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.
Silvio Amir, Glen Coppersmith, Paula Carvalho, Mário J. Silva and Byron C. Wallace. Quantifying Mental Health from Social Media with Neural User Embeddings Machine Learning in Health Care (MLHC); 2017.
Ye Zhang, Matt Lease and Byron C. Wallace. Active Discriminative Text Representation Learning AAAI Conference on Artificial Intelligence (AAAI); 2017.
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.
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)
02/05/2018 NSF CAREER
My NSF CAREER proposal, "Structured Scientific Evidence Extraction: Models and Corpora", has been selected for funding!
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.