Research overview

My research is in machine learning/data mining and natural language processing, 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.

A random sample of semi-recent publications

Michael L. Mortensen, Gaelen P. Adam, Thomas A. Trikalinos, Tim Kraska and Byron C. Wallace. An Exploration of Crowdsourcing Citation Screening for Systematic Reviews Research Synthesis Methods; 2017.

Ye Zhang, Matt Lease and Byron C. Wallace. Active Discriminative Text Representation Learning AAAI Conference on Artificial Intelligence (AAAI); 2017.

Iain Marshall, Anna Noel Storr, Joël Kuiper, James Thomas and Byron C. Wallace. Machine Learning for Identifying Randomized Controlled Trials: an evaluation and practitioner’s guide Research Synthesis Methods; 2018.

News

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.

07/02/2018 "Top Reviewer" @ ACL '18

I was recognized as a top reviewer for ACL 2018.

Support

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.