I am currently a 2nd year machine learning Ph.D. student at Columbia University advised by Richard Zemel (and also lucky to work closely with Hongseok Namkoong). I aim to create tools to facilitate responsible and reliable machine learning and AI deployments, especially in important domains like medicine and education. I am particularly interested in uncertainty quantification, LLMs, skiing, and baseball.
Email me.
PUBLICATIONS
PersonalLLM: Tailoring LLMs to Individual Preferences
Thomas P. Zollo*, Andrew Siah*, Naimeng Ye, Ang Li, Hongseok Namkoong
Under Submission 2024
Improving Predictor Reliability with Selective Recalibration
Thomas P. Zollo, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
TMLR 2024
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
ICLR 2024
Distribution-Free Statistical Dispersion Control for Societal Applications
Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard Zemel
NeurIPS 2023 (Spotlight)
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel
ICLR 2023
AWARDS
Jonathan L. Gross Prize for Academic Excellence
Columbia University Department of Computer Science
Honors the student who graduates at the top of their class with a track record of promising innovative contributions to computer science, May 2023