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.
Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh and Byron C. Wallace. Intermediate Entity-based Sparse Interpretable Representation Learning BlackboxNLP workshop @ EMNLP; 2022.
Ashwin Devaraj, William Berkeley Sheffield, Byron C Wallace and Junyi Jessy Li. Evaluating Factuality in Text Simplification ACL; 2022.
Somin Wadhwa, Vivek Khetan, Silvio Amir and Byron C. Wallace. RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media EACL (Findings); 2023.
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
03/18/2021 Student Paper Award @ AMIA Summits
Our paper — led by my PhD student Ben Nye — received the best student-led paper award at the AMIA (Virtual) Summits