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Conferences, Lectures, & Seminars
Events for September
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Architecting More Power-Efficient Datacenters By Removing the Peaks
Thu, Sep 01, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dean Tullsen, University of California, San Diego
Talk Title: Architecting More Power-Efficient Datacenters By Removing the Peaks
Series: EE 598 Computer Engineering Seminar Series
Abstract: Datacenters are rapidly increasing in size and computational ability. However, this growth places great stress on the power delivery and heat removal of the datacenter. The cost of power and cooling, and the computational capacity of the datacenter, are both driven by the peak demands on the power infrastructure and the cooling infrastructure, even though most datacenters see large differences between the peak demand and the average demand. We will discuss two technologies that enable the datacenter to service the peak computational demand, yet present the power and cooling infrastructure with a flat profile that hides the peaks, without sacrificing peak-period performance. We make use of batteries and phase-change materials (e.g., wax) for these optimizations.
Biography: Dean Tullsen is a professor and chair of the computer science and engineering department at University of California, San Diego. He received his PhD from the University of Washington in 1996, where he introduced simultaneous multithreading (hyper-threading). He has continued to work in the area of computer architecture and back-end compilation, where with various co-authors he has introduced many new ideas to the research community, including threaded multipath execution, symbiotic job scheduling for multithreaded processors, dynamic critical path prediction, speculative precomputation, heterogeneous multi-core architectures, conjoined core architectures, event-driven simultaneous code optimization, and data triggered threads. He is a Fellow of the ACM and the IEEE. He has twice won the Influential ISCA Paper Award. He is chair of the IEEE Technical Committee on Computer Architecture.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Seminar -
Fri, Sep 02, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Odette Scharenborg, Radboud University (Nijmegen, the Netherlands)
Talk Title: Modeling and Understanding Human Spoken-Word Recognition
Abstract: The question that underlies most of my research is the question why humans are so much better at recognizing speech than computers. I have approached this question from several angles, from the field of automatic speech recognition, the field of psycholinguistics, and through the combination of the two, i.e., the computational modeling of human spoken-word recognition. In this talk, I will present results from my computational modelling and psycholinguistics work.
In the first part, I will present my computational model, which is able to recognize real speech, Fine-Tracker. Fine-Tracker was specifically developed to account for the accumulating evidence that subtle phonetic detail in the speech signal is important in human spoken-word recognition. I will explain the model and illustrate its modelling ability by presenting a simulation study investigating the role of durational information in resolving temporary ambiguity due to lexical embedding (i.e., 'ham' in the longer word 'hamster') to aid spoken-word recognition. I will start the talk by briefly discussing the value of computational modelling in spoken-word recognition.
In the second part of this talk, I will focus on the results obtained in my current project on human non-native word recognition in noise. Most people will have noticed that communication in the presence of background noise is more difficult in a non-native than in the native language - even for those who have a high proficiency in the non-native language involved. The aim of this project is to understand the effect of background noise on the processes underlying non-native spoken-word recognition. In this presentation, I will present recent results on the effect of background noise on 1) the flexibility of the perceptual system in non-native listening; 2) the multiple activation, competition and recognition processes in non-native spoken-word recognition.
Biography: Odette Scharenborg (PhD) is an associate professor at the Centre for Language Studies and a research fellow at the Donders Institute for Brain, Cognition, and Behaviour, Radboud University (Nijmegen, the Netherlands). Her research interests focus on narrowing the gap between automatic and human word recognition. In 2008, she co-organized the Interspeech 2008 Consonant Challenge, which aimed at promoting comparisons of human and machine speech recognition in noise in order to investigate where the human advantage in word recognition originates. She was one of the initiators of the EU funded Marie Curie Initial Training Network 'Investigating Speech Processing In Realistic Environments' (INSPIRE, 2012-2015). Her current project is funded by a fellowship from the Netherlands Organisation for Scientific Research on
the topic of human non-native word recognition in noise, which will be investigated using a combination of listening experiments and computational modelling.
