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Conferences, Lectures, & Seminars
Events for March
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CS Colloquium: Manu Sridharan (Samsung Research) - Analysis Tools for Reliable Software Everywhere
Tue, Mar 01, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Manu Sridharan, Samsung Research America
Talk Title: Analysis Tools for Reliable Software Everywhere
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Software is becoming ubiquitous in everyday life, from today's
smartphones and servers to tomorrow's self-driving cars, drones, and Internet of Things devices. However, the distributed, always-on nature of this software poses significant new challenges for reliability, security, and programmer productivity. Better programming tools are needed to enable next-generation applications to achieve their full transformative potential. I have helped design and develop several such tools in my recent research based on novel techniques in program analysis.
This talk will focus on EventRacer, the first tool for discovering and debugging non-determinism errors in event-driven programs. Event-driven programming has recently achieved a meteoric rise in popularity, as it is well-suited to the needs of modern interactive, client-server applications. However, event-driven programs often suffer from timing-based data races that can be fiendishly difficult to reproduce and debug. EventRacer adapts the notion of a "happens-before relation" from concurrent and distributed systems to give a clean definition of data races for event-driven programs. It also incorporates multiple novel techniques to achieve scalability and usability for real-world applications. With EventRacer, we found many errors in deployed Fortune 100 web sites, and its techniques have since been applied in a variety of other emerging domains.
Biography: Manu Sridharan is a senior researcher at Samsung Research America in the area of programming languages and software engineering. He received his PhD from the University of California, Berkeley in 2007, and he worked as a research staff member at IBM Research from 2008-2013. His research has drawn on, and contributed to, techniques in static analysis, dynamic analysis, and program synthesis, with applications to security, software quality, code refactoring, and software performance. His work has been incorporated into multiple commercial products, including IBM's commercial security analysis tool and Samsung's developer toolkit for the Tizen operating system. For further details, see http://manu.sridharan.net.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute Seminar - ISE 651
Tue, Mar 01, 2016 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Eric Horvitz, Technical Fellow & Managing Director - Microsoft Research Lab
Talk Title: Data, Predictions, and Decisions in Support of People and Society
Host: Ali Abbas
More Information: March 1, 2016 Horvitz.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Michele ISE
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CS Colloquium: Iolanda Leite (Disney Research) - Long-term Human-Robot Interaction in the Real-World
Tue, Mar 01, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Iolanda Leite, Disney Research
Talk Title: Long-term Human-Robot Interaction in the Real-World
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Most social robots and virtual characters are still unable to keep users engaged over repeated interactions because they lack social and adaptive capabilities that facilitate the interaction once the novelty effect fades away. In this seminar, I will present my past and current research on mechanisms that allow autonomous robots to be deployed in real-world social environments over weeks and months. These mechanisms include computational models of empathy, turn-taking and engagement. I will present evidence on the positive effects of implementing these models in robots and virtual characters interacting with people in several application domains, and discuss limitations of the current state of the art in robotic technology suitable for realistic social environments. An improved understanding of how robots should perceive and act depending on their surrounding social context can lead to more natural, enjoyable and useful long-term human-robot interactions.
The meeting will be available to stream HERE. Please open in new tab for best results.
Biography: Iolanda Leite is an Associate Research Scientist at Disney Research, Pittsburgh. She received her Ph.D in Information Systems and Computer Engineering from Instituto Superior Técnico, University of Lisbon, in 2013. From 2013 to 2015, she was a Postdoctoral Associate at the Yale Social Robotics Lab. Her doctoral dissertation, "Long-term Interactions with Empathic Social Robots", received an honorable mention in the IFAAMAS-13 Victor Lesser Distinguished Dissertation Award. Iolanda has published over 40 conference and journal in the areas of human-robot interaction, artificial intelligence and affective computing.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MHI Distinguished Visitor Talk
Wed, Mar 02, 2016 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. K.J. Ray Liu, University of Maryland
Talk Title: Why Time-Reversal for Future 5G Wireless?
Abstract: Time reversal is a fundamental physical phenomenon that takes advantage of unavoidable but rich multi-paths in radio propagation to create the spatial-temporal resonance effect, the so-called focusing effect. One can image that the larger the transmission power, the more observable multipaths. When the power is fixed, so does the maximum number of observable multipaths. Since radio waves travel at the speed of light, for one to see the multipath profile in detail, it needs high resolution in time, which implies very broad bandwidth in frequency. The larger the bandwidth, the better the time resolution, and therefore the more multipaths can be revealed. In essence, multipaths are naturally existing 'degrees of freedom' ready to be harvested via power and bandwidth. In a real environment, especially indoors, depending on the structure of the buildings, the number of observable multipaths can one observe is around 15-30 significant multipaths with 150 MHz bandwidth - the entire ISM band at 5.8 GHz. Such a large number of degrees of freedom, existing in nature, can be harvested to enable engineering applications. In this talk, we will argue that time-reversal is an ideal platform for future 5G wireless because it realizes the massive multipath effect by using a single antenna and has low complexity as the environment is serving as the computer. It is highly secure and energy efficient, scalable for extreme network densification, and ideal for cloud-based radio networks. It also offers very simply but high resolution for indoor positioning systems, an essential property for Internet of Things applications. Time-reversal meets all the demands one can envision for future 5G wireless!
