Research overview

My research is in Natural Language Processing and Machine Learning, with an emphasis on applications in health.

Working in the domain of health naturally motivates the methodological problems that I have worked on. For example, these include: model interpretability; learning with limited supervision from diverse sources; human-in-the-loop/hybrid systems; and trustworthiness of model outputs. For more details, see recent publications here.

On the applications side, one thread of my research concerns developing language technologies to automate (or semi-automate) biomedical evidence synthesis. Here is an episode of the NLP highlights podcast in which I discuss this work, here is a (brief) talk I gave at SciNLP 2020, and here is an article written for a lay audience about the effort. Elsewhere, I have worked on models for processing notes in Electronic Health Records.

A random sample of recentish publications

Hye Sun Yun, Iain J. Marshall, Thomas Trikalinos and Byron C. Wallace. Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models Machine Learning for Healthcare (MLHC); 2024.

Hiba Ahsan, Denis Jered McInerney, Jisoo Kim, Christopher A Potter, Geoffrey Young, Silvio Amir, and Byron C Wallace. Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges ACM CHIL; 2024.

Koyena Pal, Jiuding Sun, Andrew Yuan, Byron C. Wallace and David Bau. Anticipating Subsequent Tokens from a Single Hidden State CoNLL; 2023.


01/16/2024 ICLR Spotlight

Our paper, Evaluating the Zero-shot Robustness of Instruction-tuned Language Models was accepted as Spotlight (top 5%) at ICLR 2024

10/01/2022 Helping radiologists navigate EHR

We have received a new R01 from the NIH/NLM to work on neural summarization methods to aid diagnosis (collaboration with Dr. Geoffrey Young at Brigham and Women's Hospital).

06/27/2022 NSF Medium

The NSF has awarded me and Zack Lipton a grant to work on summarization methods for consequential domains (like healthcare).

05/13/2022 ACL Outstanding Paper Award

Our paper, “Evaluating Factuality in Text Simplification", was selected as an Outstanding Paper at ACL 2022


My work has been supported with grants from the National Institutes of Health, National Science Foundation (including a CAREER grant), the Army Research Office, Seton hospital, Amazon and seed funds from Brown University.