Machine Learning for Emerging Engineering Applications: Circuit, Brain and Automobile
Tuesday, October 13, 2015
11:45 am - 1:00 pm
Hudson Hall 222
Pizza lunch served at 11:45 am. Seminar starts at 12 noon. Abstract: Machine learning is an important technique that has been continuously growing during the past several decades. It has been successfully applied to a variety of commercial applications in healthcare, social network, etc. This seminar will cover several emerging engineering applications, including circuit, brain and automobile, where machine learning is playing an extremely important role. In particular, we will discuss a number of machine learning algorithms (e.g., sparse regression, Bayesian model fusion, spectral clustering, etc.) and hardware accelerators, and demonstrate how they can effectively benefit our applications of interest. In addition to our academic research in this area, a case study of applying machine learning to smart building in an industrial setup will be briefly presented at the end of this seminar. Bio: Xin Li received the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA in 2005, and the M.S. and B.S. degrees in Electronics Engineering from Fudan University, Shanghai, China in 2001 and 1998, respectively.