Host: Prof. Shrikanth Narayanan & Prof. Panayiotis Georgiou
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Surface-based Methods for Analyzing Brain Structure and Connectivity
Fri, Sep 02, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Boris Gutman, Ph.D., Keck School of Medicine, University of Southern California
Talk Title: Surface-based Methods for Analyzing Brain Structure and Connectivity
Series: Medical Imaging Seminar Series
Abstract: In this talk I will describe several shape centric methods for analyzing brain MR image data. The first part will focus on surface-based analysis of structural MRI. I will suggest some parametric registration techniques, with particular focus on adapting traditional image registration algorithms to the spherical domain. Building on this, an alternative shape space will be proposed, extending the Ebin metric on the 2 sphere to a Riemannian product metric for simple closed surfaces.
The second part of the talk will offer a method to combine surface representations and diffusion MRI based connectivity analysis. We will propose a generative model of structural connectivity based on the Poisson point process. Treating each tractography fiber model as a point observation in the continuous brain product space, we estimate the spatially distributed Poisson parameter to represent cortical connectivity. We can then adapt traditional spatial domain tasks such as registration and segmentation based on this continuous connectivity representation. Example adaptations will be proposed.
Example applications to the study of genetics and disease will be shown throughout, with some special focus on Partial Least Squares modeling as an alternative to the traditional genome wide association study (GWAS).
Biography: Boris Gutman is a Post-doctoral Scholar at the Imaging Genetics Center within the Stevens Institute for Neuroimaging and Informatics at the University of Southern California. His current research interests include biomedical shape analysis, brain connectivity and imaging genetics, with the goal of enabling new discoveries of genetic associations and disease effects in the human brain.
Host: Professor Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Invariant Inference for Program Specification and Verification
Thu, Sep 08, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Todd Millstein, University of California, Los Angeles
Talk Title: Invariant Inference for Program Specification and Verification
Series: EE 598 Computer Engineering Seminar Series
Abstract: Why isn't software verification technology in common use today? One reason is that, despite decades of foundational and practical advances, verification is still too costly in terms of human time and effort. I'll describe my recent research with colleagues to address two of the most onerous parts of the software verification process: creating a high-quality specification, and identifying the inductive program invariants that form the key lemmas in a proof of software correctness. Our research supports both tasks through a new form of automatic invariant inference that is both more expressive and less burdensome than prior techniques.
We extend the data-driven approach to invariant inference, whereby program invariants are learned from a set of test executions. This approach is appealingly general, as it naturally handles arbitrarily complex code and specifications. However, prior data-driven techniques have required the user to provide a fixed set of "features" as input, which are atomic predicates that define the search space of possible invariants. If these features are insufficient, invariant inference will either fail or produce an incorrect result. In contrast, we introduce a technique for on-demand feature learning, which automatically expands the search space of candidate invariants in a targeted manner on demand. Our approach eliminates the problem of feature selection and guarantees that inferred invariants are consistent with the given tests. We have used our technique both to infer rich specifications for black-box code and to infer provably correct loop invariants as part of an automatic program verifier.
Joint work with Saswat Padhi (UCLA) and Rahul Sharma (Stanford).
Biography: Todd Millstein is a Professor in the Computer Science Department at the University of California, Los Angeles. His research interests are broadly in programming languages and software verification. Todd received his Ph.D. and M.S. from the University of Washington and his A.B. from Brown University, all in Computer Science. Todd received an NSF CAREER award in 2006, an IBM Faculty Award in 2008, an ACM SIGPLAN Most Influential PLDI Paper Award in 2011, an IEEE Micro Top Picks selection in 2012, the Northrop Grumman Excellence in Teaching Award from UCLA Engineering in 2016, and a Microsoft Research Outstanding Collaborator Award in 2016.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Computer Engineering Seminar
Thu, Sep 08, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Todd Millstein, Professor, University of California, Los Angeles
Talk Title: Invariant Inference for Program Specification and Verification
Abstract: Why isn't software verification technology in common use today? One reason is that, despite decades of foundational and practical advances, verification is still too costly in terms of human time and effort. I'll describe my recent research with colleagues to address two of the most onerous parts of the software verification process: creating a high-quality specification, and identifying the inductive program invariants that form the key lemmas in a proof of software correctness. Our research supports both tasks through a new form of automatic invariant inference that is both more expressive and less burdensome than prior techniques.
