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.

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

Ian J Saldanha, Christopher H Schmid, Joseph Lau, Kay Dickersin, Jesse A Berlin, Jens Jap, Bryant T Smith, Simona Carini, Wiley Chan, Berry De Bruijn, Byron C Wallace, Susan M Hutfless, Ida Sim, M Hassan Murad, Sandra A Walsh, Elizabeth J Whamond and Tianjing Li. Evaluating Data Abstraction Assistant, anovel software application for dataabstraction during systematic reviews:protocol for a randomized controlled trial Systematic Reviews; 2016.

Ryan Bridges, Jette Henderson, Joyce Ho, Byron C. Wallace and Joydeep Ghosh. Automated Verification of Phenotypes using PubMed Methods and Applications for Healthcare Analytics (MAHA) at ACM BCB; 2016.

Zhiguo Yu, Trevor Cohen, Todd R. Johnson, Byron C Wallace and Elmer Bernstam. Retrofitting Word Vectors of MeSH Terms to Improve Semantic Similarity Measures The International Workshop on Health Text Mining and Information Analysis (Co-Located with EMNLP 2016); 2016.


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.

06/08/2018 SRSM Early Career Award

I have been selected as the recipient of the 2018 Early Career Award, from the Society for Research Synthesis Methods.

04/09/2018 Talks at upcoming local events: NEML, ODSC & NECHS

I'm excited to be giving invited talks at the New England Machine Learning day and Data Science Conference & Expo (ODSC East). I'll also be speaking on a panel at NorthEast Computational Health Summit (NECHS)


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.