Amit Surana, PhD; United Technologies Research Center – MIDAS Seminar Series

  Title: Koopman Operator Theoretic Framework for Dynamic Data Analytics  Abstract: Recent technological advances in ubiquitous sensing, networking, storage and computing technology is 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, I 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. Koopman operator is a linear but an infinite-dimensional operator that governs the time evolution of observables or outputs defined on the state space of any dynamical system. We exploit the spectral properties of Koopman operator to construct a linear system for output evolution, and thereby propose a new framework for exploiting linear systems/control theory in context of nonlinear analysis. This framework is model free, amenable to scalable/streaming computations, and readily combines with and complements various supervised/unsupervised machine learning techniques. We demonstrate our framework in computer vision, and prognostics and health monitoring domain with applications including time series prediction, forecasting, anomaly detection, indexing/retrieval and classification. For more information on MIDAS or the Seminar Series, please contact midas-contact@umich.edu. MIDAS gratefully acknowledges Northrop Grumman Corporation for its generous support of the MIDAS Seminar Series.