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.

More broadly, I am interested in core machine learning and natural language processing issues: e.g., structured and unstructured classiļ¬cation 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

Finale Doshi-Velez, Byron C Wallace and Ryan P Adams. Graph-Sparse LDA: A Topic Model with Structured Sparsity AAAI Conference on Artificial Intelligence (AAAI); 2015.

Ye Zhang and Byron C. Wallace. Active Discriminative Word Embedding Learning arXiv; 2016.

Jenna Wiens and Byron C. Wallace. Editorial: special issue on machine learning for health and medicine Machine Learning Journal; 2015.


09/13/2016 AHRQ grant funded

My AHRQ R03 grant, Hybrid Approaches to Optimizing Evidence Synthesis via Machine Learning and Crowdsourcing, has been selected for funding!

07/30/2016 Panelist @ IEEE ICHI

I'll be sitting on a panel on computational methods for evidence synthesis at IEEE ICHI 2016.

06/01/2016 Talk @ U Lisbon/INESC-ID

I'll be giving a talk at the University of Lisbon this June.

05/19/2016 NIH grant funded

Our NIH "Big Data to Knowledge" proposal, Crowdsourcing Mark-up of the Medical Literature to Support Evidence-Based Medicine and Develop Automated Annotation Capabilities has been selected for funding! This is a collaborative effort with Ani Nenkova and Zachary Ives.


My work has been supported with grants from the National Institutes of Health, National Science Foundation, the Army Research Office, Seton hospital, Amazon and seed funds from Brown University.