Wed, Sep 04, 2019 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Gaurav Gupta, Electrical & Computer Engineering, University of Southern California
Talk Title: Dealing with Unknown Unknowns: Compact Perception from Heterogeneous Data
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Deciphering patterns from heterogeneous and noisy data to make robust inferences require knowledge of the complete system. Even with big data sizes, the presence of unknown unknowns (contributors) may not be neglected due to complex interactions of the observed system with the unobserved components and the environment (e.g., brain, social networks, gene-regulatory networks, physiological signals). In this talk, we will discuss the incorporation of unknown unknowns in the context of non-stationary non-Markovian processes. A multi-scale approach is used to model the non-Markovian time-dependence of the complex network nodes. The behavior is modeled using fractional differential equations. The benefits of this approach are demonstrated by modeling the real-world biological data of brain electroencephalogram (EEG), neuron spikes, and physiological signals like temperature and heartbeat intervals while considering the prediction of brain state or the prediction of viral infection. We will also describe how a compact model can be efficiently used to tackle some practical problems in brain machine interfaces and viral prediction in a different perspective than the traditional machine learning approaches.
Biography: Gaurav Gupta is a Ph.D. student working under the supervision of Prof. Paul Bogdan in the Ming Hsieh Department of Electrical and Computer Engineering at University of Southern California. He received his B.Tech degree from Indian Institute of Technology Kanpur in 2013 and M.S. from University of California Irvine in 2016, both in Electrical Engineering. His research interests include modeling of complex networks in the presence of unknown unknowns, discrete optimization, information theory for machine and representation learning, network inference for biological and social networks and the science biological computation.
Host: Paul Bogdan
Audiences: Everyone Is Invited
Contact: Talyia White