of big data and the rigors of determining applicable design structures from that data. Examine classic design structures, non-typical data structures and novel design processes, and causal inference, and explore data-based decision making. Fall 2023 SDS 431 - Probability and Statistical Inference Fall 2022 SDS 301 - Elementary Statistical Methods Lower division introductory statistics course for a broad audience. Covers the fundamental procedures for data organization and analysis. Subjects include frequency distributions, graphical presentation, sampling, experimental design, inference, and regression. back