We extend the data-driven approach to invariant inference, whereby program invariants are learned from a set of test executions. This approach is appealingly general, as it naturally handles arbitrarily complex code and specifications. However, prior data-driven techniques have required the user to provide a fixed set of "features" as input, which are atomic predicates that define the search space of possible invariants. If these features are insufficient, invariant inference will either fail or produce an incorrect result. In contrast, we introduce a technique for on-demand feature learning, which automatically expands the search space of candidate invariants in a targeted manner on demand. Our approach eliminates the problem of feature selection and guarantees that inferred invariants are consistent with the given tests. We have used our technique both to infer rich specifications for black-box code and to infer provably correct loop invariants as part of an automatic program verifier.
Biography: Todd Millstein is a Professor in the Computer Science Department at the University of California, Los Angeles. His research interests are broadly in programming languages and software verification. Todd received his Ph.D. and M.S. from the University of Washington and his A.B. from Brown University, all in Computer Science. Todd received an NSF CAREER award in 2006, an IBM Faculty Award in 2008, an ACM SIGPLAN Most Influential PLDI Paper Award in 2011, an IEEE Micro Top Picks selection in 2012, the Northrop Grumman Excellence in Teaching Award from UCLA Engineering in 2016, and a Microsoft Research Outstanding Collaborator Award in 2016.
Host: Xuehai Qian
Location: Olin Hall of Engineering (OHE) - OHE 100D
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Sep 09, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Sunil Bhave, Purdue University
Talk Title: Lithium Niobate MEMS Resonators for RF, Photonics and Opto-mechanics
Host: Prof. Hossein Hashemi, Prof. Mike Chen, and Prof. Mahta Moghaddam. Sponsored by the Ming Hsieh Institute.
More Information: Ming Hsieh Inst Seminar on IS - Sunil_Bhave_Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Cyber-Physical Systems Seminar Series
Mon, Sep 12, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yasser Shoukry, Postdoctoral Scholar, UCLA/UC Berkeley /UPenn
Talk Title: Secure State Estimation For Cyber Physical Systems Under Sensor Attacks: A Satisfiability Modulo Theory Approach
Abstract: Motivated by the need to secure critical infrastructure against sensor attacks, in this talk I will focus on a problem known as "secure state estimation". It consists of estimating the state of a dynamical system when a subset of its sensors is arbitrarily corrupted by an adversary. Although of critical importance, this problem is combinatorial in nature since the subset of attacked sensors in unknown. Previous work in this area can be classified into two broad categories. The first category is based on numerical optimization techniques. These techniques are well suited to handle the continuous part of the problem, estimating the real-valued variable describing the state, if the combinatorial part of the problem has been solved. The second category is based on Boolean reasoning, which is well suited to handle the combinatorial part of the problem, if the continuous part of the problem has been solved. However, since we need to simultaneously solve the combinatorial and the continuous part of the secure state estimation problem, the existing approaches result in algorithms with worst case exponential time complexity.
In this talk, I will present a novel and efficient algorithm for the secure state estimation problem that uses the lazy SMT approach in order to combine the power of both SAT solving as well as convex optimization. While SAT solving is used to perform the combinatorial search, convex optimization techniques are used to reason more efficiently about the real-valued state of the system and/or generating theory lemmas explaining conflicts in the combinatorial search. We show that by splitting the reasoning between the two domains (Booleans and Reals) and intermixing a powerful tool from each domain, we obtain a new suite of tools that scales more favorably compared to the previous techniques. I will start by discussing the simplest case when the underlying dynamics are linear, sensors are perfect (noiseless), and only data collected over a finite window is considered. I will then move forward by showing several extensions to handle noisy measurements, recursive implementations (data over infinite windows) and nonlinear dynamics.
Biography: Yasser Shoukry is a Postdoctoral Scholar at the EECS Department at UC Berkeley, the EE Department at UCLA and the ESE Department at UPenn. He received the Ph.D. in Electrical Engineering from UCLA in 2015 where he was affiliated with both the Cyber-Physical Systems Lab as well as the Networked and Embedded Systems Lab. Before joining UCLA, he spent four years as an R&D engineer in the industry of automotive embedded systems. His research interests include the design and implementation of secure- and privacy- aware cyber-physical systems by drawing on tools from embedded systems, control and optimization theory, and formal methods.
