Kelly W. Zhang
Postdoctoral Fellow at Columbia Business School
Assistant Professor/Lecturer at Imperial College London (fall 2024)
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kelly.w.zhang@columbia.edu
About
I'm a Postdoctoral Fellow at Columbia Business School in the Descision, Risk, and Optimization group, working with Daniel Russo and Hongseok Namkoong. My research interests lie at the intersection of adaptive experimentation, reinforcement learning, and statistical inference. In fall 2024, I will start as a Lecturer (Assistant Professor) at Imperial College London in the Mathematics Department (statistics section). I will also be a faculty member in the Imperial-X, an initiative driving innovation in machine learning, artificial intelligence and data science.
I completed my Ph.D. student in computer science at Harvard University in the Statistical Reinforcement Learning Lab. I was advised by Susan Murphy and Lucas Janson. Prior to that, I worked on natural language processing and deep learning with Sasha Rush, Sam Bowman, and Yann LeCun. I also previously interned at Apple's HealthAI team in Seattle, Facebook AI Research in New York, and at eBay New York on the homepage recommendations team.
News
- Summer 2023: I was interviewed as a part of the Harvard Women in Statistics and Data Science Series!
- August 2023: I will be speaking at the session on Integrating Algorithms and Analysis for Adaptively Randomized Experiments on August 6 at JSM in Toronto. The session is organized by John Langford, Sofia Villar, Aaditya Ramdas, Joseph Jay Williams, and Tong Li.
- May 2023: I defended my thesis on Statistical Inference for Adaptive Experimentation! My slides are here. My thesis committee members were Susan Murphy, Lucas Janson, Milind Tambe, and Jónas Oddur Jónasson.
- August 2022: I will speak at the Prediction and Inference in Statistical Machine Learning session organized by Tracy Ke at the 2022 Joint Statistical Meetings!!
- June 2022: I am very excited to be organizing an invited session at the 2022 Institute of Mathematical Statistics Annual Meeting on "Inference Methods for Adaptively Collected Data". The speakers will be Nathan Kallus, Koulik Khamaru, Evan Munro, and myself! Joseph Jay Williams and Nina Deliu will chair the session.
Honors
- Siebel Scholar, Class of 2023.
($35,000 award; given to 100 final year PhD candidates in engineering) - Hannan Graduate Student Travel Award, Institute of Mathematical Statistics, 2022
- NSF Graduate Research Fellowship, Awarded in 2019
- Computer Science Prize for Academic Excellence in the Honors Program, New York University, 2018