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 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

Stefano V. Albrecht, André M. S. Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres, Amir-massoud Farahmand, Mark Fox, Lutz Frommberger, Sam Ganzfried, Yolanda Gil, Sébastien Guillet, Lawrence E. Hunter, Arnav Jhala, Kristian Kersting, George Konidaris, Freddy Lecue, Sheila McIlraith, Sriraam Natarajan, Zeinab Noorian, David Poole, Rémi Ronfard, Alessandro Saffiotti, Arash Shaban-Nejad, Biplav Srivastava, Gerald Tesauro, Rosario Uceda-Sosa, Guy Van den Broeck, Martijn van Otterlo, Byron C. Wallace, Paul Weng, Jenna Wiens, Jie Zhang. Reports of the AAAI 2014 Conference Workshops Artificial Intelligence Magazine; 2015.

Jette Henderson, Ryan Bridges, Joyce C. Ho, Byron C. Wallace and Joydeep Ghosh. PheKnow–Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature AMIA Joint Summits on Translational Science; 2017.

Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi (Brian) Zhu and Iain J. Marshall. Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision Journal of Machine Learning Research (JMLR); 2016.


06/16/2017 Upcoming talks

I'll be giving a talk at Weill Cornell Medicine as part of their Machine Learning in Medicine series on 7/14. I'll also be giving a keynote at the 4th International Symposium on Systematic Review and Meta-Analysis of Laboratory Animal Studies in August.

03/31/2017 Distinguished Clinical Research Informatics Paper Award at AMIA Joint Summits

Our paper, "PheKnow–Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature" was awarded the 2017 Distinguished Clinical Research Informatics Paper Award @ the AMIA joint summits. Joint work with Jette Henderson, Ryan Bridges, Joyce Ho and Joydeep Ghosh.

03/08/2017 SDM panel

I'll be serving on a panel at the annual Statistics and Data Mining (SDM) conference on Data mining, clinical medicine, and medical research: The current state and future of the nexus

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!


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.