Dr. Shoukry is the recipient of the Best Paper Award from the International Conference on Cyber-Physical Systems (ICCPS) in 2016. He is also the recipient of the UCLA EE Distinguished PhD Dissertation Award in 2016, the UCLA Chancellor's prize in 2011 and 2012, UCLA EE Graduate Division Fellowship in 2011 and 2012, and the UCLA EE Preliminary Exam Fellowship in 2012. In 2015, Dr. Shoukry led the UCLA/Caltech/CMU team to win the first place in the NSF Early Career Investigators (NSF-ECI) research challenge. His team represented the NSF-ECI in the NIST Global Cities Technology Challenge, an initiative designed to advance the deployment of Internet of Things (IoT) technologies within a smart city.
Host: Paul Bogdan
Location: 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar
Thu, Sep 15, 2016 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Terence D. Sanger, MD PhD, Depts of Biomedical Engineering, Biokinesiology, and Child Neurology/USC
Talk Title: A Bayesian nonlinear filter and a stochastic nonlinear control algorithm suitable for estimation and control by populations of spiking neurons
Abstract: The best-known examples of Bayesian nonlinear filters are the Kushner and Zakai equations which unfortunately have limited applicability to important classes of real-world problems. I derive a general nonlinear filter with broad applicability that can be shown to integrate to Bayes' rule over short time intervals. The filter extracts maximal information per unit time, in the sense that the rate of decrease of the entropy of the estimate is equal to the mutual information between the state and the observation. I show that this filter has a straightforward parallel implementation, and I show an efficient representation using Poisson-distributed spiking neurons.
I then show that this technique can be extended to a class of stochastic nonlinear controllers. These controllers extend linear feedback controllers and permit control of systems with non-Gaussian noise or state uncertainty, asymmetric cost or perturbations, or state measurements that are not characterized by additive Gaussian noise. The theory is based on Stochastic Dynamic Operators (SDOs) in which the fundamental signals used for feedback are not estimates of state, but estimates of the probability distribution of state. This allows control to vary depending on the degree of state uncertainty (eg: one might drive more slowly if visibility is poor). The reference signal used for control is not a desired time-varying reference state, but a time-varying cost function that assign a value to every potential state. Such cost functions can represent asymmetric penalties and discontinuities in cost (eg: a cliff to one side of a road). Feedback control uses Bayesian statistics to combine the uncertain state estimate (from a nonlinear filter) and the time-varying cost function to produce an estimated motor command. The command is the solution to a short-term optimization problem. As with the Bayesian nonlinear filter, populations of spiking neurons provide a good representation for SDOs and an efficient control algorithm. I will show a real-time implementation of a feedback controller for a desktop robot arm using a population of 900 simulated spiking neurons that tracks the desired minimum cost and stably resists perturbations.
Biography: Terry Sanger holds an SM in Applied mathematics (Harvard), PhD in Electrical Engineering and Computer Science (MIT), and MD (Harvard), with medical specialization in Child Neurology and Movement Disorders. He is currently Associate Professor of Biomedical Engineering, Neurology, and Biokinesiology, and he is the director of the Pediatric Movement Disorders Clinic at Childrens Hospital of Los Angeles, and the Health Technology and Engineering program at USC (HTE@USC).
His research on disorders of developmental motor control is driven by his interest in finding new treatments for children with movement disorders including dystonia, chorea, spasticity, and dyspraxia. He has a particular interest in computational motor learning, and the role of motor learning in recovery from childhood brain injury. Major focus areas of laboratory research include wearable devices to promote motor learning, EMG-driven communication devices and assistive prosthetics, and modeling of the electrophysiology of deep-brain stimulation. Personal involvement in motor control and motor learning includes snowboarding, jazz and classical piano, bluegrass banjo, and ballroom dance with particular focus on Argentine Tango.
Host: Professor Sandeep K. Gupta, sandeep@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Computer Engineering Seminar
Thu, Sep 15, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rajiv Gupta, University of California, Riverside
Talk Title: Parallel Graph Processing on GPUs, Clusters, and Multicores
Abstract: The importance of iterative graph algorithms has grown due to their widespread use in graph mining and analytics. Although computations on graphs with millions of nodes and edges contain vast amounts of data level parallelism, exploiting this parallelism is challenging due to the highly irregular nature of real-world graphs. In this talk I will present our recent results that greatly improve the SIMD-efficiency, communication efficiency, and I/O efficiency of graph processing on GPUs, a cluster, and a single multicore machine. In comparison to prior techniques, our Warp Segmentation technique achieves 1.3x-2.8x performance improvement on a single GPU, our Vertex Refinement technique achieves 2.7x performance improvement on a multi-GPU system, our Relaxed Consistency protocol achieves 2.3x performance improvement on a 16-node cluster, and our Dynamic Shards I/O optimization achieves up to 2.8x performance improvement on a single multicore machine.
