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November 2017
Free

CESG Seminar: “Sequential Learning, Optimization and Control for Cyber-Physical Systems” – Dileep Kalathil

November 17 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

“Sequential Learning, Optimization and Control for Cyber-Physical Systems” Dr. Dileep Kalathil, ECE Assistant Professor, Texas A&M University   Abstract:  Convergence of the dramatic increase in available data and processing power, enabled by ubiquitous sensing and computing capabilities, is rapidly changing engineered systems. Cyber-Physical Systems (CPS) refers to such systems with tightly integrated computational, control and physical capabilities, like power grids and transportation networks. However, designing and implementing CPS involves an array of complex and challenging tasks: learning and making inference from data, designing scalable optimization and control methods, as well as developing decentralized and adaptive decision making algorithms. In the first part of the talk, I will discuss a strategy for sequential learning and decision-making for decentralized CPS. I will first introduce a multi-player, multi-armed bandit’s framework for modeling this class of problems. I will then present a sequential learning and decision making algorithm to solve this problem and show that it achieves optimal performance. In the second part of the talk, I will discuss an approach for simulation-based optimization and control of Markov Decision Process (MDP) models in the context of CPS. Designing exact optimization and control of such systems may be intractable due to its complexity. I develop a class of algorithms called Empirical Dynamic Programming to overcome this difficulty and provide provable non-asymptotic performance guarantees. I will also briefly discuss my work on data-driven learning and control in the context of transportation CPS.   Biography: Dr. Dileep Kalathil is an Assistant Professor in the Electrical and Computer Engineering Department at TAMU. Before joining TAMU, he was a postdoctoral scholar in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his PhD from the University of Southern California (USC) in 2014 where he won the best PhD Dissertation Prize in the USC Department of Electrical Engineering. He received an M.Tech from IIT Madras where he won the award for the best academic performance in the EE department. His research interests include control theory, sequential learning, game theory, and sustainable energy systems.

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Free

Special CESG Seminar on Engineering Practice: Implantable Electronic Migration at Biotronik

November 14 @ 2:20 pm - 3:20 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

“Implantable Electronic Migration at Biotronik” David Genzer ’83 Director of IC Design and Development – Texas Biotronik/Micro Systems Engineering Inc. Abstract: This talk will focus briefly on the state of Biotronik/MSEI, in the implantable device industry, the aggressive design challenges, and tradeoffs facing the design of implantable device products. A high level overview of the Hardware design and chip architecture of selected products and how these are architected to meet the needs of the customer in this aggressive industry will be covered. Biography: David Genzer PE., ’83, is the Director of IC Design and Development for Biotronik/Micro Systems Engineering Inc. with more than 30 years of experience in the development of systems and mixed-signal integrated circuits for implantable products. His interests include the design and development of biomedical systems using advanced VLSI architectures using Ultra Low Power/Low Voltage battery operated SOC’s for next generation implantable medical devices. His work in various medical devices includes implantable BioMonitors, Pacemaker products, Neuro Implantable SCS devices and next generation “injectable” cardiovascular electronic devices. David’s work experience includes positions at Micro Systems Engineering Inc. (MSEI or Biotronik) www.biotronik.com, 1999 to present; Director of the IC Design and Development, Intermedics Inc. (Sulzer Intermedics), 1985-1999 as Manager of the IC design group and previously as an IC Designer on the team developing the first single chip implantable pacemaker for Intermedics. Prior to this, he worked at UMC/Mostek Corp. (Now ST Micro) as an IC Development Engineer for various Low Power battery operated IC’s for consumer products. David received his BS degree in Electrical Engineering from Texas A&M University in 1983 and a Master’s Degree in Electrical Engineering from the University of Houston in 1991. He is also a member of IEEE Solid State Circuits Society (SSCC), a Licensed Professional Engineer in the State of Texas, and member of the EADC committee for Electrical & Computer Engineering at Texas A&M University.        David Genzer 2017 slide    

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Free

CESG Seminar: “Human-Centric Machine Learning in the Big Data Era”

