CS Seminar: Kevin Michael Small, “Interactive Machine Learning in High-Expertise Domains”

Join the Department of Computer Science at a seminar on “Interactive Machine Learning in High-Expertise Domains,” presented by Kevin Michael Small on Monday, February 25, from 10:00–11:00 am in the Stuart Building, Room 113.

Machine-learning and data-mining methods have emerged as cornerstone technologies for transforming the deluge of data generated by modern society into actionable intelligence. For applications ranging from business intelligence to public policy to clinical guidelines, the overarching goal of “big data” analytics is to identify, analyze and summarize the available evidence to support decision makers. While ubiquitous computing has greatly simplified data collection, successful deployment of machine learning techniques is also generally predicated on obtaining sufficient quantities of human-supplied annotations. Accordingly, judicious use of human effort in these settings is crucial to building high-performance systems in a cost-effective manner.

Small will describe methods for reducing annotation costs and improving system performance via interactive learning protocols. Specifically, he will present models capable of exploiting domain-expert knowledge through the use of labeled features—both within the active-learning framework to explicitly reduce the need for labeled data during training and the more general setting of improving classifier performance in high-expertise domains. Furthermore, he will contextualize this work within the scientific systematic review process, highlighting the importance of interactive learning protocols in a particular scenario where information must be reliably extracted from multiple information sources, synthesized into a cohesive report and updated as new evidence is made available in the scientific literature. Small will demonstrate that we can partially automate many of the aspects of this important task, thus reducing the costs incurred when interacting with highly-trained experts.

Small received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (Cognitive Computation Group) in 2009. From 2009–2012, he held positions as a postdoctoral researcher at Tufts University (Machine Learning Group) and as a research scientist at Tufts Medical Center (Center for Evidence-based Medicine). He is presently conducting research within the Division of Program Coordination, Planning and Strategic Initiatives at the National Institutes of Health. Small’s primary research interests are in the areas of machine learning, data mining, natural language processing and artificial intelligence. Specifically, his research results concern using interactive learning protocols to improve the performance of machine learning algorithms while reducing sample complexity.