Biography: Rajiv is a Professor of Computer Science at the University of California, Riverside. His research interests include Compilers, Architectures, and Runtimes for Parallel Systems. He has supervised PhD dissertations of 28 students including two winners of ACM SIGPLAN Outstanding Doctoral Dissertation Award. Papers coauthored by Rajiv with his students have been selected for: inclusion in 20 Years of PLDI (1979-1999), a best paper award in PACT 2010, and a distinguished paper award in ICSE 2003. Rajiv is a Fellow of the ACM, IEEE, and AAAS. He received the National Science Foundation's Presidential Young Investigator Award and UCR Doctoral Dissertation Advisor/Mentor Award. He has chaired several major conferences including FCRC, PLDI, HPCA, ASPLOS, CGO, CC, HiPEAC, and LCTES. He serves on the Editorial Boards of ACM Transactions on Architecture and Code Optimization and Parallel Computing journal. Rajiv served as a member of a technical advisory group on networking and information technology created by the PCAST.
Host: Xuehai Qian
Location: Olin Hall of Engineering (OHE) - OHE 100D
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Munushian Visiting Seminar Series
Fri, Sep 16, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Grant Willson, Rashid Engineering Regent Chair, UT Austin
Talk Title: Polymers for High Resolution Imaging Applications
Series: Munushian Seminar Series
Abstract: There has been a continuing and nearly frantic effort on the part of the microelectronics manufacturers over the past
several decades to make smaller and smaller devices. Companies that cannot keep pace with these advances quickly disappear
from the market place and sadly many with famous names like Siemens, Motorola and Sony have fallen by the wayside.
Photolithography, the process that has enabled the production of all of today's microelectronic devices has now reached physical
limits. Efforts to push that technology to provide still higher resolution by the historical paths of exposure wave length reduction,
increasing the numerical aperture of the projection lens and reduction in the Raleigh constant have been abandoned. Is this the
end? Can device scaling continue??
Various incredibly clever tricks based on chemical engineering principles have been devised that extend the resolution limits of
photolithography, some of which are already in use in full scale manufacturing. One promising approach for future generations
of devices is based on the "directed self-assembly" of block co-polymers. We have worked to design block co-polymers that are
optimized for this application. Doing so requires incorporation of blocks with very high interaction parameters (X) and for some
applications, incorporation of silicon into one of the blocks. Polymers of this sort form very small structures. We have now
demonstrated well resolved 50 Angstrom wide lines and spaces. Aligning the structures and orienting them in a way that is useful
for microelectronics is a challenge as is development of processes for transfer of such small patterns into substrates that are useful
for device fabrication. A progress report on these efforts will be presented.
Biography: Dr. Grant Willson is a Professor of Chemical Engineering and Chemistry at the
University of Texas at Austin where he holds the Rashid Engineering Regent's Chair. He received both his B.S. and Ph.D. in organic chemistry from the University of California, Berkeley and his M.S., in organic chemistry, from San Diego State University. He joined the faculty of the University of Texas at Austin in 1993. Prior to joining the university, Dr. Willson worked at IBM for 17 years as an IBM Fellow and Manager of the Polymer Science and Technology area at the IBM Almaden Research Center in San Jose, CA. He joined IBM after serving on the faculties of California State University, Long Beach and the University of California, San Diego. Dr. Willson is the co-inventor
of more than 40 issues U.S. patents and co-author of more than 400 publications.
Dr. Willson's research work is focused on the design and synthesis of functional organic materials with emphasis on organic materials for microelectronics. His work is supported by grants from both government and industry. His research group includes graduate and undergraduate students
enrolled in both the Chemistry and Chemical Engineering Departments. He was a cofounder of Molecular Imprints, Inc., an Austin firm that employed more than 100 people and was very recently acquired by Canon.
