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Conferences, Lectures, & Seminars
Events for August
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CAIS Seminar: Dr. Long Tran-Thanh (University of Southampton) - Bandit Theory and its Application to Security Games
Wed, Aug 09, 2017 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Long Tran-Thanh, University of Southampton
Talk Title: Bandit Theory and its Application to Security Games
Series: Center for AI in Society (CAIS) Seminar Series
Abstract: Dr. Tran-Thanh will briefly discuss different models of bandit theory, which lies within the intersection of sequential decision making and online optimization. In particular, he will start with the basic concept of the multi-armed bandit model, and later extend it by changing its parameters and/or assumptions. Finally, Dr. Tran-Thanh will show how these models can be applied to repeated security games, for both zero-sum and non zero-sum.
Biography: Dr. Tran-Thanh is a Lecturer at the University of Southampton, UK. He obtained his PhD in Computer Science in 2012 at the same university. He has been conducting active research in a number of key areas of AI, mainly focusing on combining online machine learning, game theory, and incentive engineering to tackle resource-constrained decision making problems where one has to deal with strategic human participants and/or malicious opponents. Additionally, Dr. Tran-Thanh has also applied his theoretical findings to a number of real world applications, such as online keyword bidding, wireless sensor networks, and crowdsourcing.
Host: Milind Tambe
Location: Hedco Pertroleum and Chemical Engineering Building (HED) - 116
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Mork Family Department Graduate Seminar
Fri, Aug 11, 2017 @ 10:00 AM - 11:00 AM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Robert Simpson, Singapore University of Technology and Design
Talk Title: Active electronic and photonic materials by nanostructural design
Host: Dr. Paulo Branicio
More Information: USC_Abstract_Simpson.docx
Location: Hedco Pertroleum and Chemical Engineering Building (HED) - 116
Audiences: Everyone Is Invited
Contact: Aleessa Atienza
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NL Seminar-Improving machine translation from low resource languages
Fri, Aug 11, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Nima Pourdamghani, USC/ISI
Talk Title: Improving machine translation from low resource languages
Series: Natural Language Seminar
Abstract: Statistical machine translation MT often needs a large corpus of parallel translated sentences in order to achieve good performance. This limits the use of current MT technologies to a few resource rich languages. Assume an incident happens in an area with a low-resource language. For a quick response, we need to build an MT system with available data, as finding or translating new parallel data is expensive and time consuming. For many languages this means that we only have a small amount of often out-of-domain parallel data e.g. a Bible or Ubuntu manual. This talk is about ways to improve machine translation in low resource scenarios. I'll talk about use of monolingual data and parallel data from related languages to improve machine translation from the low resource language into English.
Biography: Nima Pourdamghani is a fourth year Ph.D. student at ISI. He works with Professor Kevin Knight on machine translation from low resource languages.
Host: Nima Pourdamghani
Location: Information Science Institute (ISI) - 6th Flr Conf Rm -# 689
Audiences: Everyone Is Invited
Contact: Peter Zamar
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Magnetic Particle Imaging as a Deep-Penetrating, Quantitative, Positive-Contrast, & Noninvasive Imaging Method with Micromolar Sensitivity
Thu, Aug 17, 2017 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Steven Conolly, UC Berkeley Bioengineering & EECS
Talk Title: Magnetic Particle Imaging as a Deep-Penetrating, Quantitative, Positive-Contrast, & Noninvasive Imaging Method with Micromolar Sensitivity
Series: Medical Imaging Seminar Series
Abstract:
Host: Professor Krishna Nayak
Location: 248
Audiences: Everyone Is Invited
Contact: Talyia White
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Synchronization and Localization in Wireless Networks
Thu, Aug 17, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Bernhard Etzlinger, Johannes Kepler University and Linz Austria
Talk Title: Synchronization and Localization in Wireless Networks
Abstract: Several widely-used radio localization systems, such as GPS and cellular localization, rely on time-of-flight measurements of data-bearing signals to determine inter-radio distances. For such measurements to be meaningful, accurate synchronization is required. Synchronization becomes more important in emerging applications for large cooperative wireless networks, and has led to active research in the area of synchronization and localization. State-of-the-art solutions either adopt a two-step, first synchronize then localize paradigm, or perform centralized, simultaneous localization and synchronization that impose stringent constraints on the network topology. In this talk, we introduce a framework for distributed simultaneous localization and synchronization that overcomes these limitations. The framework consists of a Bayesian factor graph formulation for cooperative simultaneous localization and synchronization, and is suited for wireless networks with mobile nodes and time-varying clock parameters. Building on this factor graph, a distributed belief propagation algorithm is developed that allows for real-time operation and is suitable for a time-varying network connectivity. While numerical results indicate a similar localization accuracy as achieved in perfectly synchronized networks, demonstrator implementations validate the robustness of the algorithm in practice.
