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Events for the 5th week of September
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MHI Pioneer Series
Mon, Sep 25, 2017 @ 03:00 AM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Andrew Viterbi, University of Southern California Trustee, Presidential Chair, and Professor of Electrical Engineering
Talk Title: "It was the worst of times, it was the best of times." (with apologies to Mr. Dickens)
Series: MHI Pioneer Series
Abstract: The last two thirds of the 20th Century was a period of tremendous upheaval and progress, social, political and especially technological. This was the period during which I pursued two careers which were tightly intertwined. Curiously both were also influenced by our nation's most threatening competitor, Russia.
The first was my academic career and the second my entrepreneurial career, both of which covered over thirty years, with considerable overlap. Though unrecognized at the time, my academic research had roots in the work of the Russian mathematician Andrei Markov, while with full recognition, my entrepreneurial career was launched and initially supported by our Defense research efforts to counter the Soviet threat.
From 1957, when I arrived at Caltech's JPL just before the launch of Sputnik, until 2000 when I retired from Qualcomm, I was involved in furthering the knowledge, understanding and implementation of wireless digital communication, first for space and ultimately for cellular networks. My academic achievements, which have given me the most satisfaction, were primarily in the fields of synchronization and of error-suppressing coding. My entrepreneurial efforts were in support of the founding of two digital communication companies, Linkabit and Qualcomm, whose technologists achieved important breakthroughs through the practical realization of communication theory principles. Among these were the first Viterbi decoder now ubiquitous in digital wireless handsets, the first fully digitally implemented satellite modem, the first mobile satellite terrestrial network and the first spread spectrum digital cellular networks, which enabled the rise of a myriad of applications.
In the new millennium, to prevent boredom and counter aging, my time has been devoted partly to activities on corporate boards of startup companies in digital communication, data storage and their numerous applications. My Memoir, "Reflections of an Educator, Researcher and Entrepreneur," was published recently.
Host: Ming Hsieh Institute
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Cathy Huang
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Sep 25, 2017 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Angelia Nedich, Arizona State University
Talk Title: Fast Distributed Algorithms for Optimization and Resource Sharing in Networks
Abstract: We will discuss the problems of distributed optimization over graphs. For the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGing, which is a combination of a distributed inexact gradient method and a gradient-tracking mechanism. The DIGing algorithm uses doubly stochastic mixing matrices and employs fixed step-sizes and, yet, drives all agents' iterates to a common global minimizer. When the graphs are directed, in which case the implementation of doubly stochastic mixing matrices is unrealistic, we construct an algorithm that incorporates the push-sum protocol into the DIGing structure, thus obtaining Push-DIGing algorithm. Under the strong convexity assumption for the objective function, we prove that both algorithms converge at R-linear (geometric) rates, as long as the step-sizes do not exceed some upper bounds. We establish explicit convergence rate estimates for the convergence rates. When the graph is undirected, we show that the convergence rate of DIGing scales polynomially in the number of agents. We also provide some numerical experiments to demonstrate the efficacy of the proposed algorithms and to validate our theoretical findings. We then discuss the variants of these algorithms for resource allocation problems in graphs.
