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, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace and Kush Varzney. Biomedical Interpretable Entity Representations ACL 2021 (Findings); 2021.
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. Learning to Faithfully Rationalize by Construction ACL 2020; 2020.
Stefan Olafsson, Byron C. Wallace, and Timothy Bickmore. Towards a Computational Framework for Automating Substance Use Counseling with Virtual Agents Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS); 2020.
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
08/15/2019 NIH/NLM R01 Renewed
The NIH has renewed the R01 grant that supports our work on RobotReviewer and related methods!
06/24/2019 NSF Grant
The NSF has awarded Jan-Willem van de Meent and I a grant to study disentangled representations for text!