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Events for February 09, 2017

  • CS Colloquium: Dorsa Sadigh (UC Berkeley) -Towards a Theory of Safe and Interactive Autonomy

    Thu, Feb 09, 2017 @ 11:00 AM - 12:20 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Dorsa Sadigh, UC Berkeley

    Talk Title: Towards a Theory of Safe and Interactive Autonomy

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    Today's society is rapidly advancing towards cyber-physical systems (CPS) that interact and collaborate with humans, e.g., semi-autonomous vehicles interacting with drivers and pedestrians, medical robots used in collaboration with doctors, or service robots interacting with their users in smart homes. The safety-critical nature of these systems requires us to provide provably correct guarantees about their performance in interaction with humans. The goal of my research is to enable such human-cyber-physical systems (h-CPS) to be safe and interactive. I aim to develop a formalism for design of algorithms and mathematical models that facilitate correct-by-construction control for safe and interactive autonomy.

    In this talk, I will first discuss interactive autonomy, where we use algorithmic human-robot interaction to be mindful of the effects of autonomous systems on humans, and further leverage these effects for better safety, efficiency, coordination, and estimation. I will then talk about safe autonomy, where we provide correctness guarantees, while taking into account the uncertainty arising from the environment. Further, I will discuss a diagnosis and repair algorithm for systematic transfer of control to the human in unrealizable settings. While the algorithms and techniques introduced can be applied to many h-CPS applications, in this talk, I will focus on the implications of my work for semi-autonomous driving.

    Biography: Dorsa Sadigh is a Ph.D. candidate in the Electrical Engineering and Computer Sciences department at UC Berkeley. Her research interests lie in the intersection of control theory, formal methods, and human-robot interactions. Specifically, she works on developing a unified framework for safe and interactive autonomy. Dorsa received her B.S. from Berkeley EECS in 2012. She was awarded the NDSEG and NSF graduate research fellowships in 2013. She was the recipient of the 2016 Leon O. Chua department award and the 2011 Arthur M. Hopkin department award for achievement in the field of nonlinear science, and she received the Google Anita Borg Scholarship in 2016.

    Host: CS Department

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • Neuromorphic Systems to Reverse Engineer Reflex Function

    Thu, Feb 09, 2017 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Professor Francisco Valero-Cuevas, USC

    Talk Title: Neuromorphic Systems to Reverse Engineer Reflex Function

    Abstract: The objective of this work is to build a neuromorphic robotic system that can interact with the physical world by implementing neuromechanical principles. It is a faithful implementation of the spinal circuitry responsible for the afferentation of muscles and is capable of producing both normal and pathological functions.

    We used state-of-the-art models of muscle spindle mechanoreceptors with fusimotor drive, monosynaptic circuitry of the stretch reflex, and alpha motoneuron recruitment and rate coding. This multi-scale, hybrid system driven by populations of 1024 spiking neurons, emulated the physiological characteristics of the afferented mammalian muscles. We implemented these models on field-programmable gate arrays (FPGAs) which are capable of running these complex computations in real-time. The FPGAs control the forces of two muscles acting on a joint via long tendons. We performed ramp-and-hold perturbations and systematically explored a range of muscle spindle gains (fusimotor drive) to characterize the stretch reflex response in different phases of the perturbation. Finally, we explored the fidelity of four models for isometric muscle force production by testing their responses to rate-coding using spike trains and produced force ramps.

    This autonomous integrated system was self-stable and the closed-loop behavior of populations of muscle spindles, alpha and gamma motoneurones, and muscle fibers emulated muscle tone and function. Sweeping the range of muscle spindle gains provided us with a subset of values that produced tenable physiological and pathological responses. Moreover, isometric force generation revealed that the dynamic response in the tendons is very sensitive to tendon elasticity, especially at high firing rates.

    This hybrid, neuromorphic, neuromechanical system is a precursor to neuromorphic robotic systems. It provides a platform to study healthy function and the potential spinal and/or supraspinal sources of pathologic behavior.