Biography: Angelia Nedich holds a Ph.D. from Moscow State University, Moscow, Russia, in Computational Mathematics and Mathematical Physics (1994), and a Ph.D. from Massachusetts Institute of Technology, Cambridge, USA in Electrical and Computer Science Engineering (2002). She has worked as a senior engineer in BAE Systems North America, Advanced Information Technology Division at Burlington, MA. She is the recipient of an NSF CAREER Award 2007 in Operations Research for her work in distributed multi-agent optimization. She is a recipient (jointly with her co-authors) of the Best Paper Award at the Winter Simulation Conference 2013 and the Best Paper Award at the International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) 2015 (with co-authors). She has served as Associate Editor for IEEE Transactions on Automatic Control and Transactions of Control of Network Systems. She is currently serving on Editorial Board of SIAM Journal on Optimization and for INFORMS Operations Research. Her current interest is in large-scale optimization, games, control and information processing in networks.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Tue, Sep 26, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Evangelos Theodorou, Georgia Institute of Technology
Talk Title: The Science of Autonomy: a "Happy" Symbiosis Among Learning, Control, and Physics
Series: Fall 2017 Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: In this talk, I will present an information theoretic approach to stochastic optimal control that has advantages over classical methodologies and theories for decision making under uncertainty. The main idea is that there are certain connections between optimality principles in control and information theoretic inequalities in statistical physics that allow us to solve hard decision making problems in robotics, autonomous systems and beyond. There are essentially two different points of view of the same "thing" and these two different points of view overlap for a fairly general class of dynamical systems that undergo stochastic effects. The information theoretic approach can also be used in a game theoretic setting for teams of robots performing cooperative or non-cooperative tasks. I will also present a holistic view to autonomy that collapses planning, perception and control into one computational engine, and ask questions related to how organization and structure relates to functionality and performance in "engineered" organisms. The last part of my talk includes computational frameworks for uncertainty representation and suggests ways to incorporate these representations within decision making and control.
Biography: Evangelos A. Theodorou is an assistant professor with the Guggenheim School of aerospace engineering at Georgia Institute of Technology. He is also affiliated with the Institute of Robotics and Intelligent Machines. Evangelos Theodorou earned his Diploma in Electronic and Computer Engineering from the Technical University of Crete (TUC), Greece in 2001. He has also received a MSc in Production Engineering from TUC in 2003, a MSc in Computer Science and Engineering from University of Minnesota in spring of 2007 and a MSc in Electrical Engineering on dynamics and controls from the University of Southern California (USC) in Spring 2010. In May of 2011 he graduated with his PhD, in Computer Science at USC. After his PhD, he was a Postdoctoral Research Fellow with the department of computer science and engineering, University of Washington, Seattle. Evangelos Theodorou is the recipient of the King-Sun Fu best paper award of the IEEE Transactions on Robotics for the year 2012 and recipient of the best paper award in cognitive robotics in International Conference of Robotics and Automation 2011. He was also the finalist for the best paper award in International Conference of Humanoid Robotics in 2010 and International Conference of Robotics and Automation in 2017. His theoretical research spans the areas of stochastic optimal control theory, machine learning, information theory, and statistical physics. Applications involve learning, planning and control in autonomous, robotics and aerospace systems.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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A Book Talk about A MIND AT PLAY: HOW CLAUDE SHANNON INVENTED THE INFORMATION AGE
Wed, Sep 27, 2017 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jimmy Soni, Author
Talk Title: A Book Talk about A MIND AT PLAY: HOW CLAUDE SHANNON INVENTED THE INFORMATION AGE
Abstract: Claude Shannon was a groundbreaking polymath, a brilliant tinkerer, and a digital pioneer. He constructed a fleet of customized unicycles and a flamethrowing trumpet, outfoxed Vegas casinos, and built juggling robots. He also wrote the seminal text of the digital revolution, which has been called "the Magna Carta of the Information Age." His discoveries would lead contemporaries to compare him to Albert Einstein and Isaac Newton. His work anticipated by decades the world we'd be living in today - and gave mathematicians and engineers the tools to bring that world to pass.
In this elegantly written, exhaustively researched biography, Jimmy Soni and Rob Goodman reveal Claude Shannon's full story for the first time. It's the story of a small-town Michigan boy whose career stretched from the era of room-sized computers powered by gears and string to the age of Apple. It's the story of the origins of our digital world in the tunnels of MIT and the "idea factory" of Bell Labs, in the "scientists' war" with Nazi Germany, and in the work of Shannon's collaborators and rivals, thinkers like Alan Turing, John von Neumann, Vannevar Bush, and Norbert Wiener.
And it's the story of Shannon's life as an often reclusive, always playful genius. With access to Shannon's family and friends, A Mind at Play brings this singular innovator and creative genius to life.