Biography: Bernhard Etzlinger received the Dipl.-Ing. (M.Sc.) degree in mechatronics in 2010 and his Dr.techn. (Ph.D.) degree in technical sciences in 2016, both with distinction from the Johannes Kepler University Linz, Linz, Austria. Since 2016, he has been with the Institute of Communication Systems and RF-Systems, Johannes Kepler University Linz, where he is currently a postdoctoral researcher. In 2010, he worked at Fraunhofer FKIE, Wachtberg, Germany. During his Ph.D. studies, he was a visiting student with the Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden, and with the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA. Currently he is visiting scholar at the Ming Hsieh Department of Electrical Engineering, University of Southern California. Dr. Etzlinger is the recipient of the 2017 Upper-Austrian innovation prize for achievements on a secure communication interface for real-time power-line protection. He has also served as a TPC member for the 2017 VTC Spring Conference and as a session chair at the Asilomar Conference in 2015. Dr. Etzlinger's research interests include statistical signal processing for receiver design, cooperative network clock synchronization and localization, and wireless control systems.
Host: Urbashi Mitra, ubli@usc.edu, EEB 536, x04667
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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NL Seminar-Neural Creative Language Generation
Fri, Aug 18, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Marjan Ghazvininejad, USC/ISI
Talk Title: Neural Creative Language Generation
Series: Natural Language Seminar
Abstract: Natural language generation NLG is a well studied and still very challenging field in natural language processing. One of the less studied NLG tasks is the generation of creative texts such as jokes, puns, or poems. Multiple reasons contribute to the difficulty of research in this area. First, no immediate application exists for creative language generation. This has made the research on creative NLG extremely diverse, having different goals, assumptions, and constraints. Second, no quantitative measure exists for creative NLG tasks. Consequently, it is often difficult to tune the parameters of creative generation models and drive improvements to these systems. Finally, rule based systems for creative language generation are not yet combined with deep learning methods.
In this work, we address these challenges for poetry generation which is one of the main areas of creative language generation. We introduce password poems as a novel application for poetry generation. Furthermore, we combine finite-state machinery with deep learning models in a system for generating poems for any given topic. We introduce a quantitative metric for evaluating the generated poems and build the first interactive poetry generation system that enables users to revise system generated poems by adjusting style configuration settings like alliteration, concreteness and the sentiment of the poem.
In order to improve the poetry generation system, we decide to borrow ideas from human literature and develop a poetry translation system. We propose to study human poetry translation and measure the language variation in this process. we will study how human poetry translation is different from human translation in general and whether a translator translates poetry more freely. Then we will use our findings to develop a machine translation system specifically for translating poetry and proposing metrics for evaluating the quality of poetry translation.
Biography: Marjan Ghazvininejad is a PhD student at ISI working with Professor Kevin Knight.
Host: Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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Seminars in Biomedical EngineeringMon, Aug 21, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Stacey Finley, PhD, Assistant Professor Gordon S. Marshall Early Career Chair Biomedical Engineering Chemical Engineering and Materials Science
Talk Title: Course Introduction
Host: Stacey Finley, PhD
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Model-based and data-based flow analysis using optimization
Mon, Aug 21, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Peter Schmid, Imperial College, London
Talk Title: Model-based and data-based flow analysis using optimization
Series: Fall 2017 Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: In recent years, PDE-constrained optimization has become an effective and efficient tool in the analysis of complex fluid systems. Inherent stability, receptivity to external or internal forcing, or sensitivity to uncertainties or imperfections of fluid systems can be treated within this approach. We will present a computational framework based on this concept and demonstrate its ability to extract relevant information from numerical simulations, with examples from aeroacoustics, inertial mixing, roughness-induced receptivity, and turbomachinery cascades.