Biography: Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of University of Maryland, College Park, in 2007, where he is Christine Kim Eminent Professor of Information Technology. He leads the Maryland Signals and Information Group conducting research encompassing broad areas of information and communications technology with recent focus on future wireless technologies, network science, and information forensics and security. Dr. Liu was a recipient of the 2016 IEEE Leon K. Kirchmayer Technical Field Award on graduate teaching and mentoring, IEEE Signal Processing Society 2014 Society Award, IEEE Signal Processing Society 2009 Technical Achievement Award, and various best paper awards. Recognized by Thomson Reuters as a Highly Cited Researcher, he is a Fellow of IEEE and AAAS. Dr. Liu is a member of IEEE Board of Director. He was President of IEEE Signal Processing Society, where he has served as Vice President -“ Publications and the Editor-in-Chief of IEEE Signal Processing Magazine. He also received teaching and research recognitions from University of Maryland including university-level Invention of the Year Award (three times); and college-level Poole and Kent Senior Faculty Teaching Award, Outstanding Faculty Research Award, and Outstanding Faculty Service Award, all from A. James Clark School of Engineering (one award each per year from the entire college).
Host: Prof. Shrikanth Narayanan & Prof. C.-C. Jay Kuo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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CS Colloquium: Philipp Kraehenbuehl (UC Berkeley) - The many ways to understand the pixels, and how to teach computers to do so
Wed, Mar 02, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Philipp Krahenbuhl , UC Berkeley
Talk Title: The many ways to understand the pixels, and how to teach computers to do so
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The field of computer vision is arguably seeing one of its most transformative changes in recent history. Convolutional neural networks (CNNs) have revolutionized the field, reaching super-human performance on some long-standing computer vision tasks, such as image classification. The success of these networks is fueled by massive amounts of human-labeled data. However this paradigm does not scale to a deeper and more detailed understanding of images, as it is simply too hard to collect enough human-labeled data. The issue is not that we humans don't understand the image, but we often struggle to convey enough information to successfully supervise a vision system.
In this talk I show how computer vision can go beyond massive human supervision. This involves designing better models that deal with fewer labels, exploiting easier and more intuitive annotations, or coming up with novel optimizations to train deep architectures with far fewer human annotations, or even without any at all. I'll focus on three long standing computer vision problems: semantic segmentation, intrinsic image decomposition and dense semantic correspondences.
Biography: Philipp Krahenbuhl is a postdoctoral researcher at UC Berkeley. He received a B.S. in Computer Science from ETH Zurich in 2009, and a PhD in Computer Science from Stanford University in 2014. Philipp's research interests lie in Computer vision, Machine learning and Computer Graphics. He is particularly interested in deep learning, efficient optimization techniques, and structured output prediction.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Mar 02, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Gregory Valiant, Stanford University
Talk Title: When your big data seems too small: accurate inferences beyond the empirical distribution
Series: CommNetS
Abstract: We discuss two problems related to the general challenge of making accurate inferences about a complex distribution, in the regime in which the amount of data (i.e the sample size) is too small for the empirical distribution of the samples to be an accurate representation of the underlying distribution. The first problem is the basic task of learning a discrete distribution, given access to independent draws. We show that one can accurately recover the unlabelled vector of probabilities of all domain elements whose true probability is greater than 1/(n log n). Stated differently, one can learn-“up to relabelling-“the portion of the distribution consisting of elements with probability greater than 1/(n log n). This result has several curious implications, including leading to an optimal algorithm for "de-noising" the empirical distribution of the samples, and implying that one can accurately estimate the number of new domain elements that would be seen given a new larger sample, of size up to n * log n. (Extrapolation beyond this sample size is provable information theoretically impossible, without additional assumptions on the distribution.) While these results are applicable generally, we highlight an adaptation of this general approach to some problems in genomics (e.g. quantifying the number of unobserved protein coding variants).
The second problem we consider is the task of accurately estimating the eigenvalues of the covariance matrix of a (high dimensional real-valued) distribution-“the "population spectrum". (These eigenvalues contain basic information about the distribution, including the presence or lack of low-dimensional structure in the distribution and the applicability of many higher-level machine learning and multivariate statistical tools.) As we show, even in the regime where the sample size is linear or sublinear in the dimensionality of the distribution, and hence the eigenvalues and eigenvectors of the empirical covariance matrix are misleading, accurate approximations to the true population spectrum are possible.
This talk is based on three papers, which are joint works with Paul Valiant, with Paul Valiant and James Zou, and with Weihao Kong.
Biography: Greg Valiant joined the Computer Science Department at Stanford as an Assistant Professor in Fall 2013, after completing a postdoc at Microsoft Research, New England. His main research interests are in algorithms, learning, applied probability and statistics; he is also interested in game theory, and has enjoyed working on problems in database theory. Valiant graduated from Harvard with a BA in Math and an MS in Computer Science, and obtained his PhD in Computer Science from UC Berkeley in 2012.
Host: Dr. Mahdi Soltanolkotabi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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MHI Distinguished Visitor Talk
Thu, Mar 03, 2016 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. K.J. Ray Liu, University of Maryland
Talk Title: Learning with Strategic Decision Making in Social Media
Abstract: With the increasing ubiquity and power of mobile devices, as well as the prevalence of social media, more and more activities in our daily life are being recorded, tracked, and shared, creating the notion of 'social media'. Such abundant and still growing real life data, known as 'big data', provide a tremendous research opportunity in many fields. To analyze, learn and understand such user generated big data, machine learning has been an important tool and various machine learning algorithms have been developed. However, since the user generated big data is the outcome of users decisions, actions and their socio economic interactions, which are highly dynamic, without considering users local behaviors and interests, existing learning approaches tend to focus on optimizing a global objective function at the macroeconomic level, while totally ignore users local decisions at the microeconomic level. As such there is a growing need in bridging machine/social learning with strategic decision making, which are two traditionally distinct research disciplines, to be able to jointly consider both global phenomenon and local effects to understand/model/analyze better the newly arising issues in the emerging social media. In this talk, we present the notion of 'decision learning that can involve users behaviors and interactions by combining learning with strategic decision making. We will discuss some examples from social media with real data to show how decision learning can be used to better analyze users optimal decision from a user perspective as well as design a mechanism from the system designers perspective to achieve a desirable outcome.