Biography: Jimmy Soni was managing editor at The Huffington Post from January 2012-2014. Previously he had worked as a strategy consultant at McKinsey and Company, as well as a speech writer at the office of the Mayor of the District of Columbia. Soni has co-authored several pieces with fellow Duke graduate Rob Goodman; their work has been featured in Politico, The Huffington Post, Business Insider, AdWeek, and The Atlantic, among others.
In 2012, Jimmy, published his first book a biography of Cato the Younger, titled Rome's Last Citizen: The Life and Legacy of Cato, Mortal Enemy of Caesar.
Host: Center for Cyber-Physical Systems and the Internet of Things
More Information: CCI_Shannon_BookTalk_September27_2017.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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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
Wed, Sep 27, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Eric Feron , Professor, Georgia Institute of Technology
Talk Title: 20 Years of Aerobatic Flight with Autonomous Air Vehicles
Abstract: The past 20 years have seen a remarkable evolution of the drone technology. Back in 1997, academia had to deal with heavy, bulky and expensive machines powered by cranky internal combustion engines. Unmanned vehicles today are a lot cheaper, lighter, and reliable, making them a lot more approachable by students and faculty alike. After tracing our research back to the late 1990s, this talk will introduce an aerobatic drone capable of producing reduced- or zero-gravity conditions at an affordable cost. The platform is still a prototype, but it captures most of the difficulties faced by the larger platform of our dreams. The controller design will be discussed, and a full non-linear maneuver stability analysis will be presented that mixes the concept of transverse dynamics with well-known concepts from robust control. This is joint work with John Hauser (U. Colorado, Boulder) and Pablo Afman (Georgia Tech).
Biography: Eric Feron is a professor at Georgia Tech, where he directs the Decision and Control Laboratory. His basic training is in applied mathematics, computer science, and operations research. His interests include aerospace systems and robotics. Noteworthy achievements include an airport congestion control algorithm now used at many major airports (1999), the first aerobatic autonomous air vehicle (2001), the english translation of Ãtienne Bézout's General Theory of Algebraic Equations (2006), and a course on cyber-physical systems offered by Georgia Tech as part of its Online Master of Science in Computer Science (2017).
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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Scaling Machine Learning Performance with Moore's Law
Thu, Sep 28, 2017 @ 02:00 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kunle Olukotun, Stanford University
Talk Title: Scaling Machine Learning Performance with Moore's Law
Abstract: The computational demands of machine learning (ML) requires energy efficient machine learning specific accelerators. This naturally results in heterogeneous computing platforms composed of CPUs and ML Accelerators. However, the staggering cost (the majority of the cost is for software development) of designing custom integrated circuits for many application domains makes it cost-prohibitive to design these accelerators. This situation calls for a new paradigm for designing accelerators that can provide energy-efficient ML-specific performance and easier software development. The key to this new paradigm is to enable application developers to optimize the underlying hardware to make it specific to their ML application needs. The new design paradigm consists of new application ML-specific programing languages, new machine learning algorithms, new compilation technology to target both existing (FPGAs) and new (Software Defined Hardware) reconfigurable architectures.
Biography: Kunle Olukotun is the Cadence Design Systems Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is well known as a pioneer in multicore processor design and the leader of the Stanford Hydra chip multipocessor (CMP) research project. Olukotun founded Afara Websystems to develop high-throughput, low-power multicore processors for server systems. The Afara multicore processor, called Niagara, was acquired by Sun Microsystems. Niagara derived processors now power all Oracle SPARC-based servers. Olukotun currently directs the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate the use of heterogeneous parallelism in all application areas using Domain Specific Languages (DSLs). Olukotun is a member of the Data Analytics for What's Next (DAWN) Lab which is developing infrastructure for usable machine learning. Olukotun is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design. Olukotun received his Ph.D. in Computer Engineering from The University of Michigan.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Sep 29, 2017 @ 10:00 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Bodhisatwa Sadhu, Research Staff Member, IBM T.J. Watson Research Center
Talk Title: mmWave Radio Design for 5G Base-stations and Mobile Handsets
Host: Profs. Hossein Hashemi, Mike Chen, Mahta Moghaddam, and Dina El-Damak
More Information: MHI Seminar Series IS -Bodhisatwa Sadhu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
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Munushian Seminar - Ming C. Wu, Friday, September 22nd at 2:00pm in EEB 132
Fri, Sep 29, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ming C. Wu, University of California, Berkeley
Talk Title: Silicon Photonic MEMS
Abstract: Ming C. Wu is Nortel Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is also Co-Director of Berkeley Sensor and Actuator Center (BSAC) and Faculty Director of UC Berkeley Marvell Nanolab. Dr. Wu received his M.S. and Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 1988. He has been with AT&T Bell Laboratories, Murray Hill (1988-1992) and UCLA (1993 to 2004) before joining the faculty at Berkeley. His research interests include optoelectronics, nanophotonics, MEMS, and optofluidics. He has published 8 book chapters, over 500 papers in journals and conferences, and 25 issued U.S. patents.
