Bio
Hi, I am Han Wu, currently a fifth-year PhD student in Statistics at Stanford University. I am fortunate to be advised by Professor Stefan Wager. My research interests lie in causal inference and machine learning, especially in heterogeneous treatment effects estimation, regression discontinuity, adaptive experimentation and interference. Previously, I completed my undergraduate studies at the University of Michigan in 2018 where I obtained B.S. degrees in Honors mathematics and Honors statistics, with a minor in computer science. I did internships at Two Sigma in 2022, Facebook (now Meta) Core Data Science in 2021 and Microsoft in 2018. Here is a copy of my CV.
Research
Erik Sverdrup*, Han Wu*, Susan Athey, Stefan Wager
Kevin Han*, Shuangning Li*, Jialiang Mao*, Han Wu*
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023
Han Wu, Stefan Wager
ACM Conference on Economics and Computation (EC), 2022
Han Wu, Stefan Wager
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Han Wu*, Sarah Tan*, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, Eytan Bakshy
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022
Dean Eckles*, Nikolaos Ignatiadis*, Stefan Wager*, Han Wu*
Kevin Han*, Han Wu*
KDD 2023 Workshop - Causal Inference and Machine Learning in Practice
Projects
Kevin Han*, Han Wu*, Yuqi Jin*
CS 224N: Natural Language Processing with Deep Learning, Stanford University
Tiancheng Cai*, Kevin Han*, Han Wu*
CS 229: Machine Learning, Stanford University
Presentations
29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23), August 2023
Data Driven Seminar, Stanford University, March 2023
2022 ACM Conference on Economics and Computation (EC), Boulder, July 2022
2022 American Causal Inference Conference (ACIC), Berkeley, May 2022
Causal Inference Group, Stanford University, March 2022
2021 Conference on Digital Experimentation @ MIT (CODE@MIT), November 2021
2021 INFORMS Annual Meeting, October 2021
Teaching and Professional Service
Teaching Assistant:
MS&E 226: Fundamentals of Data Science (Winter 2023)
Stats 263/363: Design of Experiments (Autumn 2022)
Stats 361: Causal Inference (Spring 2022)
Stats 366: Modern Statistics for Modern Biology (Spring 2021)
Stats 305A: Applied Statistics I (Autumn 2021)
Stats 320: Machine Learning Methods for Neural Data Analysis (Winter 2021)
Stats 216: Introduction to Statistical Learning (Winter 2020)
Stats 202: Data Mining and Analysis (Autumn 2018, Autumn 2019)
Stats 200: Introduction to Statistical Inference (Winter 2019)
Stats 116: Theory of Probability (Summer 2019, Spring 2020, Summer 2020)
Stats 100: Mathematics of Sports (Winter 2022)
Professional Service:
Reviewer for ICLR 2024, ICML 2022, 2023, NeurIPS 2022, 2023, KDD 2023, UAI 2023