Biography: Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of University of Maryland, College Park, in 2007, where he is Christine Kim Eminent Professor of Information Technology. He leads the Maryland Signals and Information Group conducting research encompassing broad areas of information and communications technology with recent focus on future wireless technologies, network science, and information forensics and security. Dr. Liu was a recipient of the 2016 IEEE Leon K. Kirchmayer Technical Field Award on graduate teaching and mentoring, IEEE Signal Processing Society 2014 Society Award, IEEE Signal Processing Society 2009 Technical Achievement Award, and various best paper awards. Recognized by Thomson Reuters as a Highly Cited Researcher, he is a Fellow of IEEE and AAAS. Dr. Liu is a member of IEEE Board of Director. He was President of IEEE Signal Processing Society, where he has served as Vice President -“ Publications and Board of Governor. He was the Editor-in-Chief of IEEE Signal Processing Magazine. He also received teaching and research recognitions from University of Maryland including university-level Invention of the Year Award; and college-level Poole and Kent Senior Faculty Teaching Award, Outstanding Faculty Research Award, and Outstanding Faculty Service Award, all from A. James Clark School of Engineering.
Host: Prof. Shrikanth Narayanan & Prof. C.-C. Jay Kuo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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Less Talking, More Learning: Avoiding Coordination In Parallel Machine Learning Algorithms
Thu, Mar 03, 2016 @ 01:30 PM - 02:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Dimitris Papailiopoulos, Postdoctoral Researcher/UC Berkeley
Talk Title: Less Talking, More Learning: Avoiding Coordination In Parallel Machine Learning Algorithms
Abstract: The recent success of machine learning (ML) in both science and industry has generated an increasing demand to support ML algorithms at scale. In this talk, I will discuss strategies to gracefully scale machine learning on modern parallel computational platforms. A common approach to such scaling is coordination-free parallel algorithms, where individual processors run independently without communication, thus maximizing the time they compute. However, analyzing the performance of these algorithms can be challenging, as they often introduce race conditions and synchronization problems.
In this talk, I will introduce a general methodology for analyzing asynchronous parallel algorithms. The key idea is to model the effects of core asynchrony as noise in the algorithmic input. This allows us to understand the performance of several popular asynchronous machine learning approaches, and to determine when asynchrony effects might overwhelm them. To overcome these effects, I will propose a new framework for parallelizing ML algorithms, where all memory conflicts and race conditions can be completely avoided. I will discuss the implementation of these ideas in practice, and demonstrate that they outperform the state-of-the-art across a large number of machine learning tasks.
Biography: Dimitris Papailiopoulos is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley and a member of the AMPLab. His research interests span machine learning, coding theory, and parallel and distributed algorithms, with a current focus on coordination-free parallel machine learning, large-scale data and graph analytics, and the use of codes to speed up distributed computation. Dimitris completed his Ph.D. in electrical and computer engineering at UT Austin in 2014. At Austin he worked under the supervision of Alex Dimakis. In 2015, he received the IEEE Signal Processing Society, Young Author Best Paper Award.
Host: Professor Keith Chugg, chugg@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE-EP Seminar - Jun-Chau Chien, Friday, March 4th in EEB 132 at 2:00pm
Fri, Mar 04, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jun-Chau Chien, University of California, Berkeley
Talk Title: mm-Wave Lab-on-CMOS: Electromagnetic Sensing from Micro-to Nano-scales
Abstract: Lab-on-CMOS is an emerging platform for Point-of-Care diagnostics and precision medicine. By directly integrating active CMOS electronics into passive Lab-on-Chip devices, the new System-on-Chip not only offers ultimate device miniaturization but also enables highly integrated multiphysics biosensing and actuation. This research addresses the challenges in such a hybrid system while embracing the opportunities in system co-design to achieve improved sensing performance that leads to new scientific findings.
In this talk, I will present my research on a Lab-on-CMOS dielectric spectrometer for single-cell analysis using near-field sensing at millimeter-wave (mm-Wave) frequencies. The aim is to understand the wideband electromagnetic signatures at cellular and molecular levels and to open its way for real-time and label-free medical diagnostics and biological studies. I will focus on innovations in circuits, systems, microfluidics, and calibration techniques to enable a capacitance equivalent sensitivity limit of sub-aF, suitable for large-scale characterization of single-cell dielectric spectroscopy (6.5 ~ 30 GHz) in the setting of high-throughput flow cytometry. The capability of cell sorting based on frequency dispersion is demonstrated with the measurements of human breast cancer cells. In addition to electromagnetic sensing, I will introduce multiphysics actuation techniques to quantify the mechanical property of the cells. Specifically, the system measures the deformation of cells using hydrodynamic stretching. I will also discuss the challenges in sensing toward nano-scales and sub-THz frequencies and present an on-chip single-element electronic calibration (E-Cal) technique for nano-device measurements. In the end, I will conclude my talk with Lab-on-CMOS technology for new applications in sensing, imaging, and communication.