Host: EE Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Cyber-Physical Systems Seminar Series
Mon, Sep 19, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yanzhi Wang, Syracuse University
Talk Title: Deep Neural Network and Deep Reinforcement Learning: Ultra-Low Energy Implementation and Broad Applications
Abstract: Recently, deep convolutional neural networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in a variety of tasks. There is a timely need to map the latest software-based DCNNs to application-specific hardware, in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness. Stochastic computing (SC), as a low-cost alternative to the conventional binary computing paradigm, has the potential to enable massive parallel and highly scalable hardware implementation of DCNNs. The first part of my presentation is a holistic design and optimization framework of SC-based DCNN systems from key arithmetic operations, function blocks, feature extraction blocks, to the overall LeNet5 structure, achieving ultra-low hardware footprint and energy consumption.
Deep reinforcement learning (DRL) has been recently invented and has been successfully utilized in AlphaGo, game playing, etc. Deep reinforcement learning has the potential of control of complicated systems with high state and action spaces (which cannot be achieved by traditional reinforcement learning techniques), thereby resulting in very wide application domains. The second part of my presentation first provides a formal statement of the DRL framework. Effective hardware implementation of the DRL framework, which is critical in the embedded control systems and IoTs, will be investigated. The more broad applications of the emerging technique will be discussed with sample examples on cloud computing and smart grid applications. Open questions and future directions will be finally presented.
Finally I will briefly present the recent work on Luminescent Solar Concentrator-based PV cells and application on electric vehicles, which is transparent and flexible and fits the streamlined surface and aesthetic requirement of modern vehicles. The proposed system can help propel the vehicle or charge the vehicle whenever solar energy is available.
Biography: Yanzhi Wang is currently an Assistant Professor at Syracuse University, starting from August 2015. He received B.S. degree from Tsinghua University in 2009 and Ph.D. degree from University of Southern California in 2014, under supervision of Prof. Massoud Pedram. His research interests include low-power circuit and systems design, neuromorphic computing, embedded systems and wearable devices, etc. He has received best paper awards from International Symposium on Low Power Electronics Design 2014, International Symposium on VLSI Designs 2014, top paper award from IEEE Cloud Computing Conference 2014. He has two popular papers in IEEE Trans. on CAD. He has received multiple best paper nominations from ACM Great Lakes Symposium on VLSI, IEEE Trans. on CAD, and Asia and South Pacific Design Automation Conference.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CommNetS seminar
Wed, Sep 21, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Insoon Yang, USC
Talk Title: Learning, Incentives and Optimization for Human-Energy System Interaction
Series: CommNetS
Abstract: With the advances in Cyber-Physical Systems (CPS) and the Internet of Things (IoT) technologies, sensor and communication networks and computing elements are pervasive in many modern infrastructures that affect our daily lives. However, sustainable interactions between human users and CPS or IoT are not guaranteed unless there is an appropriate coordination mechanism for them. Specifically, on one hand we can customize the operation of these systems by learning user behaviors and preferences. On the other hand, we can incentivize human users to cooperate for the system operation. Such feedback loops between human users and CPS can improve large-scale critical infrastructure systems with suitable optimization techniques.
In this talk, I will present learning, incentive, and optimization tools that support interactions between human users and modern energy systems, which is an important class of CPS- and IoT-enabled infrastructure systems. The first tool, called the utility learning model predictive control, provides a way to learn quasi-periodic user behaviors and preferences using Gaussian processes to optimize the operation of personal electric loads such as HVAC systems and Electric Vehicles. Second, I will talk about contracts that can incentivize customers to provide useful services to the power grids with the aid of automated demand response technology that automatically controls the customers' loads. In the last part of this talk, we will discuss resource allocation problems in power networks associated with these CPS- and IoT-based technologies as well as customer targeting to maximize the social welfare and identify the submodularity structure that justifies the use of greedy algorithms providing (1-1/e)-optimal solutions.
Biography: Insoon Yang is an Assistant Professor of Electrical Engineering at USC. He received B.S. degrees in Mathematics and in Mechanical Engineering (summa cum laude) from Seoul National University in 2009; and an M.S. in EECS, an M.A. in Mathematics and a Ph.D. in EECS from UC Berkeley in 2012, 2013 and 2015, respectively. Before joining USC, he was a Postdoctoral Associate at the Laboratory for Information and Decision Systems at MIT. Insoon's research interests are in stochastic control, optimization in systems and control, and energy and power systems. He currently focuses on control methods, risk management solutions and incentive mechanisms that support interactions between human users and CPS- or IoT-enabled systems with limited information. He is a recipient of the 2015 Eli Jury Award.
