My research is in natural language processing and machine learning, with an emphasis on applications in health informatics.
For example, one of my major ongoing research aims concerns optimizing the processes of evidence-based medicine using novel natural language processing and machine learning methods. The aim is to reduce the (human) workload involved in conducting systematic reviews (i.e., making sense of the biomedical literature), so that we can realize the aim of evidence-based care in an era of information overload. An ongoing project in this direction is RobotReviewer. Here is an article written for a general audience about some of this work. I have also talked about this project at length on the NLP Highlights podcast, available here.
More broadly, I am interested in core machine learning and natural language processing issues: e.g., structured and unstructured classiﬁcation techniques; neural models; semi-supervised learning methods; learning from imbalanced data; and learning from alternative forms of supervision. I tend to be most excited by interdisciplinary research that motivates technical questions by way of interesting applications.
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions ACL 2020; 2020.
An T. Nguyen, Aditya Kharosekar, Saumyaa Krishnan, Siddhesh Krishnan, Elizabeth Tate, Byron C. Wallace and Matthew Lease. Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking ACM UIST; 2018.
Jette Henderson, Junyuan Ke, Joyce C. Ho, Joydeep Ghosh, and Byron C. Wallace. PIVET: A scaled phenotype evidence generation framework using online medical literature Journal of Medical Internet Research; 2018.
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!
05/20/2019 Early career spotlight @ IJCAI
I'll be giving an "early career spotlight" talk at IJCAI in August.
07/12/2018 ARO grant
The Army Research Office (ARO) has has selected my proposal, "Neural Models for Text: Improving Efficiency, Interpretability and Accuracy" for funding.