Prof. Wu is an IEEE Fellow, and a Packard Foundation Fellow (1992 - 1997). He received the 2007 Paul F. Forman Engineering Excellence Award, the 2017 C.E.K. Mees Medal from Optical Society of America, and the 2016 William Streifer Award from IEEE Photonics Society.
Biography: Ming C. Wu is Nortel Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is also Co-Director of Berkeley Sensor and Actuator Center (BSAC) and Faculty Director of UC Berkeley Marvell Nanolab. Dr. Wu received his M.S. and Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 1988. He has been with AT&T Bell Laboratories, Murray Hill (1988-1992) and UCLA (1993 to 2004) before joining the faculty at Berkeley. His research interests include optoelectronics, nanophotonics, MEMS, and optofluidics. He has published 8 book chapters, over 500 papers in journals and conferences, and 25 issued U.S. patents.
Prof. Wu is an IEEE Fellow, and a Packard Foundation Fellow (1992 - 1997). He received the 2007 Paul F. Forman Engineering Excellence Award, the 2017 C.E.K. Mees Medal from Optical Society of America, and the 2016 William Streifer Award from IEEE Photonics Society.
Host: EE-Electrophysics
More Info: minghsiehee.usc.edu/about/lectures
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: minghsiehee.usc.edu/about/lectures
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Powering the Future of Imaging and Signal Processing with Data-Driven Systems
Fri, Sep 29, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Saiprasad Ravishankar, Electrical Engineering & Computer Science Department, University of Michigan
Talk Title: Powering the Future of Imaging and Signal Processing with Data-Driven Systems
Series: Medical Imaging Seminar Series
Abstract: The data-driven learning of signal models including dictionaries, sparsifying transforms, low-rank models, tensor and manifold models, etc., is of great interest in many applications. In this talk, I will present my research that developed efficient, scalable, and effective data-driven models and methodologies for signal processing and imaging. I will mainly discuss my work on transform learning. Various interesting structures for sparsifying transforms such as well-conditioning, double sparsity, union-of-transforms, incoherence, rotation invariance, etc., can be considered, which enable their efficient and effective learning and usage. Transform learning-driven approaches achieve promising results in applications such as image and video denoising, and X-ray computed tomography or magnetic resonance image (MRI) reconstruction from limited or corrupted data. The convergence properties of the algorithms will be discussed. I will also present recent work on efficient dictionary learning in combination with low-rank models, and demonstrate the usefulness of the resulting LASSI method for dynamic MRI. The efficiency and effectiveness of the methods proposed in my research may benefit a wide range of additional applications in imaging, computer vision, neuroscience, and other areas requiring data-driven parsimonious models. Finally, I will provide a brief overview of recent works on physics-driven deep training of image reconstruction algorithms, light field reconstruction from focal stacks, online data-driven estimation of dynamic data from streaming, limited measurements, etc.