We will also discuss the reformulation of the PDE-constrained into a data-constrained framework and show preliminary steps in the analysis of fluid systems based on data only. We will present work in progress on phase-space clustering, data assimilation, and dynamic observers to detect and describe relevant mechanisms and coherent structures in data sequences.
Biography: Peter Schmid holds a Chair Professorship of Applied Mathematics and Mathematical Physics in the Department of Mathematics at Imperial College, London. Before joining the department in 2013, he held a position of research director (DR2) with the French National Research Agency (CNRS) and a professorship (PCC) at the Ecole Polytechnique in France, from 2005 to 2014. Before then, he was a faculty member in Applied Mathematics at the University of Washington in Seattle, WA (from 1993 to 2005).
Professor Schmid is a Fellow of the American Physical Society and a member of the editorial board of the Physical Review Fluids. He received his PhD in Mathematics in 1993 from the Massachusetts Institute of Technology and his Engineer's Degree in Aerospace Engineering from the Technical University Munich. His research lies in the area of computational fluid mechanics, with emphasis on stability theory, receptivity theory, flow control, model reduction, and system identification. He is also interested in methods for quantitative flow analysis for numerical and experimental data.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Networking for Big Data: Theory and Optimization for NDN
Tue, Aug 22, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Edmund Yeh, Northeastern University
Talk Title: Networking for Big Data: Theory and Optimization for NDN
Abstract: The advent of Big Data is stimulating the development of new networking architectures which facilitate the acquisition, transmission, storage, and computation of data. In particular, Named Data Networking (NDN) is an emerging content-centric networking architecture which focuses on enabling end users to obtain the data they want, rather than to communicate with specific nodes. By naming content instead of their locations, NDN transforms data into a first-class network entity.
In this talk, we present a new analytical and design framework for the optimization of key network functionalities within the NDN architecture, which is also broadly applicable to content delivery and peer-to-peer networks. The framework includes the joint optimization of traffic engineering and caching strategies, in order to best utilize both bandwidth and storage for efficient content distribution. It also includes optimal congestion control when user demand for content becomes excessive. We first develop distributed and adaptive algorithms for joint request forwarding and dynamic cache placement and eviction, which effectively achieve network load balancing, thereby maximizing the user demand rate that the NDN network can satisfy. Next, we investigate fair congestion control for NDN. In the absence of source-destination pairs, traditional congestion control schemes are inappropriate. Instead, we develop content-based congestion control algorithms which naturally work in concert with forwarding and caching to achieve a favorable tradeoff between the aggregate user utility from admitted content requests and the total user delay. Numerical experiments within a number of network settings demonstrate the superior performance of these algorithms in terms of multiple metrics.
Joint work with Tracey Ho, Ying Cui, Ran Liu, Michael Burd, and Derek Leong
Biography: Edmund Yeh received his B.S. in Electrical Engineering with Distinction and Phi Beta Kappa from Stanford University in 1994. He then studied at Cambridge University on the Winston Churchill Scholarship, obtaining his M.Phil in Engineering in 1995. He received his Ph.D. in Electrical Engineering and Computer Science from MIT under Professor Robert Gallager in 2001. He is currently Professor of Electrical and Computer Engineering at Northeastern University. He was previously Assistant and Associate Professor of Electrical Engineering, Computer Science, and Statistics at Yale University. He has held visiting positions at MIT, Stanford, Princeton, UC Berkeley, EPFL, and TU Munich.
Professor Yeh was one of the PIs on the original NSF-funded FIA Named Data Networking project. He will serve as General Co-Chair for ACM Conference on Information Centric Networking (ICN) 2018 in Boston. He is the recipient of the Alexander von Humboldt Research Fellowship, the Army Research Office Young Investigator Award, the Winston Churchill Scholarship, the National Science Foundation and Office of Naval Research Graduate Fellowships, the Barry M. Goldwater Scholarship, the Frederick Emmons Terman Engineering Scholastic Award, and the President's Award for Academic Excellence (Stanford University). Professor Yeh has served as the Secretary of the Board of Governors of the IEEE Information Theory Society. He received the Best Paper Award at the 2015 IEEE International Conference on Communications (ICC) Communication Theory Symposium.
