CS Seminar: Bert Huang

Event Date: 
Tue, 2014-03-18 09:30 - 10:45

Dr. Bert Huang

University of Maryland

Tuesday, March 18, 2014 9:30AM-10:45AM 655 McBryde

STRUCTURED MACHINE LEARNING FOR THE

COMPLEX WORLD

Modern computing applications involve data measured from complex, real-world phenomena, whose complexity stems from massive networks of dynamic, multifaceted relationships among typed entities. For example, human social interaction, electronic communication, and municipal operations all exhibit this form of complexity. Effective data-driven analysis of these phenomena requires computational tools expressive enough to model their complex structure. However, such models overextend the theoretical foundation that supports core machine learning methods. In this talk, I will describe new algorithmic tools for efficient machine learning of complex structured models, applications of these tools, and theoretical analysis to support them. Specifically, I will cover probabilistic soft logic (PSL), a modeling language for relational domains, highlighting PSL’s underlying mathematical foundation, its associated algorithms, and its applications to computational social science and computer vision problems. I will also discuss new theoretical guarantees that formally characterize our ability to learn complex structured models. Finally, I will share my vision of the immediate and long-term future for complex structured machine learning.

Dr. Bert Huang is a postdoctoral research associate in the Department of Computer Science at the University of Maryland. He earned his PhD from Columbia University in 2011 for his work on efficient learning and inference using probabilistic models of graph structure. Bert’s research investigates topics including structured prediction, statistical relational learning, and computational social science, and his papers have been published at core conferences including NIPS, ICML, UAI, and AISTATS. His teaching in Columbia’s Department of Computer Science earned him the Andrew P. Kosoresow Memorial Award for Outstanding Performance in Teaching and Service.