November 10 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

“Human-Centric Machine Learning in the Big Data Era” Dr. Xia Ben Hu, Assistant Professor, Computer Science and Engineering, Texas A&M University   Abstract:  This talk will cover the recent progress of several ongoing projects directed by Dr. Xia “Ben” Hu on human-centric machine learning. Though machine learning has achieved a lot of success in different applications, it is still challenging for domain experts to easily make use of machine learning systems and algorithms in applications that matter, especially in the big data era. To bridge the gap, we propose to develop interpretable and automated machine learning systems to handle big data through the following efforts. First, we will present a specific example using social spammer detection to show how we can develop machine learning algorithms to handle large-scale, heterogeneous and dynamic big data? Second, we will discuss the system architecture and main algorithms, as well as our current progress, to bridge the gap between powerful deep learning algorithms and interpretable shallow models. This is to answer the question how we could enable interpretable machine learning. Third, we will briefly introduce the system architecture and main algorithms, as well as our current progress, to develop an end-to-end automated machine learning system. This is to show that how we could enable automated machine learning for domain experts with limited data science background. Biography: Dr. Xia “Ben” Hu is currently a tenure-track Assistant Professor at Texas A&M University in the Department of Computer Science and Engineering. He received his Ph.D. in Computer Science and Engineering from Arizona State University, and M.S. and B.S. in Computer Science from Beihang University, China. His research focus is to develop data mining and machine learning algorithms with understanding of theoretical properties to better discover actionable patterns from large-scale, networked, dynamic and sparse data. Existing research projects are directly motivated by, and contribute to, applications in social informatics, health informatics and information security. PI Hu has published more than 90 papers in several major Data Mining and Artificial Intelligence venues, including KDD, WWW, SIGIR, ICDM, SDM, WSDM, IJCAI, AAAI, CIKM, and ICWSM. One of his papers was selected in the Best Paper Shortlist in WSDM’13, and one received the Best Paper Award from IJCAI-BOOM’16 workshop. He is the recipient of the 2014 ASU President Award for Innovation, and Atluri Award from IEEE Foundation. His work has been featured in several news media, including MIT Technology Review, Fast Company, New Scientist, and others. His research is generously funded by federal and industrial sponsors such as DARPA, NSF, Apple and Alibaba.

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Free

CESG Fishbowl Teleseminar: Security and Privacy in the IoT.

November 9 @ 2:30 pm - 3:30 pm

“Security and Privacy in the IoT” Dr. Elisa Bertino, Purdue University   Abstract: The Internet of Things (IoT) paradigm refers to the network of physical objects or “things” embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. IoT will usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g. SmartGrid), to healthcare management and urban life (e.g. SmartCity). However, because of its fine-grained, continuous and pervasive data acquisition and control capabilities, IoT raises concerns about security and privacy. Deploying existing security solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in IoT security and privacy, we Biography: Elisa Bertino is the Samuel Conte term professor of computer science at Purdue University, and serves as Director of the CyberSpace Security Lab (Cyber2SLab). Prior to joining Purdue in 2004, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory (now Almaden) in San Jose, at the Microelectronics and Computer Technology Corporation, at Rutgers University Her recent research focuses on database security, digital identity management, policy systems, and security for web services. She is a Fellow of ACM, of IEEE, and AAAS. She received the IEEE Computer Society 2002 Technical Achievement Award, the IEEE Computer Society 2005 Kanai Award, and the ACM SIGSAC Outstanding Contributions Award. She is currently serving as EiC of IEEE Transactions on Dependable and Secure Computing and as associate EiC of IEEE Computer.

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Free

CESG Seminar: Throughput-Optimal Scheduling for Multi-Hop Networked Transportation Systems with Switch-Over Delay

November 3 @ 4:10 am - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

Dr. I-Hong Hou, Assistant Professor in the ECE, Texas A& M University Abstract: The emerging connected-vehicle technology provides a new dimension for developing more intelligent traffic control algorithms for signalized intersections. An important challenge for scheduling in networked transportation systems is the switchover delay caused by the guard time before any traffic signal change. The switch-over delay can result in significant loss of system capacity and hence needs to be accommodated in the scheduling design. To tackle this challenge, this talk discusses a distributed online scheduling policy that extends the well-known Max-Pressure policy to address switch-over delay by introducing a bias factor favoring the current schedule. We prove that the proposed policy is throughput-optimal with switch-over delay. Furthermore, the proposed policy remains optimal when there are both connected signalized intersections and conventional fixed-time ones in the system. With connected-vehicle technology, the proposed policy can be easily incorporated into the current transportation systems without additional infrastructure.  Biography: Dr. I-Hong Hou is an assistant professor in the ECE Department of the Texas A&M University. He received his Ph.D. from the Computer Science Department of the University of Illinois at Urbana-Champaign. His research interests include wireless networks, real-time systems, and cloud computing. His work received the Best Paper Award from ACM MobiHoc 2017, and Best Student Paper Award from WiOpt 2017. He received the C.W. Gear Outstanding Graduate Student Award from the University of Illinois at Urbana-Champaign, and the Silver Prize in the Asian Pacific Mathematics Olympiad.