Biography: Jun-Chau Chien received the B.S. and M.S. degrees in Electrical Engineering from National Taiwan University in 2004 and 2006, respectively, and the Ph.D. degree in Electrical Engineering and Computer Sciences from University of California, Berkeley, in 2015. He is currently a post-doctoral research associate at University of California, Berkeley. He has held industrial positions at InvenSense, Xilinx, and HMicro working on mixed-signal integrated circuits for inertial sensors and wireline/wireless transceivers. He is broadly interested in innovative biotechnology for point-of-care diagnostics and medical imaging with emphasis on silicon-based approaches.
Dr. Chien is the recipient of the 2006 Annual Best Thesis Award from Graduate Institute of Electronics Engineering, National Taiwan University, the 2007 International Solid-State Circuit Conference (ISSCC) Silkroad Award, the co-recipient of 2010 IEEE Jack Kilby Award for ISSCC Outstanding Student Paper, the 2014 Analog Devices Outstanding Design Award, the 2014 Microwave Theory and Techniques Society (MTT-S) Graduate Fellowship for Medical Applications, the 2014 Solid-State Circuit Society (SSCS) Predoctoral Achievement Award, and the 2014 UC Berkeley Outstanding Graduate Student Instructor Award.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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NL Seminar-Linguistic Annotation Using Video Games with a Purpose
Fri, Mar 04, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: David Jurgens, Stanford University
Talk Title: Linguistic Annotation Using Video Games with a Purpose
Series: Natural Language Seminar
Abstract: Building systems that understand human language often requires access to large amounts of text annotated with all the features and nuances of human communication. However, building these annotated corpora is often prohibitive due to the time, cost, and expertise required to annotate. While crowdsourcing the work can help, untrained workers still incur costs and the workers may not be as motivated to answer correctly. In this talk, I will describe how to solve this annotation bottleneck using video games in which traditional annotation tasks are transformed into core video game mechanics and embedded in the kinds of games you might play on your mobile phone. Our video games are not only fun to play but are capable of annotating a wide variety of linguistic phenomena at costs lower that crowdsourcing and have quality equal to that of experts. Using four games, I will demonstrate how their creation process can be distilled into reusable design patterns to create new games for different types of tasks in linguistics and beyond.
Biography: David Jurgens is postdoctoral scholar in the department of Computer Science at Stanford University. He received his PhD in Computer Science from UCLA in 2014 and has been a visiting researcher at HRL Laboratories, research scientist at Sapienza University of Rome and postdoctoral scholar at McGill University. His research focuses on two areas: natural language processing, where he works on new methods for understanding the meaning of text, and computational social science where he investigates population dynamics through peoples' language and demographics. He is currently a co-chair of the International Workshops on Semantic Evaluation (SemEval) and of the workshop on Natural Language Processing and Computational Social Science. His research has been featured in Forbes, MIT Technology Review, Business Insider, and Schneier on Security.
Host: Xing Shi 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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PhD Seminar
Fri, Mar 04, 2016 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Rebecca Peer, Environmental Engineering PhD Candidate, University of Southern California
Talk Title: Spatiotemporal analysis of environmental trade-offs in electricity generation
Abstract: The US power sector is a leading contributor of emissions that affect air quality and climate. It also requires a lot of water for cooling thermoelectric power plants. Although these impacts affect ecosystems and human health unevenly in space and time, there has been very little quantification of these environmental trade-offs on decision-relevant scales. This work quantifies hourly water consumption, emissions (i.e. carbon dioxide, nitrogen oxides, and sulfur oxides), and marginal heat rates for 252 electric generation units (EGUs) in the Electric Reliability Council of Texas (ERCOT) region in 2011 using a unit commitment and dispatch model (UC&D). Annual, seasonal and daily variations, as well as spatial variability are assessed. When normalized over the grid, hourly average emissions and water consumption intensities (i.e. output per MWh) are found to be highest when electricity demand is the lowest, as baseload EGUs tend to be the most water and emissions intensive. Results suggest that a large fraction of emissions and water consumption are caused by small number of power plants, mainly baseload coal-fired generators. Replacing 8-10 existing units with modern natural gas combined cycle units would result in reductions of 19-29%, 51-55%, 60-62%, and 13-27% in CO2 emissions, NOx emissions, SOx emissions, and water consumption, respectively, across the ERCOT region for two different conversion scenarios.
Host: Kelly Sander
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Kaela Berry
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A Network-Centric Approach To Data Science: From Distributed Learning To Social Recommender Systems
Mon, Mar 07, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Zhenming Liu, Princeton University
Talk Title: A Network-Centric Approach To Data Science: From Distributed Learning To Social Recommender Systems
Abstract: Networks play important roles in various stages of a data science life cycle, including the design of scalable platforms, the collection of data, and the analysis of statistical models. I will talk about my efforts to develop a suite of network-based techniques in these stages. After briefly describing my work on designing scalable platforms for online machine learning algorithms and that for sampling data from the Web, I will discuss the details of a recent project that uses network analysis to study social recommender systems. A social recommender system leverages its users social connections to improve recommendation service. The recommender system we have designed simultaneously maximizes (a) an individuals benefit from using a social network and (b) the networks efficiency in disseminating information. The design solution brings together techniques from spectral analysis, random walk theory, and large-scale optimization.
