My name is Byron Wallace.
I'm the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University in the Khoury College of Computer Sciences. I work on NLP/ML + health. I am the Director of the BS in Data Science program.
My office is 2208 177 Huntington.
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.
Nikita Salkar, Thomas Trikalinos, Byron C. Wallace, and Ani Nenkova. Self-repetition in abstractive neural summarizers AACL; 2022.
Somin Wadhwa, Adit Krishnan, Runhui Wang, Byron C Wallace, and Luyang Kong. Learning from Natural Language Explanations for Generalizable Entity Matching EMNLP; 2024.
Somin Wadhwa, Silvio Amir and Byron C. Wallace. Revisiting Relation Extraction in the era of Large Language Models ACL; 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.
