Amit Surana of United Technologies Research Center will speak on campus as part of the Michigan Institute for Data Science (MIDAS) Seminar Series.
Abstract: Recent technological advances in ubiquitous sensing, networking, storage and computing technology are leading to emergence of new paradigms such as Internet of Things, Industrial Internet and Cloud Robotics. These paradigms have led to an exponential explosion in the availability of high volume high velocity time series data which is posing new challenges in data analytics. Classical machine learning techniques exhibit poor scalability in dealing with such high dimensional continuous valued data, and often do not take advantage/preserve the dynamics inherent in the temporally evolving data. In this talk, Dr. Surana will describe a Koopman operator theoretic framework whereby one can cross-fertilize ideas from dynamical system and control theory with machine learning and statistics in order to address some of these challenges.
Time/Date: 4 p.m., Friday, Jan. 15
Location: Stern Auditorum, U-M Museum of Art
More information: MIDAS event page.