Biography: Zhenming Liu received his PhD from Harvard University (working with Michael Mitzenmacher) and then spent two years as a postdoc at Princeton University (primarily working with Mung Chiang and Jennifer Rexford). Presently, he is a machine learning researcher for a quantitative hedge fund. Dr. Liu's research focus is the intersection of data science and network analysis; he designs both algorithms that analyze network structures inherent in the data (e.g., social and biological networks) and scalable platforms in support of big data analytics. He has received several awards for his research, including a Best Paper Runner Up at INFOCOM 2015 and a Best Student Paper Award at ECML/PKDD 2010.
Host: Professor Bhaskar Krishnamachari
Location: 248
Audiences: Everyone Is Invited
Contact: Theodore Low
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Seminars in Biomedical Engineering
Mon, Mar 07, 2016 @ 12:30 PM - 01:49 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Chethan Pandarinath, PhD, Postdoctoral Fellow Stanford University Departments of Neurosurgery and Electrical Engineering
Talk Title: Advancing brain-machine interfaces towards clinical viability
Host: K. Kirk Shung, PhD
More Information: Abstract_Bio_ Chethan Pandarinath.pdf
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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EE-EP Seminar - Mona Zebarjadi, Monday, March 7th in EEB 132 @ 2:00pm
Mon, Mar 07, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mona Zebarjadi, Rutgers University
Talk Title: Manipulation of Electricity and Heat at Nanoscales
Abstract: Fundamental understanding of electron and phonon transport enables us to manipulate electricity and heat at nanoscales. Advances in computations and in parallel in nanotechnology, allow design of specific quantum potentials inside materials and devices to guide charge and heat carriers. The resulting capability of manipulating electricity and heat inspires design of new electronic devices, power generators, and heat pumps. In this talk, I will discuss several examples to elucidate our approach. First I will discuss novel strategies to enhance materials carrier mobility and conductivity for design of fast transistors, switches, and thermoelectric modules via un-conventional doping schemes including 3D modulation-doping and invisible-doping. Transport modeling and experimental results on single layer graphene will be presented next as a design example of active and passive coolers. Finally, design of solid-state thermionic coolers in micron-scales (using Monte-Carlo simulations) as well as nano-scales (using first-principles modeling approach) will be described.
Biography: Mona Zebarjadi is a professor of mechanical engineering at Rutgers University. Her research interests are in electron and phonon transport modeling; materials and device design, fabrication and characterization; with emphasis on energy conversion systems such as thermoelectric, thermionic, and thermomagnetic power generators, and heat management in high power electronics and optoelectronic devices. She received her Bachelor's degree in physics from Sharif University in 2004 and her PhD in EE from UCSC in 2009, after which she spent 3 years at MIT as a postdoctoral fellow working jointly with electrical and mechanical engineering departments. She joined Rutgers University in January 2013. She is the recipient of 2014 AFOSR career award, 2015 A.W. Tyson assistant professorship award, MRS graduate student gold medal, and SWE electronics for imaging scholarship.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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EE 598 Cyber-Physical Systems Seminar Series
Mon, Mar 07, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Saman Zonouz, Assistant Professor at Rutgers University
Talk Title: Trustworthy Critical Infrastructures Threats, Challenges, and Countermeasures Applications
Abstract: Critical cyber-physical infrastructures, such as the power grid, integrate networks of computational and physical processes to provide the people across the globe with essential functionalities and services. Protecting these critical infrastructures is a vital necessity because the failure of these systems would have a debilitating impact on economic security and public health and safety. Our research and development projects aim at provision of real-world solutions to facilitate the secure and reliable operation of next-generation critical infrastructures and require interdisciplinary research efforts across adaptive systems and network security, cyber-physical systems, and trustworthy real-time detection and response mechanisms. In this talk, I will focus on real past and potential future threats against critical infrastructures and embedded devices, and discuss the challenges in design, implementation, and analysis of security solutions to protect cyber-physical platforms. I will introduce novel classes of working systems that we have developed to overcome these challenges in practice, and finally conclude with several concrete directions for future research.
Biography: Saman Zonouz is an Assistant Professor in the Electrical and Computer Engineering Department at Rutgers University since September 2014 and the Director of the 4N6 Cyber Security and Forensics Laboratory. His research has been awarded NSF CAREER Award in 2015, Google Security Reward in 2015, Top-3 Demo at IEEE SmartGridComm 2015, the Faculty Fellowship Award by AFOSR in 2013, the Best Student Paper Award at IEEE SmartGridComm 2013, the University EARLY CAREER Research award in 2012 as well as the Provost Research Award in 2011. The 4N6 research is currently supported by National Science Foundation (NSF), Department of Homeland Security (DHS), Office of Naval Research (ONR), Department of Energy (DOE), Advanced Research Projects Agency Energy (ARPA-E), Department of Education (DOE), Siemens Research Labs (SRL), WinRiver, GrammaTech, Google, and Fortinet Corporation including tech-to-market initiatives. Saman's current research focuses on systems security and privacy, trustworthy cyber-physical critical infrastructures, binary/malware analysis and reverse engineering, as well as adaptive intrusion tolerance architectures. Saman has served as the chair, program committee member, guest editor and a reviewer for top international conferences and journals. Saman serves on Editorial Board for IEEE Transactions on Smart Grid. He obtained his Ph.D. in Computer Science, specifically, intrusion tolerance architectures for the cyber-physical infrastructures, from the University of Illinois at Urbana-Champaign in 2011.