Host: Michael Neely, mjneely@usc.edu, EEB 520, x03505
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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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, Aug 23, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Davide Bresolin, Assistant Professor, University of Padova, Italy, and Luca Geretti, Research Fellow, University of Verona, Italy
Talk Title: Formal Verification of Nonlinear Hybrid Systems using Ariadne
Abstract: In embedded systems design there is often the need to model complex systems having a mixed discrete and continuous behavior that cannot be characterized faithfully using either discrete or continuous models only. Such systems consist of a discrete control part that operates in a continuous environment and are named hybrid systems. Unfortunately, most of the verification problems for hybrid systems, like reachability analysis, turn out to be undecidable. Because of this, many approximation techniques and tools to estimate the reachable set have been proposed in the literature. However, most of the tools are unable to handle nonlinear dynamics and constraints, and do not perform conservative numerical rounding. In this seminary we present an open-source framework for hybrid system verification, called Ariadne, which exploits approximation techniques based on the theory of computable analysis for implementing formal verification algorithms.
Biography: Davide Bresolin received the Ph.D. degree in computer science from the University of Udine, Udine, Italy, in 2007. He is an Assistant Professor with the Mathematics Department, University of Padova, Italy. From 2007 to 2013, he was a Research Fellow with the Department of Computer Science, University of Verona, Verona, Italy, where he collaborated with the Electronic Systems Design Group (ESD) and the ALTAIR Robotics Group. From 2013 to 2016 he was an Assistant Professor with the Computer Science and Engineering Department, University of Bologna, Bologna, Italy. His research activity is focused on formal verification of cyber-physical and embedded systems using hybrid automata and temporal logics, on automata theory, and on temporal representation and reasoning using interval-based temporal logics.
Luca Geretti received the Laurea degree in electrical engineering and the Ph.D. degree in computer engineering from the University of Udine, Udine, Italy, in 2005 and 2009, respectively. He was a Research Fellow with the Department of Computer Science, University of Verona, Verona, Italy, between 2009 and 2011. He was a Research Fellow with the Department of Electrical Engineering, University of Udine, Udine, Italy, between 2012 and 2015. He is currently a Research Fellow with the Department of Computer Science, University of Verona, Verona, Italy. His current research interests are in the fields of formal verification of cyber-physical systems using hybrid automata, and of parallel and distributed computing.
Host: Pierluigi Nuzzo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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Aerospace & Mechanical Engineering Seminar
Wed, Aug 23, 2017 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Allison Anderson, University of Colorado - Boulder
Talk Title: TBA
Host: Department of Aerospace and Mechanical Engineering
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
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W.V.T. RUSCH ENGINEERING HONORS COLLOQUIUM
Fri, Aug 25, 2017 @ 01:00 PM - 02:00 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Luz Rivas, Board of Public Works Commissioner City of Los Angeles
Talk Title: Engineering, Entrepreneurship, and Service
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Su Stevens
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Techniques for Content Delivery at Scale in Current and Future Network Architectures
Fri, Aug 25, 2017 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Nishanth Sastry, King's College London
Talk Title: Techniques for Content Delivery at Scale in Current and Future Network Architectures
Abstract: On-demand video streaming dominates today's Internet traffic mix. For instance, Netflix constitutes a third of the peak time traffic in the USA. Nearly half of UK online households have accessed BBC's shows through its on-demand streaming interface, BBC iPlayer. Using UK-wide traces from BBC iPlayer as a case study, this talk will characterise users' content consumption at scale and discuss techniques that can be deployed at the edge by users to substantially decrease the load in the Internet. We will survey both well-known techniques such as peer-assisted video-on-demand, studying whether it works at scale, as well as new edge-caching mechanisms that can potentially be deployed today. We will conclude by exploring new directions for future network architectures, to address the roots of the pain points observed in our user workload, in a "clean" fashion.
Biography: Nishanth is a Senior Lecturer at King's College London. He holds a PhD from the University of Cambridge, UK, a Master's degree from The University of Texas at Austin, and a Bachelor's degree from Bangalore University, India, all in Computer Science. He has spent several years in Industry, at Cisco Systems and at IBM (both in the Software Group and at the TJ Watson Research Center).
His work in the last few years has focused on analysing large real-world datasets, funded by several grants from two different UK Research Councils (EPSRC and ESRC), as well as by the European Commission. He has given several keynotes about his work, and has frequently been featured in various TV shows and other media outlets including Nature News, New Scientist and BBC.
