Distinguished Lecture Series 2014/15 - Prof. Daniel BoleyOn 11 February 2015, Prof. Daniel Boley of the University of Minnesota visited the Department and gave a Distinguished Lecture on “Optimization in Machine Learning”. Prof. Boley explained one of the most popular optimization methods to use - the Alternating Direction Method of Multipliers, which is extremely well-scalable, but the convergence rate can be erratic. He introduced the problem and algorithm with some applications and showed how linear algebra can explain the erratic behavior.The lecture was well received and concluded with active discussion.
Daniel Boley received his Ph.D. degree in Computer Science from Stanford University in 1981. Since then, he has been on the faculty of the Department of Computer Science and Engineering at the University of Minnesota, where he is now a full professor. Dr. Boley is known for his past work on numerical linear algebra methods for control problems, parallel algorithms, iterative methods for matrix eigenproblems, inverse problems in linear algebra, as well as his more recent work on computational methods in statistical machine learning, data mining, and bioinformatics. His current interests include scalable algorithms for convex optimization in machine learning, the analysis of networks and graphs such as those arising from metabolic biochemical networks and networks of wireless devices. He is an associate editor for the SIAM Journal of Matrix Analysis and has chaired several technical symposia at major conferences.