Host: Paul Bogdan
Location: 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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CAMS Colloquium: Shang-Hua Teng (USC) - Through the Lens of the Laplacian Paradigm: Big Data and Scalable Algorithms -- a Pragmatic Match Made On Earth
Mon, Mar 07, 2016 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Shang-Hua Teng, USC
Talk Title: Through the Lens of the Laplacian Paradigm: Big Data and Scalable Algorithms -- a Pragmatic Match Made On Earth
Abstract: In the age of Big Data, efficient algorithms are in higher demand now more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, the explosive growth of problem size has also significantly challenged the classical notion of efficient algorithms:
Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation.
In this talk, I will discuss the emerging Laplacian Paradigm, which has led to breakthroughs in scalable algorithms for several fundamental problems in network analysis, machine learning, and scientific computing. I will focus on three recent applications: (1) PageRank Approximation (and identification of network nodes with significant PageRanks). (2) Random-Walk Sparsification. (3) Scalable Newton's Method for Gaussian Sampling.
Biography: Dr. Shang-Hua Teng has twice won the prestigious Godel Prize in theoretical computer science, first in 2008, for developing the theory of smoothed analysis , and then in 2015, for designing the groundbreaking nearly-linear time Laplacian solver for network systems. Both are joint work with Dan Spielman of Yale --- his long-time collaborator. Smoothed analysis is fundamental for modeling and analyzing practical algorithms, and the Laplacian paradigm has since led to several breakthroughs in network analysis, matrix computation, and optimization. Citing him as, "one of the most original theoretical computer scientists in the world", the Simons Foundation named Teng a 2014 Simons Investigator, for pursuing long-term curiosity-driven fundamental research. He and his collaborators also received the best paper award at ACM Symposium on Theory of Computing (STOC) for what's considered to be the "first improvement in 10 years" of a fundamental optimization problem --- the computation of maximum flows and minimum cuts in a network. In addition, he is known for his joint work with Xi Chen and Xiaotie Deng that characterized the complexity for computing an approximate Nash equilibrium in game theory, and his joint papers on market equilibria in computational economics. He and his collaborators also pioneered the development of well-shaped Dalaunay meshing algorithms for arbitrary three-dimensional geometric domains, which settled a long-term open problem in numerical simulation, also a fundamental problem in computer graphics. Software based on this development was used at the University of Illinois for the simulation of advanced rockets. Teng is also interested in mathematical board games. With his former Ph.D. student Kyle Burke, he designed and analyzed a game called Atropos , which is played on the Sperner's triangle and based on the beautiful, celebrated Sperner's Lemma. In 2000 at UIUC, Teng was named on the List of Teachers Ranked as Excellent by Their Students for his class, "Network Security and Cryptography". He has worked and consulted for Microsoft Research, Akamai, IBM Almaden Research Center, Intel Corporation, Xerox PARC, and NASA Ames Research Center, for which he received fifteen patents for his work on compiler optimization, Internet technology, and social network.
Host: USC CAMS
Location: Kaprielian Hall (KAP) - 414
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Junior Faculty Candidate Mini-symposium: Department of Stem Cell Biology and Regenerative Medicine
Tue, Mar 08, 2016 @ 08:30 AM - 04:30 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: -, -
Talk Title: -
Abstract: 10:30 a.m.
Blood cell engineering and drug discovery using iPS cells
Sergei Doulatov, PhD
Boston Children's Hospital
11:45 a.m.
Cell diversity in liver regeneration and cancer development
Joan Font-Burgada, PhD
University of California, San Diego
2 p.m.
Self-renewal of human hematopoietic progenitor cells: From the clinic to the laboratory and back to the clinic
Hsiang-Ying (Sherry) Lee, PhD
3:15 p.m.
Towards engineering developmental systems: A new family of synthetic cell-cell communication pathways to control multicellular self-organization
Leonardo Morsut, PhD
University of California, San Francisco
4:30 p.m.
Development and evolution of the human cerebral cortex
Alexander Pollen, PhD
University of California, San Francisco
Reception to follow
Host: Department of Stem Cell Biology and Regenerative Medicine
More Info: https://stemcell.usc.edu/events/details/?event_id=919129
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: https://stemcell.usc.edu/events/details/?event_id=919129
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CS Colloquium: Andreas Haeberlen (U. of Pennsylvania) - Accountability for Distributed Systems
Tue, Mar 08, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andreas Haeberlen, U. of Pennsylvania
Talk Title: Accountability for Distributed Systems
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Many of our everyday activities are now performed online - whether it is banking, shopping, or chatting with friends. Behind the scenes, these activities are implemented by large distributed systems that often contain machines from several different organizations. Usually, these machines do what we expect them to, but occasionally they 'misbehave' - sometimes by mistake, sometimes to gain an advantage, and sometimes because of a deliberate attack.
In society, accountability is widely used to counter such threats.
Accountability incentivizes good performance, exposes problems, and builds trust between competing individuals and organizations. In this talk, I will argue that accountability is also a powerful tool for designing distributed systems. An accountable distributed system ensures that 'misbehavior' can be detected, and that it can be linked to a specific machine via some form of digital evidence. The evidence can then be used just like in the 'offline' world, e.g., to correct the problem and/or to take action against the responsible organizations.
I will give an overview of our progress towards accountable distributed systems, ranging from theoretical foundations and efficient algorithms to practical applications. I will also present one result in detail: a technique that can detect information leaks through covert timing channels.
