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.
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. ERASER: A Benchmark to Evaluate Rationalized NLP Models ACL 2020; 2020.
Iain Marshall, Blair T. Johnson, Zigeng Wang, Sanguthevar Rajasekaran and Byron C. Wallace. Semi-Automated Evidence Synthesis in Health Psychology: Current Methods and Future Prospects Health Psychology Review; 2020.
David Lowell, Brian Howard, Zachary C. Lipton. Byron C. Wallace. Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data EMNLP; 2021.
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