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 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.
Ye Zhang and Byron C. Wallace. A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification arXiv; 2015.
Byron C Wallace, Michael J Paul and Noémie Elhadad. What Predicts Media Coverage of Health Science Articles? The International Workshop on the World Wide Web and Public Health Intelligence (W3PHI); 2015.
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.
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.