Host: Prof. Ashutosh Nayyar
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Engineering Challenges in Next Generation Neurosurgery
Thu, Sep 22, 2016 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Aaron E Bond, M.D., Ph.D., University of Virginia Health System, Charlottesville, VA
Talk Title: Engineering Challenges in Next Generation Neurosurgery
Host: P. Daniel Dapkus
More Information: Aaron Bond Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Jenny Lin
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Computer Engineering Seminar
Thu, Sep 22, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rajeev Alur, Professor, University of Pennsylvania
Talk Title: Quantitative Policies over Streaming Data
Abstract: Decision making in cyber-physical systems often requires dynamic monitoring of a data stream to compute performance-related quantitative properties. We propose StreamQRE as a high-level declarative language for modular specifications of such quantitative policies. This language is rooted in the emerging theory of regular functions, and every policy described in this language can be compiled into a space-efficient streaming implementation. We describe a prototype system that is integrated within an SDN controller and show how it can be used to specify and enforce dynamic updates for traffic engineering as well as in response to security threats. We conclude by outlining the rich opportunities for both theoretical investigations and practical systems for real-time decision making in IoT applications.
This talk is based on recent and ongoing work with Penn researchers Dana Fisman, Sanjeev Khanna, Boon Thau Loo, Kostas Mamouras, Mukund Raghothaman, and Yifei Yuan.
Biography: Rajeev Alur is Zisman Family Professor of Computer and Information Science at University of Pennsylvania. He obtained his bachelor's degree in computer science from IIT Kanpur in 1987 and PhD in computer science from Stanford University in 1991. Before joining Penn in 1997, he was with Computing Science Research Center at Bell Labs. His research is focused on formal methods for system design, and spans theoretical computer science, software verification and synthesis, and cyber-physical systems. He is a Fellow of the ACM, a Fellow of the IEEE, an Alfred P. Sloan Faculty Fellow, and a Simons Investigator. He was awarded the inaugural CAV (Computer-Aided Verification) award in 2008, ACM/IEEE Logic in Computer Science (LICS) Test-of-Time award in 2010 and the inaugural Alonzo Church award by ACM SIGLOG / EATCS / EACSL in 2016 for his work on timed automata. Prof. Alur has served as the chair of ACM SIGBED (Special Interest Group on Embedded Systems), and as the general chair of LICS. He is the author of the textbook Principles of Cyber-Physical Systems (MIT Press, 2015), and is currently the lead PI of the NSF Expeditions in Computing center ExCAPE (Expeditions in Computer Augmented Program Engineering).
Host: Xuehai Qian
Location: Olin Hall of Engineering (OHE) - OHE 100D
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Cyber-Physical Systems Seminar Series
Mon, Sep 26, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yuankun Xue, University of Southern California
Talk Title: Data-Centers-on-a-Chip as Enablers for Cyber-Physical Systems: A Scalable Model of Computation Guiding the Design Methodologies of Network-on-Chip based Manycore Platforms
Abstract: Synergistic coupling of physical and cyber processes with the goal of enabling a closed-loop control, calls for a paradigm shift in processing and mining the large amounts of cross-device data. One of the fundamental issues to be resolved targets the definition of new models of computation that allows us to integrate, interpret / mine and predict massive amount of multisystem data which requires a wide range of heterogeneous algorithmic description in order to provide accurate decision-making and control.
Towards this end, the complexity of the design-space exploration of large scale networks-on-chip (NoC)-based is exacerbated not only by the ever-increasing number of cores, but also by the increased runtime uncertainties in both the scale and task structure of the emerging applications. As a result, it is crucial to develop rigorous mathematical frameworks for capturing the task dependencies of varied applications to foster the generation of realistic benchmarks that can guide the NoC design. The current NoC benchmark suites either lack portability and poorly scale as they require intensive development efforts on specific architectures and simulation time, or are synthesized based on purely stochastic models that are disconnected from the characteristics of real applications, which may easily lead to biased and/or delayed design choices.