Host: Andreas Molisch, x04670, molisch@usc.edu
Location: 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Seminars in Biomedical Engineering
Mon, Aug 28, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Mike Kwon, CEO, Whitecoat
Talk Title: Research Presentation & Career Path
Host: Stacey Finley, PhD
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Aug 28, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Terence Sanger, University of Southern California
Talk Title: Rate coding, spike coding, and biological control
Series: Fall 2017 Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: Although biological and artificial computation must solve similar problems, they do so in very different ways. I will discuss several closely related topics in biological control systems. Risk-aware control is a set of human behaviors in asymmetric cost environments that set the groundwork and requirements for models of biological feedback control. Stochastic Dynamic Operators provide a set of tools for control of uncertain stochastic systems, and I show that these operators can implement risk aware control in a simple robotic visual targeting task. I also show how the calculations necessary for control and stabilization can be implemented in populations of asynchronous spiking neurons. Finally, I provide preliminary data from electrophysiological recordings in the brains of children with movement disorders that provide some clues as to how the human basal ganglia encode movement.
Biography: Dr. Terence Sanger is the director of the USC Pediatric Movement Disorders Center. His research focuses on understanding the origins of pediatric movement disorders from both a biological and a computational perspective. The primary goal of his research is to discover new methods for treating children with movement disorders. Dr. Sanger coordinates the Childhood Motor Study Group (CMSG) and the NIH Taskforce on Childhood Movement Disorders, and he is principal investigator on several research studies at USC. He runs the pediatric movement disorders clinic at Children's Hospital of Los Angeles (CHLA) in the department of Neurology. His training includes background in Child Neurology, Electrical Engineering, Signal Processing, Control Theory, Neural Networks, and Computational Neuroscience.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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The Convergence of Machine Learning, Big Data, and Supercomputing
Tue, Aug 29, 2017 @ 10:30 AM - 11:30 PM
Thomas Lord Department of Computer Science, Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jeremy Kepner, MIT Lincoln Laboratory Fellow/MIT Lincoln Laboratory Supercomputing Center Founder
Talk Title: The Convergence of Machine Learning, Big Data, and Supercomputing
Abstract: Machine learning, big data and simulation challenges have led to a proliferation of computing hardware and software solutions. Hyperscale data centers, accelerators and programmable logic can deliver enormous performance via a range of analytic environments and data-storage technologies. Effectively exploiting these capabilities for science and engineering requires mathematically rigorous interfaces that allow scientists and engineers to focus on their research and avoid rewriting software each time computing technology changes. Mathematically rigorous interfaces are at the core of the MIT Lincoln Laboratory Supercomputing Center and let it deliver leading-edge technologies to thousands of scientists and engineers. This talk discusses the rapidly evolving computing landscape and how mathematically rigorous interfaces are key to exploiting advanced computing capabilities.
Biography: Dr. Jeremy Kepner is a MIT Lincoln Laboratory Fellow. He founded the Lincoln Laboratory Supercomputing Center and pioneered the establishment of the Massachusetts Green High Performance Computing Center. He has developed novel big data and parallel computing software used by thousands of scientists and engineers worldwide. He has led several embedded computing efforts, which earned him a 2011 R&D 100 Award. Dr. Kepner has chaired SIAM Data Mining, IEEE Big Data, and the IEEE HPEC conference. Dr. Kepner is the author of two bestselling books on Parallel MATLAB and Graph Algorithms. His peer-reviewed publications include works on abstract algebra, astronomy, astrophysics, cloud computing, cybersecurity, data mining, databases, graph algorithms, health sciences, plasma physics, signal processing, and 3D visualization. In 2014, he received Lincoln Laboratory's Technical Excellence Award. Dr. Kepner holds a B.A. in astrophysics from Pomona College and a Ph.D. in astrophysics from Princeton University.