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October 2017
Free

CESG Seminar: “Knowledge-driven representations of physiological signals: Developing measurable indices of non-observable behavior”

October 27 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

Dr. Theodora Chaspari, Assistant Professor, Computer Science & Engineering, Texas A & M University Abstract:  Recent converging advances in sensing and computing, including wearable technologies, allow the unobtrusive long-term tracking of individuals yielding rich multimodal signal measurements from real-life. In this thesis, we will present the development of data-scientific and context-rich bio-behavioral approaches for analyzing, quantifying, and interpreting these bio-behavioral signals. We propose a novel knowledge-driven signal representation framework able to efficiently handle the large volume of acquired data and the noisy signal measurements. Our approach involves the use of sparse approximation techniques and the design of signal-specific dictionaries learned through Bayesian methods, outperforming previously proposed models in terms of signal reconstruction and information retrieval criteria. We further focus on translating the derived signal representations into novel intuitive quantitative measures analyzed with probabilistic and statistical models in relation to external factors of observable behavior. This work has found applications in Autism intervention for detecting beneficial regulation mechanisms during child-therapist interactions, as well as in the family studies domain for identifying instances of emotional escalation and interpersonal conflict. These are discussed in relation to designing human-assistive personalized bio-feedback systems able to promote healthy routines, increase emotional wellness and awareness, and empower clinical assessment and intervention. Biography: Dr. Theodora Chaspari is an Assistant Professor at the Computer Science & Engineering Department in Texas A&M University. She has received her diploma (2010) in Electrical and Computer Engineering from the National Technical University of Athens, Greece and a Master of Science (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California. Between 2010 and 2017, she had been working as a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC. She has also been a Lab Associate Intern at Disney Research (summer 2015). Dr Chaspari’s research interests lie in the areas of biomedical signal processing, human-computer interaction, behavioral signal processing, data science, and machine learning. She is a recipient of the USC Annenberg Graduate Fellowship, USC Women in Science and Engineering Merit Fellowship, and the IEEE Signal Processing Society Travel Grant.

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Free

CESG FISHBOWL TELESEMINAR: “Engineering High-Quality Immersive Virtual Reality on Today’s Mobile Devices”

October 26 @ 2:30 pm

Title: “Engineering High-Quality Immersive Virtual Reality on Today’s Mobile Devices” Abstract: Today’s high-resolution VR apps can only run on special VR hardware gears (tethered or the upcoming 60Ghz-based wireless). As a result, the VR industry has been stagnating due to a painful “chicken-and-egg” dilemma: there is a lack of VR content/apps due to limited VR hardware gears sold (about one million units), but VR hardware sale is stagnating because of the lack of VR content/apps in the market.We envision the most promising way to break away from this dilemma is to enable high-resolution VR apps to run on commodity mobile devices such as the billion smartphones. To realize the vision, we have embarked on a course of R&D that aims to: (1) understand the fundamental technological (hardware) challenges in solving the problem (today and in the near future), and (2) explore software innovations to circumvent the hardware constraints. In preliminary work, we have developed a new VR rendering architecture that supports single-player high-resolution VR apps on Google Pixel XL, over 802.11ac, with under 14ms latency and 60 FPS.   Biography: Dr. Y. Charlie Hu is a Professor of Electrical and Computer Engineering and Computer Science (by courtesy), and an ACM Distinguished Scientist and IEEE Fellow. He received his Ph.D. in Computer Science from Harvard in 1997. He is a recipient of the Honda Initiation Grant Award, NSF CAREER Award, Purdue University Faculty Scholar, Purdue CoE Early Career Research Award, and EuroSys Best Student Paper Award. His research interests include mobile systems, distributed systems, and computer networks. He served as a general co-chair of ACM SIGCOMM 2014, and PC co-chair of ACM MobiCom 2016 and ACM SIGOPS EuroSys 2018. Since 2010, his group has conducted pioneering work on energy profiling and energy debugging on smartphones which has been widely covered by news media such as ABC News, NBC News, BBC, Times of India, MIT Tech Review, and Scientific American. The technology has been commercialized and is extending the battery life of hundreds of millions of smartphones.