Biography: Andreas Haeberlen is a Raj and Neera Singh Assistant Professor at the University of Pennsylvania. His research interests are in distributed systems, networking, and security. Andreas received his PhD degree in Computer Science from Rice University in 2009; he is the recipient of a NSF CAREER award, and he was awarded the Otto Hahn Medal by the Max Planck Society.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute Seminar - ISE 651
Tue, Mar 08, 2016 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Karen Smilowitz, Northwestern University
Talk Title: Logistical Challenges at Mass Participation Events: Operations Research Models for Marathon Planning
Host: John Carlsson
More Information: March 8, 2016_Smilowitz.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Michele ISE
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CS Colloquium: Hristo Paskov (Stanford) -Learning with N-Grams: from Massive Scales to Compressed Representations
Tue, Mar 08, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Hristo Paskov, Stanford
Talk Title: Learning with N-Grams: from Massive Scales to Compressed Representations
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
N-gram models are essential in any kind of text processing; they offer simple baselines that are surprisingly competitive with more complicated "state of the art" techniques. I will present a survey of my work for learning with arbitrarily long N-grams at massive scales. This framework combines fast matrix multiplication with a dual learning paradigm that I am developing to reconcile sparsity-inducing penalties with Kernels. The presentation will also introduce Dracula, a new form of deep learning based on classical ideas from compression. Dracula is a combinatorial optimization problem, and I will discuss some its problem structure and use this to visualize its solution surface.
The lecture will be available to stream HERE. Open in new window or tab for best results.
Biography: Hristo Paskov was born in Bulgaria and grew up in New York. He received a B.S. and M.Eng. in Computer Science from MIT while conducting research at the MIT Datacenter and Tomaso Poggio's group (CBCL). He is currently finishing a Ph.D. in Computer Science at Stanford under the advisement of John Mitchell and Trevor Hastie. His research spans machine learning, optimization, and algorithms in order to build large-scale statistical methods and data representations. He is developing a new deep learning paradigm that uses compression to find compact data representations that are useful for statistical inference. His work has provided state of the art methods for security and natural language processing.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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From Communication to Sensing and Learning: An Information Theoretic Perspective
Wed, Mar 09, 2016 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Hamed Hassani, Postdoctoral Scholar/ETH Zurich
Talk Title: From Communication to Sensing and Learning: An Information Theoretic Perspective
Abstract: We are witnessing a new era of science -” ushered in by our ability to collect massive amounts of data and by unprecedented ways to learn about the physical world. Beyond the challenges of storage and communication, there are new frontiers in the acquisition, analysis and exploration of data. In this talk, I will view these frontiers through the lens of information theory. I will argue that information theory lies at the center of data science, offering insights beyond its classical applications. As a concrete example, I will consider the problem of optimal data acquisition, a challenge that arises in active learning, optimal sensing and experimental design. Based on information theoretic foundations, and equipped with tools from submodular optimization theory, I will present a rigorous analysis of the widely-used sequential information maximization policy (also known as the information-gain heuristic). Our analysis establishes conditions under which this policy provably works near-optimally and identifies situations where the policy fails. In the latter case, our framework suggests novel, efficient surrogate objectives and algorithms that outperform classical techniques.
Biography: Hamed Hassani is a post-doctoral scholar at the Institute for Machine Learning at ETH Zurich. He received a Ph.D. degree in Computer and Communication Sciences from EPFL, Lausanne. Prior to that, he received a B.Sc. degree in Electrical Engineering and a B.Sc. degree in Mathematics from Sharif University of Technology, Tehran. Hamed's fields of interest include machine learning, coding and information theory as well as theory and applications of graphical models. He is the recipient of the 2014 IEEE Information Theory Society Thomas M. Cover Dissertation Award. His co-authored paper at NIPS 2015 was selected for an oral (plenary) presentation, and his co-authored paper at ISIT 2015 received the IEEE Jack Keil Wolf ISIT Student Paper Award.
Host: Professor Urbashi Mitra, ubli@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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CS Colloquium: XiaoFeng Wang (Indiana University at Bloomington) - Security Innovations in the Big-Data Era
Wed, Mar 09, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: XiaoFeng Wang, Indiana University at Bloomington
Talk Title: Security Innovations in the Big-Data Era
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The rapid progress in computing has produced a huge amount of data, which will continue to grow in the years to come. In this big-data era, we envision that tomorrow's security technologies will be data-centric: new defense will become smart and proactive by using the data to understand what the attackers have already done, what they are about to do, what their strategies and infrastructures are; effective protection will be provided for dissemination and analysis of the data involving sensitive information on an unprecedented scale. In this talk, I report our first step toward this future of secure computing. We show that through effective analysis of over a million Android apps, previously unknown malware can be detected within a few seconds, without resorting to conventional Anti-Virus means such as signatures and behavior patterns. Also, by leveraging trillions of web pages indexed by search engines, we can capture tens of thousands of compromised websites (including those of government agencies like NIH, NSF and leading education institutions world-wide) by simply asking Google and Bing right questions and automatically analyzing their answers through Natural Language Processing. Further, we found that an in-depth understanding about the unique features of human genomes and how they are used in biomedical research and healthcare systems can help us find a highly efficient way to protect patient privacy during a large-scale genome analysis. Our findings indicate that by unlocking the great value of data, we can revolutionize the security landscape, making tomorrow security technologies more intelligent and effective.