Biography: Saiprasad Ravishankar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, in 2008. He received the M.S. and Ph.D. degrees in Electrical and Computer Engineering, in 2010 and 2014 respectively, from the University of Illinois at Urbana-Champaign, where he was an Adjunct Lecturer in the Department of Electrical and Computer Engineering during Spring 2015, and a Postdoctoral Research Associate at the Coordinated Science Laboratory until August, 2015. Since then, he has been a Research Fellow in the Electrical Engineering and Computer Science Department at the University of Michigan. His research interests include signal, image and video processing, signal modeling, data science, dictionary learning, biomedical and computational imaging, data-driven methods, inverse problems, compressed sensing, machine learning, and large-scale data processing.He has received multiple awards including the Sri Ramasarma V Kolluri Memorial Prize from IIT Madras and the IEEE Signal Processing Society Young Author Best Paper Award for his paper Learning Sparsifying Transforms published in IEEE Transactions on Signal Processing.
Host: Professor Richard Leahy
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Talyia White
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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
Fri, Sep 29, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yanzhi Wang , Syracuse University
Talk Title: Towards the limits of energy efficiency and performance of deep learning systems
Abstract: Deep learning systems have achieved unprecedented progresses in a number of fields such as computer vision, robotics, game playing, unmanned driving and aerial systems, and other AI-related fields. However, the rapidly expanding model size is posing a significant restriction on both the computation and weight storage, for both inference and training, and on both high-performance computing systems and low-power embedded system and IoT applications. In order to overcome these limitations, we propose a holistic framework of incorporating structured matrices into deep learning systems, and could achieve (i) simultaneous reduction on weight storage and computational complexities, (ii) simultaneous speedup of training and inference, and (iii) generality and fundamentality that can be adopted to both software and hardware implementations, different platforms, and different neural network types, sizes, and scalability.
Besides algorithm-level achievements, our framework has (i) a solid theoretical foundation to prove that our approach will converge to the same "effectiveness" as deep learning without compression, and to demonstrate/prove that our approach approach/achieve the theoretical limitation of computation and storage of deep learning systems; (ii) platform-specific implementations and optimizations on smartphones, FPGAs, and ASIC circuits. We demonstrate that our smartphone-based implementation achieves the similar speed of GPU and existing ASIC implementations on the same application. Our FPGA-based implementations for deep learning systems and LSTM networks could achieve 11X+ energy efficiency improvement compared with the best state-of-the-arts, and even higher energy efficiency gain compared with IBM TrueNorth neurosynaptic processor. Our proposed framework can achieve 3.5 TOPS computation performance in FPGAs, and is the first to enable nano-second level recognition speed for image recognition tasks.
Biography: Yanzhi Wang is currently an assistant professor in the Department of Electrical Engineering and Computer Science at Syracuse University, from August 2015. He has received his Ph.D. Degree in Computer Engineering from University of Southern California (USC) in 2014, under supervision of Prof. Massoud Pedram, and his B.S. Degree in Electronic Engineering from Tsinghua University in 2009.
Dr. Wang's current research interests are the energy-efficient and high-performance implementations of deep learning and artificial intelligence systems, neuromorphic computing and new computing paradigms, and emerging deep learning algorithms/systems such as Bayesian neural networks, generative adversarial networks (GANs), and deep reinforcement learning. Besides, he works on the application of deep learning and machine intelligence in various mobile and IoT systems, medical systems, and UAVs, as well as the integration of security protection in deep learning systems. He also works on near-threshold computing for IoT devices and energy-efficient cyber-physical systems. His group works on both algorithms and actual implementations (FPGAs, circuit tapeouts, mobile and embedded systems, and UAVs).
His work has been published in top venues in conferences and journals (e.g. ASPLOS, MICRO, ICML, DAC, ICCAD, DATE, ASP-DAC, ISLPED, INFOCOM, ICDCS, TComputer, TCAD, etc.), and has been cited for around 3,000 times according to Google Scholar. He has received four Best Paper or Top Paper Awards from major conferences including IEEE ICASSP (top 3 among all 2,000+ submissions), ISLPED, IEEE CLOUD, and ISVLSI. He has another six Best Paper Nominations and two Popular Papers in IEEE TCAD. His group is sponsored by the NSF, DARPA, IARPA, AFRL/AFOSR, Syracuse CASE Center, and industry sources.
Host: Paul Bogdan
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Estela Lopez