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Free

CESG Seminar: “Model Checking Asynchronous Systems: A New Technique and a Practical Tool”

October 20 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

Dr. Jeff Huang, Assistant Professor , Computer Science & Engineering, Texas A & M University Abstract:  Model checking is a state-of-the-art (and probably the most practical) approach for formal verification of safety- and security-critical systems. Unfortunately, despite more than three decades of research, it remains challenging to model check real-world asynchronous systems due to the infamous state-explosion problem caused by system-level asynchrony. In this talk, I will describe a recent advance in model checking, namely “maximal causality reduction”, developed by my group over the last three years. By reducing the state space exploration to embarrassingly-parallel constraint satisfaction problems and by eliminating redundant state space explorations, maximal causality reduction makes model checking dramatically more scalable than existing techniques such as bounded model checking and partial order reduction. We have developed a practical model checking tool based on this technique, and have applied it on a variety of asynchronous systems including real-world web servers and browsers and found tens of serious bugs in these systems. Recently, we have also released our tool as open source and publicly available on Github. Biography: Dr. Jeff Huang an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on developing techniques and tools for improving performance, safety and security of complex software systems based on fundamental program analyses and programming language theory. His research has won awards including ACM SIGPLAN PLDI Distinguished Paper Award, SIGPLAN Research Highlights, ACM SIGSOFT Outstanding Dissertation Award, Google Faculty Research Award, and NSF CAREER Award.  

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Free

CESG FISHBOWL SEMINAR: “Integrating robots in cognitive solutions: application studies and system architecture implications”

October 19 @ 2:30 pm - 3:30 pm

Title: “Integrating robots in cognitive solutions: application studies and system architecture implications” Abstract: As cognitive systems have become increasingly capable, cloud-based services have enabled the integration of conversational robotic agents and human agents into into cognitive solutions. These robots are sophisticated mobile sensor platforms and compelling dialog agents. Cognitive solutions involving robots offer a rich domain for computing systems research. This talk will focus on research using conversational robotic agents for wellness and tracking in an aging-in-place lab and on individualized robotic dialog systems. System architectures, data services, and memory and compute optimizations to support the robotic agents will be presented.   Biography: Dr. Kevin Nowka is the Director of IBM Research – Austin, one of IBM’s 12 global research laboratories. He leads a team of scientists and engineers working on optimized systems for big-data and analytics, cognitive computing systems, cloud infrastructure, and energy-efficient systems and datacenters. He is also IBM Senior State Executive for Texas responsible for government, community, and university relations in Texas. He received a B.S. degree in Computer Engineering from Iowa State University, Ames, in 1986 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1988 and 1995, respectively. He has 79 US issued patents and has published over 70 technical papers on circuits, systems and processor design, and technology issues. He is an IBM Master Inventor and a member of the IBM Academy of Technology. Dr. Nowka is also an adjunct professor at Texas A&M University in the Electrical and Computer Engineering Department, is a member of the Texas Science and Engineering Fair Advisory Board, serves as Vice Chair of the Grace Academy Board of Trustees, and is a member of the TEES Advisory Board.

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Free

CESG Seminar: “Embracing Sparsity in Deep Networks: From Algorithms to Hardware”

October 13 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

Abstract: In this talk, I will present how the sparsity, a desirable property for both algorithms and hardware design, could be discovered, exploited, and enhanced in the context of deep networks. I will first introduce how an iterative sparse solver could be linked to and tuned as a feed-forward deep network, using the “unfolding then truncating” trick. Next, I will show how a double sparse dictionary structure could be naturally utilized to sparsify the weights of the obtained networks resulting in a new deep network whose feature and parameter spaces are simultaneously sparsified. I will then describe PredictiveNet, a recent work by one of my collaborators, which predicts the zero hidden activations of the nonlinear CNN layers at low costs thereby skipping a large fraction of convolutions in CNNs at runtime without modifying the CNN structure or requiring additional branch networks. Such an energy-efficient hardware implementation could be seamlessly integrated with the theory bridging sparse optimization and deep learning, potentially leading to even larger energy savings. Bio: Dr. Zhangyang (Atlas) Wang is an Assistant Professor of Computer Science and Engineering (CSE), at the Texas A&M University (TAMU), since August 2017. He is currently leading a research group of 5 Ph.D. students and 3 M.S. students and has several projects supported by DARPA, Adobe, etc. During 2012-2016, he was a Ph.D. student in the Electrical and Computer Engineering (ECE) Department, at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Thomas S. Huang. Prior to that, he obtained the B.E. degree at the University of Science and Technology of China (USTC) in 2012. Dr. Wang’s research has been addressing machine learning, computer vision and multimedia signal processing problems using advanced feature learning and optimization techniques. He has co-authored 40 papers, and published several books and chapters. He has been granted 3 patents, and has received over 15 research awards and scholarships. His research has been covered by worldwide media such as BBC, Fortune, International Business Times, UIUC news and alumni magazine. More can be found at: http://www.atlaswang.com.

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