    Biography: I attended Swarthmore College from 1984-88 where I obtained a BS degree in Engineering. After spending a year in the Indian subcontinent as a Thomas J Watson Fellow, I joined Queen's University in Ontario and worked with Dr. Carolyn Small. The research for my Masters Degree in Mechanical Engineering at Queen's focused on developing non-invasive methods to estimate the kinematic integrity of the wrist joint.

    In 1991, I joined the doctoral program in the Design Division of the Mechanical Engineering Department at Stanford University. I worked with Dr. Felix Zajac developing a realistic biomechanical model of the human digits. This research, done at the Rehabilitation R & D Center in Palo Alto, focused on predicting optimal coordination patterns of finger musculature during static force production.

    After completing my doctoral degree in 1997, I joined the core faculty of the Biomechanical Engineering Division at Stanford University as a Research Associate and Lecturer. In 1999, I joined the faculty of the Sibley School of Mechanical and Aerospace Engineering at Cornell University as Assistant Professor, and was tenured in 2005. In 2007, I joined the faculty at the Department of Biomedical Engineering, and the Division of Biokinesiology & Physical Therapy at the University of Southern California as Associate Professor; where I was promoted to Full Professor in 2011. In 2013 I was elected Senior Member of the IEEE, and in 2014 to the College of Fellows of the American Institute for Medical and Biological Engineers.

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Lyman L. Handy Colloquia

    Thu, Feb 09, 2017 @ 12:45 PM - 01:50 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars

    Speaker: Dr. Rampi Ramprasad , University of Connecticut

    Talk Title: Rational Computation-Guided Design of Polymer Dielectrics

    Series: Lyman Handy Colloquia

    Host: Professor Rajiv Kalia

    Location: James H. Zumberge Hall Of Science (ZHS) - 159

    Audiences: Everyone Is Invited

    Contact: Martin Olekszyk

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  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems

    Thu, Feb 09, 2017 @ 02:00 PM - 03:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Justinian Rosca , Siemens Corporate Technology, Princeton, NJ

    Talk Title: Synchronizing the Real and Digital Worlds: Lessons from Autonomous Cars

    Abstract: Computational estimation problems for real-world applications are rife with myriad sources of uncertainty from noise, sensor inaccuracies, incompleteness of the data, unmeasured effects, calibration errors, to physical principles not modeled in computation. We are interested in inference and reasoning frameworks with capabilities for characterizing and handling uncertainty throughout the computational process in all phases of the unified digital twin of a cyber physical system.
    In this talk I present examples from my research on uncertainty handling for two problems. Each deals with a different phase for building the digital twin of an automated system, such as an autonomously driven connected car. Automated vehicle technology senses the driving environment and operates a vehicle with limited or even without human input. Digital twins offer the potential to unify models across the lifecycle phases of a complex cyber physical system, from design (CAD models), engineering (CAE models), production (CAM and simulation models), to operation and maintenance (PHM and reliability models).

    The first use case is from the engineering phase of an autonomous vehicle that drives safely through intersections. Simulation is a powerful cost-effective method for developing, testing, and evaluating various components of new technologies, where a limited initial market penetration and unknown human behavioral responses are the status-quo. Realistic modeling of how connected vehicles "talk" to each other while moving in traffic is essential for large scale simulations of time-critical applications. However, there is no widely agreed upon physical model for Dedicated Short Range Communications (DSRC) over the 75 MHz of spectrum using the IEEE 802.11p standard. How do we sum up and exploit real world measurements of interference, fading, antennas, weather, environment type, vehicle movement and traffic density, which are difficult to characterize and rich in uncertainty at all levels? Brought into simulation, these will affect the very algorithms that control the vehicle and acquire new data. Therefore, we bring data from the real world into the digital twin to affect the design and engineering phases and vet the application on a large scale. At the other end of the digital twin, the second use case is about edge intelligence in a vehicle perpetually connected to its physical world through hundreds of sensors and communication links, which offer fast analogue and digital data to be exploited in understanding the patterns of operation for machine health management and ultimately, for control. Again we face the challenge of processing a river of data and reasoning with uncertainty pro-actively about the past and the future, to explain system dynamics, gain immediate insights for control, and connect to the prior lifecycle phases of design and engineering.

