SUNMONTUEWEDTHUFRISAT
Events for the 4th week of January
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Stochastic Optimal Control – Overview and Recent Advancesces
Wed, Jan 23, 2019 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ioannis Exarchos , Department of Biomedical Informatics, Emory University
Talk Title: Stochastic Optimal Control -“ Overview and Recent Advances
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Stochastic optimal control lies within the foundation of mathematical control theory ever since its inception. Its usefulness has been proven in a plethora of engineering applications, such as autonomous systems, robotics, neuroscience, and financial engineering, among others. Specifically, in robotics and autonomous systems, stochastic control has become one of the most successful approaches for planning and learning, as demonstrated by its effectiveness in many applications, such as control of ground and aerial vehicles, articulated mechanisms and manipulators, and humanoid robots. In computational neuroscience and human motor control, stochastic optimal control theory has been used in the process of modeling the underlying computational principles of the neural control of movement. Furthermore, in financial engineering, stochastic optimal control provides the main computational and analytical framework, with widespread application in portfolio management and stock market trading.
The aim of this talk is to provide an overview on model-based stochastic optimal control and highlight some recent advances in its field. We will briefly present some well-established methods (Differential Dynamic Programming, Path Integral Control), illustrating their differences in approach and restrictive conditions. Motivated by these restrictive conditions, we will then present a novel framework for stochastic optimal control that capitalizes on the innate relationship between certain nonlinear PDEs and Forward and Backward Stochastic Differential Equations (FBSDEs), that relaxes some of these conditions. The utility of the proposed method will be demonstrated on some examples of L2- and L1- optimal control, as well as differential games.
Biography: Ioannis Exarchos received his Diploma degree (graduating valedictorian) in Mechanical Engineering and Aeronautics from the University of Patras, Greece, in 2010. He also received an M.S. degree in Mathematics in 2015, as well as his M.S. and Ph.D. degrees in Aerospace Engineering in 2013 and 2017 respectively, all from the Georgia Institute of Technology. During his PhD studies, he was an Onassis Foundation fellowship scholar. His research interests include stochastic optimal control, machine learning applications in control and neuroscience, dynamical systems and system identification, as well as differential game theory. He is currently a postdoctoral fellow at the Department of Biomedical Informatics, Emory University.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Ming Hsieh Institute Visitor Program
Thu, Jan 24, 2019 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: H. Vincent Poor, Michael Henry Strater University Professor of Electrical Engineering at Princeton University
Talk Title: Fundamentals for Low Latency Communications
Abstract: Information theory provides fundamental insights into communication system capabilities, and the classical theory of Shannon has guided development of such systems over many decades. However, the classical models are based on assumptions of infinite block-length codes and not address situations in which short block lengths are imposed by system design considerations.
Notably in this context, latency has become a critical design issue in emerging wireless networking paradigms, such as the Internet of Things and associated applications like autonomous driving, factory automation, etc. This situation has inspired the development of a finite-block-length information theory, with man new results coming in recent years. This talk will review these developments, including fundamental finite-block-length results for the basic models of network information theory. Age of Information, another approach to the fundamental study of latency, will also be discussed briefly.
Biography: H. Vincent Poor is the Michael Henry Strater Professor of Electrical Engineering at Princeton University. From 1977, and until joining the Princeton faculty in 1990, he was on the faculty of the University of Illinois. During 2006 - 2016, he served as Dean of Princeton's School of Engineering and Applied Science. He has also held visiting positions at several other universities, including most recently at Berkeley and Cambridge. Dr. Poor's research interests are in the areas of signal processing and information theory and their applications in wireless networks, energy systems and related fields. He is a member of the National Academy of Engineering and the National Academy of Sciences, and is a foreign member of the Chinese Academy of Sciences, the Royal Society, and other national and international academies. He received the IEEE Alexander Graham Bell Medal in 2017.
Host: MHI
Location: 132
Audiences: Everyone Is Invited
Contact: Benjamin Paul
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EEG Characteristics of Major Depressive Disorder Patients with Suicidal Symptoms
Fri, Jan 25, 2019 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Lars Benschop, Ghent Experimental Psychiatry Ghent University Hospital in Belgium
Talk Title: EEG Characteristics of Major Depressive Disorder Patients with Suicidal Symptoms
Biography: Lars Benschop is a PhD candidate from the Ghent Experimental Psychiatry (GHEP) lab at the Ghent University hospital in Belgium. He received his Master's degree in experimental and biological psychology from the University of Ghent.
Lar's research focuses on identifying clinically relevant neural electrophysiological biomarkers in major depressive disorder (MDD) patients with a high risk of suicide. His first study applied a cluster-based permutation analysis on EEG spatial-frequency resting-state data to evaluate differences with respect to suicide risk.
Currently, Lars is designing a combined resting state and task-based study in which 90 MDD patients with varying suicide risk will be shown death and life-related concepts (words and pictures) while undergoing EEG. The aim is to replicate the findings of the first study while also expanding into the time-frequency domain to further our understanding of the suicidal brain. Additionally, Lars will be applying machine learning techniques with the goal of differentiating between MDDs with and without suicide risk.
Outside of his research interests, Lars enjoys composing music, scuba-diving, traveling and hiking.
Host: Professor Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White