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.
Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg and Byron C. Wallace. Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? NAACL; 2021.
Anneliese Arno, James Thomas, Byron C. Wallace, Iain J. Marshall, Joanne E. McKenzie, and Julian H. Elliott. Accuracy and Efficiency of Machine Learning–Assisted Risk-of-Bias Assessments in “Real-World” Systematic Reviews Annals of Internal Medicine; 2022.
Ashwin Devaraj, William Berkeley Sheffield, Byron C Wallace and Junyi Jessy Li. Evaluating Factuality in Text Simplification ACL; 2022.
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
01/20/2021 Lutron Award
I received the 2021 Joel and Ruth Spira Excellence in Teaching Award for the Khoury College of Computer Sciences