Bio
I am a final year PhD student in MS&E at Stanford, with PhD Minor in Statistics. I am fortunate to be advised by Markus Pelger, co-advised by Itai Ashlagi, and to have Kay Giesecke and Peter W. Glynn as committee members. I am a member of the Advanced Financial Technologies Lab, and I organize the lab’s Doctoral Seminar.
Research Brief
I develop statistical methods for inference in large dimensional data sets to make better decisions. My domain knowledge of healthcare and financial economics problems informs my methodological work.
In the first stream of my research, I identify a new class of multiple testing problems in panel data learning, which is broadly applicable. In a sequence of follow-up papers, I use this framework for latent graphs learning and change point detection in time-series.
In my second stream, I tailor machine learning methods and design experiments for healthcare applications. Specifically, I am focusing on driving down inefficiencies in deceased donor organ allocation with machine learning tools.
See more in my research tab.
Updates
[Oct 2023] Present panel inference in INFORMS Session SE73, October 15 (Sun) at Pheonix, AZ.
[July 2023] Change point detection accepted in ICML 2023 SPIGM and SCIS at Hawai’i.
[June 2023] Present panel inference in AMES at SEM Tsinghua University 清华大学经管学院.
[June 2023] Present panel inference in NASMES at UCLA.
[May 2023] Co-organized and presented at AI in Fintech Forum.
[Mar 2023] Present panel inference in 11th WCMF at UC Berkeley.
[Oct 2022] Working paper on Cadeveric Organ Allocation project selected as Spotlight at NeurIPS 2022 Learning from Time Series for Health.
[Oct 2022] Present the Cadeveric Organ Allocation project at INFORMS on Monday 11am see details here.
[August 2022] Present panel inference at NBER-NSF SBIES: Session on Mixtures and Panel.
[June 2022] Internship at Meta (previously Facebook), working on sentiment analysis and ads recommendation.
[May 2022] Present panel inference at California Econometrics Conference.
[April 2020] Advanced to PhD Candidacy.