Host: Dr. Viktor K. Prasanna
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Kathy Kassar
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Epstein Institute Seminar, ISE 651
Tue, Aug 29, 2017 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jim Luedtke , Associate Professor, University of Wisconsin-Madison
Talk Title: New Approximate Solution Approaches for Multi-Stage Stochastic Optimization
Host: Prof. Phebe Vayanos
More Information: August 29, 2017.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Aerospace & Mechanical Engineering Seminar
Wed, Aug 30, 2017 @ 03:30 AM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Michele Guala, Associate Professor/Unniversity of Minnesota
Talk Title: Wall Turbulence Structure in the Atmospheric Surface Layer. Scaling and Implications on Wind Turbine Siting
Abstract: The atmospheric surface layer, under special geophysical conditions, has been used as a canonical representation of wall turbulence flows at high Reynolds numbers. In this presentation I will describe how hotwire field measurements in the SLTEST (Utah) and Super-large-scale particle image velocimetry (SPIV, Hong et al., 2014, Toloui et al. 2014) during natural snowfalls in Minnesota, gave us the opportunity to explore atmospheric flows with unprecedentedly high spatio-temporal resolution. Results from SPIV measurements in the thermally neutral atmospheric surface layer, collected at the EOLOS field station over relatively flat, snow-covered farmland, will be introduced as a fully rough wall boundary layer with a Reynolds number Re ~10^6. The data include three time-resolved 15-minute acquisition periods with a field of view extending from 3 m to 19 m above the ground and up to 14 m wide. The flow statistics are validated and supplemented by sonic anemometers from a meteorological tower immediately downstream of the SPIV field of view. The time-resolved planar measurements provide temporal and spatial characterization of key wall turbulence features at high Reynolds number, including ramp-like structures, spanwise vortices, and uniform momentum zones. In comparing the findings to laboratory studies, Reynolds number similarity and the scaling behavior of characteristic properties will be discussed. The limitations of SPIV measurements will be presented using concepts of particle-turbulence interaction and further observations of snow flake dynamics. The impact of large scale flow measurements and turbulent motions will be discussed in the context of wind energy.
Host: Department of Aerospace and Mechanical Engineering
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
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EE 598 Computer Engineering Seminar Series
Thu, Aug 31, 2017 @ 02:00 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rakesh Kumar, University of Illinois at Urbana Champaign
Talk Title: Ultra Low Power Computing in the IoT Era
Series: EE 598 Computer Engineering Seminar Series
Abstract: Wearables, sensors, and Internet of Things (IoT) arguably represent the next frontier of computing. They will be characterized by extremely low power and area requirements. In our recent research, we asked the question: are there opportunities for power and area reduction that are unique to these emerging computing platforms. We answered the question in the affirmative and developed several techniques that appear to be very effective. In this talk, I will focus one such technique--symbolic hardware-software co-analysis--that is applicable over a wide class of applications. Through a novel symbolic execution-based approach, we can determine for a given application the gates in the hardware that the application is guaranteed to not touch. This information can then be used to determine application-specific Vmin, determine application-specific peak power, and, build bespoke processors customized to a given application. If time permits, I will also discuss how architectural ideas such bit serial processors and k-hot pipelining may become promising for the IoT applications.
Biography: Rakesh Kumar is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois at Urbana Champaign and a Co-Founder and Chief Architect at Hyperion Core, Inc. He has made contributions in the area of processor design and memory system design that have directly impacted industry and state-of-art. His current research interests are in computer architecture, low power and error resilient computer systems, and approximate computing. He has a B-Tech from IIT Kharagpur and a PhD from University of California at San Diego. He is often seen at a restaurant or hanging out with his very active four-year old.
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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THURSDAY TALKS: NL Seminars-1 Recurrent Neural Networks as Weighted Language Recognizers 2 Gloss-to-English: Improving Low Resource Language Translation Using Alignment Tables
Thu, Aug 31, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yining Chen and Sasha Mayn , USC/ISI Interns
Talk Title: THURSDAY TALKS: 1 Recurrent Neural Networks as Weighted Language Recognizers 2 Gloss-to-English: Improving Low Resource Language Translation Using Alignment Tables
Series: Natural Language Seminar
Abstract: 1. We investigate properties of a simple recurrent neural network RNN as a formal device for recognizing weighted languages. We focus on the single layer, ReLU activation, rational weight RNN with softmax, a standard form of RNN used in language processing applications. We prove that many questions one may ask about such RNNs are undecidable, including consistency, equivalence, minimization, and finding the highest weighted string. For consistent RNNs, finding the highest weighted string is decidable, although the solution can be exponentially long in the length of the input RNN encoded in binary. Limiting to solutions of polynomial length, we prove that finding the highest-weighted string for a consistent RNN is NP complete and APX hard.
2. Neural Machine Translation has gained popularity in recent years and has been able to achieve impressive results. The only caveat is that millions of parallel sentences are needed in order to train the system properly, and in a low resource scenario that amount of data simply may not be available. This talk will discuss strategies for addressing the data scarcity problem, particularly using alignment tables to make use of parallel data from higher resource language pairs and creating synthetic in domain data.
