The Yale Institute for Network Science cordially invites you for lunch and the YINS Distinguished Lecturer Series:
Speaker: Jeffrey Bilmes
Professor, Department of Electrical Engineering, University of Washington, Seattle
“The Science of Data Management”
Abstract: The recent growth of available data is both a blessing and a curse for the field of data science. Large data sets can lead to improved predictive accuracy, create research opportunities in parallel computing, and (as we will discuss) expose holistic knowledge. Such data sets also be plagued with redundancy, leading to wasted computation. In this talk we will discuss a class of approaches to data management based on submodular functions, a powerful class of discrete functions that have properties analogous to both convexity and concavity. We will see how a form of “combinatorial dependence” over data sets can be naturally induced via submodular functions, and how resulting submodular programs (that often have approximation guarantees) can yield practical and high-quality data management strategies, such as data summarization and data partitioning for large-scale parallel computing. The effectiveness will be demonstrated via results from a range of applications, including computer vision, natural language processing, functional genomics, and distributed parallel computation.
Bio: Jeffrey A. Bilmes is a professor in the Department of Electrical Engineering at the University of Washington, Seattle and an adjunct professor in the Department of Computer Science and Engineering and the Department of Linguistics. He received his Ph.D. in Computer Science from the University of California, Berkeley. He is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer. Prof. Bilmes has been working on submodularity in machine learning for more than thirteen years. He received the best paper award at ICML 2013 and a best paper award at NIPS 2013 for work in this area. Prof. Bilmes is also a recipient of a 25-year paper award from the International Conference on Supercomputing for his 1997 paper on high-performance matrix optimization. Prof. Bilmes authored the graphical models toolkit (GMTK), a dynamic graphical-model based software system that is widely used in speech and language processing, bioinformatics, and human-activity recognition.