To address this challenge, we present in this talk a benchmark synthesis framework that not only allows extraction of dynamical task dependencies of the application and synthesize traffic workloads spatio-temporally consistent with realistic traffic behavior, but can also be easily scaled by the proposed complex network inspired metrics for large-scale benchmark generation while preserving key structural features that governs application communication behaviors. We validate the proposed framework via a comparative analysis on a realistic simulation environment by running a set of real application benchmarks. We show the synthesized benchmarks respect the traffic patterns of the original applications and preserve key features of application task structures. This newly proposed model of computation enables the efficient and accelerated design of future data-center-on-a-chip architectures for CPS infrastructures.
Biography: Yuankun Xue is a Ph.D student working under the supervision of Professor Paul Bogdan in the Ming Hsieh Department of Electrical Engineering at University of Southern California. He received his B.Sc and M.Sc degree from Fudan University in 2007 and 2011, respectively. His research interests include mathematical approaches for causal modeling, analysis and control of Cyber-Physical Systems, large-scale dynamic networked systems modeling, optimization and control, and design methodologies for high performance manycore platforms for computational biology.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE 598 Computer Engineering Seminar
Thu, Sep 29, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Tarek A. El-Ghazawi, Professor, George Washington University
Talk Title: Hierarchical Locality and Parallel Programming in the Extreme Scale Era
Abstract: Modern high-performance computers are characterized with massive hardware parallelism and deep hierarchies. Hierarchical levels may include cores, dies, chips, and nodes to name a few. Locality exploitation at all levels of the hierarchy is a must as the cost of data transfers can be high. Programmer's knowledge and the expressivity of locality-aware programming models such as the Partitioned Global Address Space (PGAS) can be very useful. However, locality awareness can come at a high cost. In addition, asking programmers to worry about expressing locality relations at multiple architecture hierarchy levels is detrimental to productivity and systems and hardware must provide adequate support for exploiting hierarchical locality.
In this talk I will discuss a framework for understanding and exploiting hierarchical locality in preparation for the next era of extreme computing. The role of system and hardware support will be highlighted will be stressed and examples will be shared.
Biography: Tarek El-Ghazawi is a Professor in the Department of Electrical and Computer Engineering at The George Washington University, where he leads the university-wide Strategic Academic Program in High-Performance Computing. His research interests include high-performance computing, computer architecture, reconfigurable computing and parallel programming.
He is the founding director of The GW Institute for Massively Parallel Applications and Computing Technologies (IMPACT) and was a founding Co-Director of the NSF Industry/University Center for High-Performance Reconfigurable Computing (CHREC). He is one of the principal co-authors of the UPC parallel programming language and the primary author of the UPC book from John Wiley and Sons. He has received his Ph.D. degree in Electrical and Computer Engineering from New Mexico State University in 1988. El-Ghazawi has published well over 250 refereed research publications in this area. Dr. El-Ghazawi has served and is serving in many editorial roles including an Associate Editor for the IEEE Transactions on Parallel and Distributed Computing and IEEE Transactions on Computers. He chaired and co-chaired many international conferences and symposia. He has served on many advisory boards and in consulting roles including service as a consultant at NASA GSFC and NASA Ames. Dr. El-Ghazawi's research has been frequently supported by Federal agencies and industry including DARPA/DoD, NSF, DoE/LBNL, AFRL, NASA, IBM, HP, Intel, AMD, SGI, and Microsoft. El-Ghazawi is a Fellow of the IEEE, a Research Faculty Fellow of the IBM Center for Advanced Studies, Toronto; a recipient of the Alexander von Humboldt Research Award; and a recipient of the Alexander Schwarzkopf Prize for Technical Innovation and the GW SEAS Distinguished Researcher Award. He also served as a U.S. Senior Fulbright Scholar.
Host: Xuehai Qian
Location: OHE 100D
Audiences: Everyone Is Invited
Contact: Estela Lopez
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Sep 30, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Nader Behdad, University of Wisconsin, Madison
Talk Title: Affordable Phased-Array Antenna Technology Exploiting Reconfigurable Metamaterials
Host: Prof. Hossein Hashemi, Prof. Mike Chen, and Prof. Mahta Moghaddam
More Information: MHI Seminar Series IS - Nader_Behdad_Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