Biography: Yining Chen is a third year undergraduate student at Dartmouth College. She is a summer intern at ISI working with Professor Kevin Knight and Professor Jonathan May.
Sasha Mayn is a summer intern for the ISI Natural Language Group. She is particularly interested in machine translation and language generation. Last summer Sasha interned at the PanLex Project in Berkeley, where she was responsible for preprocessing digital dictionaries and entering them into PanLex's multilingual database. This summer she has been working on improving neural machine translation strategies for low resource languages under the supervision of Jon May and Kevin Knight.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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Epstein Institute Seminar, ISE 651
Thu, Aug 31, 2017 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Georgia-Ann Klutke, National Science Foundation (NSF)
Talk Title: Navigating NSF: Funding Opportunities for Operations Research
Host: Prof. Suvrajeet Sen
More Information: August 31, 2017.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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PhD Defense: Sampling Theory for Graph Signals with Applications to Semi-supervised Learning
Thu, Aug 31, 2017 @ 03:30 PM - 05:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Aamir Anis, USC
Talk Title: PhD Defense: Sampling Theory for Graph Signals with Applications to Semi-supervised Learning
Abstract: The representation, processing and analysis of large-scale data as signals defined over graphs has drawn much interest recently. Graphs allow us to embed natural inter-connectivities between data points and exploit them during processing. As a result, graph signal processing has laid a strong foothold in various modern application domains such as machine learning, analysis of social, transportation, web and sensor networks, and even traditional areas such as image processing and video compression. Although powerful, this research area is still in its infancy. Recent efforts have therefore focused on translating well-developed tools of traditional signal processing for handling graph signals.
An important aspect of graph signal processing is defining a notion of frequency for graph signals. A frequency domain representation for graph signals can be defined using the eigenvectors and eigenvalues of variation operators (e.g., graph Laplacian) that take into account the underlying graph connectivity. These operators can also be used to design graph spectral filters. The primary focus of our work is to develop a theory of sampling for graph signals that answers the following questions: 1. When can one recover a graph signal from its samples on a given subset of nodes of the graph? 2. What is the best choice of nodes to sample a given graph signal? Our formulation primarily works under the assumption of bandlimitedness in the graph Fourier domain, which amounts to smoothness of the signal over the graph. The techniques we employ to answer these questions are based on the introduction of special quantities called graph spectral proxies that allow our algorithms to operate in the vertex domain, thereby admitting efficient, localized implementations.
We also explore the sampling problem in the context of designing wavelet filterbanks on graphs. This problem is fundamentally different since one needs to choose a sampling scheme jointly over multiple channels of the filterbank. We explore constraints for designing perfect reconstruction two-channel critically-sampled filterbanks with low-degree polynomial filters, and conclude that such a design is in general not possible for arbitrary graphs. This leads us to propose an efficient technique for designing a critical sampling scheme that, given pre-designed filters, aims to minimize the overall reconstruction error of the filterbank. We also explore M-channel filterbanks over M-block cyclic graphs (that are natural extensions of bipartite graphs), and propose a tree-structured design in a simpler setting when M is a power of 2.
As an application, we study the graph-based semi-supervised learning problem from a sampling theory point of view. A crucial assumption here is that class labels form a smooth graph signal over a similarity graph constructed from the feature vectors. Our analysis justifies this premise by showing that in the asymptotic limit, the bandwidth (a measure of smoothness) of any class indicator signal is closely related to the geometry of the dataset. Using the sampling theory perspective, we also quantitatively show that the label complexity (i.e., the amount of labeling required for perfect prediction of unknown labels) matches its theoretical value, thereby adding to the appeal of graph-based techniques for semi-supervised learning.
Biography: Aamir Anis received his Bachelor and Master of Technology degree in Electronics and Electrical Communication Engineering from the Indian Institute of Technology (IIT), Kharagpur, India, in 2012. He joined the Electrical Engineering department at the University of Southern California (USC), Los Angeles, in 2012, where he has been working towards a Ph.D. degree in Electrical Engineering. He has been the recipient of the Best Student Paper award at ICASSP 2014. His research interests include graph signal processing with applications in machine learning, and multimedia compression.
Host: Dr. Antonio Ortega
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