    Biography: Justinian Rosca is Senior Key Expert of Siemens Corp., Corporate Technology in Princeton NJ, where he has been managing research and innovation since 1999. He received his Ph.D. and M.S. degrees in Computer Science from the University of Rochester, NY. He also holds the M.S. degree in Computers and Control Engineering from Polytechnic University Bucharest. He was Affiliate Professor at the University of Washington, 2008-2011, and obtained a certificate in executive management for innovation, from the University of Pennsylvania, Wharton School of Business.

    Dr. Rosca's primary research interests span sensing and communication, statistical signal processing, machine learning, probabilistic inference, and artificial intelligence, with an emphasis on embedded intelligence in autonomous systems. Dr. Rosca holds close to 50 patents, 100 publications in the areas of signal processing, machine learning, and cyber-physical systems, and co-authored two books in mathematics and signal processing. Several of his innovations are at the foundation of Siemens' multi-channel digital hearing aids technology, and affect the quality of hearing for millions of people worldwide. His scientific contributions were transferred into a variety of products and systems such as microphone array technologies for hearing aids and mobile phones, adaptive multimedia wireless network management, traffic services for connected vehicles, and edge analytics in industry, and earned him multiple Siemens business unit awards. He served as program chair of the 6th Independent Component Analysis and Blind Signal Separation International Conference, chair of the Neural Information and Processing Systems workshop on Sparse Representations in Signal Processing, and recently as chair of the Data Challenge 2015 and 2016 competitions of the Prognostics and Health Management Society.

    Host: Paul Bogdan

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Estela Lopez

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  • PhD Defense: Analysis and Modeling of Multi-Level Dynamics of Multimodal Behavior in Affective Human Interactions

    Thu, Feb 09, 2017 @ 02:00 PM - 04:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Zhaojun Yang, University of Southern California, PhD Candidate

    Talk Title: Analysis and Modeling of Multi-Level Dynamics of Multimodal Behavior in Affective Human Interactions

    Abstract: Human communication is a dynamical and interactive process that is established on a common ground of conveying emotions, achieving the interaction goals and sharing mutual interests of the interaction participants. Such an interactive process naturally induces a multi-level dynamical flow along various verbal and nonverbal behavior dimensions of spoken words, speech prosody, body gestures, and facial expressions. As one of the major components that shape the structure of social interactions, emotions greatly affect the multi-level dynamics of multimodal behavior throughout the course of an interaction. This thesis, from three perspectives, explores computational methodologies to understand, analyze and model human behaviors dynamics that relate to and arise from affective processes underlying human interactions: 1) modeling the dynamics of body gesture expression of emotions; 2) studying how multimodal behavior channels, speech and body particularly, of an individual dynamically interact with one another towards emotion expression; and 3) exploring interpersonal coordination of multimodal behavior induced in human interactions.

    Defense committee: Prof. Shrikanth Narayanan (Chair), Prof. C.-C. Jay Kuo, Prof. Gayla Margolin (Outside member)

    Biography: Zhaojun Yang is a PhD candidate in Electrical Engineering at the University of Southern California (USC). She received her B.E. Degree from University of Science and Technology of China (USTC) 2009 and M.Phil. Degree from Chinese University of Hong Kong (CUHK) 2011. She was awarded with the USC Annenberg Fellowship (2011-2015). Her work (with S. S. Narayanan) has won the Best Student Paper Award at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016. Her research interests include Affective Computing, Machine Learning, and Human-centered multimodal signal processing.

    Host: Shrikanth Narayanan

    Location: Ronald Tutor Hall of Engineering (RTH) - 320

    Audiences: Everyone Is Invited

    Contact: Tanya Acevedo-Lam/EE-Systems

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  • Schlumberger Information Session

    Thu, Feb 09, 2017 @ 06:30 PM - 08:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions

    Learn about Schlumberger and network with company representatives.

    Location: Seeley G. Mudd Building (SGM) - 101

    Audiences: All Viterbi Students

    Contact: RTH 218 Viterbi Career Connections

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