Biography: Dr. XiaoFeng Wang is a professor in the School of Informatics and Computing at Indiana University, Bloomington. He received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2004, and has since been a faculty member at IU. Dr. Wang is a well-recognized researcher on system and network security. His work focuses on cloud and mobile security, and data privacy. He is a recipient of 2011 Award for Outstanding Research in Privacy Enhancing Technologies (the PET Award) and the Best Practical Paper Award at the 32nd IEEE Symposium on Security and Privacy. His work frequently receives attention from media, including CNN, MSNBC, Slashdot, CNet, PC World, etc. Examples include his discovery of security-critical vulnerabilities in payment API integrations (http://money.cnn.com/2011/04/13/technology/ecommerce_security_flaw/) and his recent study of the security flaws on the Apple platform (http://money.cnn.com/2015/06/18/technology/apple-keychain-passwords/). His research is supported by the NIH, NSF, Department of Homeland Security, the Air Force and Microsoft Research. He is the director of IU's Center for Security Informatics.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Computational Imaging for Real-Time Gigapixel and 3D Wave-Field Microscopy
Wed, Mar 09, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Lei Tian, Postdoctoral Associate / Dept of EECS, University of California, Berkeley
Talk Title: Computational Imaging for Real-Time Gigapixel and 3D Wave-Field Microscopy
Abstract: Abstract: Computational imaging is a new frontier of imaging technology that overcomes fundamental limitations of conventional systems by jointly designing optics, devices, signal processing, and algorithms. In this talk, I will present recent advancements in computational wave-field imaging that enable Gigapixel and 3D phase microscopy capability, breaking the limit of space-time-bandwidth product in traditional systems. In particular, I will describe a computational microscopy platform that implements coded illumination and nonlinear phase retrieval algorithms to reconstruct wide field-of-view and high-resolution phase images. Further, new illumination multiplexing techniques reduce data requirements by one order of magnitude, and acquisition times from minutes to sub-second. Experiments demonstrate quantitative dynamic imaging of rare biological events across multiple scales in both space and time. Finally, new 3D wave-optical model and reconstruction technique allow Gigavoxel reconstruction of 3D objects, achieving lateral resolution and depth sectioning well beyond the physical limit of traditional systems. Such computational imaging approach creates significant new capabilities by integrating hardware and computation at the system level. It promises wide applications, such as biomedicine, metrology, inspection, security and X-ray.
Biography: Bio: Lei Tian is a postdoctoral associate in the department of Electrical Engineering and Computer Sciences at University of California Berkeley. He received his Ph.D. in 2013 and M.S. in 2010, both from Massachusetts Institute of Technology (MIT). His research interests include computational imaging, computational-optical instrumentation, phase retrieval, imaging through 3D complex media, large-scale microscopy, and their applications in biomedicine, security, metrology, inspection, X-ray and EUV.
Dr. Tian is the author of over 30 peer-reviewed articles and is a named inventor on 3 US patent applications. His recent work on coded illumination for Gigapixel imaging was awarded the Best Paper in Optical Society of America (OSA) Imaging Systems and Applications conference (2014). His work on optical coherence recovery using low-rank method was awarded the Emil Wolf Best Student Paper in OSA Frontier in Optics annual meeting (2011). Dr. Tian is currently serving as conference chair and program committee member in multiple conferences of OSA, SPIE, and IEEE.
Host: Dr. Justin Haldar, jhaldar@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Mar 09, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Na Li, Harvard University
Talk Title: Distributed Energy Management with Limited Communication
Series: CommNetS
Abstract: A major issue in future smart grid is how intelligent devices and independent producers can respectively change their power consumption/production to achieve near maximum efficiency for the power network. Limited communications between devices, producers etc. necessitates an approach where the elements of the network can act in an autonomous manner with limited information/communications yet achieve near optimal performance. In this talk, I will present our recent work on distributed energy management with limited communication. In particular, I will show how we can extract information from physical measurements and recover information from local computation. We will investigate the minimum amount of communication for achieving the optimal energy management and study how limited communication affects the convergence rate of the distributed algorithms.
Biography: Na Li is an assistant professor in Electrical Engineering and Applied Mathematics of the School of Engineering and Applied Sciences in Harvard University since 2014. She received her PhD degree in Control and Dynamical systems from California Institute of Technology in 2013 and was a postdoctoral associate of the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology. Her research lies in the design, analysis, optimization and control of distributed network systems, with particular applications to power networks. She received NSF career award (2016) and entered the Best Student Paper Award ï¬nalist in the 2011 IEEE Conference on Decision and Control.
Host: Prof. Ashutosh Nayyar
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Aerospace and Mechanical Engineering Candidate Series
Wed, Mar 09, 2016 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Janna Nawroth, Postdoctoral Technology Development Fellow at the Wyss Institute for Biologically Inspired Engineering at Harvard University, Cambridge, MA
Talk Title: Multiscale Fluid Sensing and Transport in Biological and Engineered Systems
Abstract: Deformable substrates mediate fluid transport and sensing in many biological systems (e.g., marine animals, inner organs), as well as in some engineered systems (soft microfluidics, soft robots). The latter, however, employ only a fraction of the multitude of mechanisms found in nature. Partly, this reflects the difficulty of isolating straightforward structure-function relationships in multiscale biological tissues that could be translated to engineered materials. The same difficulty has impeded the development of in vitro assays and diagnostics tools for (fluid-) mechanically mediated diseases, such as polycystic kidney syndrome, hearing loss, osteoporosis, and cardiomyopathy. I approach this challenge by studying native and engineered tissues specialized for a particular transport function, which enables me to isolate, quantify, and reverse-engineer selected structure-function relationships. For this, I combine the powers of flow visualization, microfluidic platforms, tissue engineering, and computational studies. Here, I will present major results and goals of my research including (1), quantifying the structure-function relationships of muscle and cilia in health and disease, with applications in biophysical studies, diagnostics, and drug discovery ("organs-on-chips"); (2), designing and building cell-based microfluidic analyzers and processors; and (3), developing biologically-inspired multiscale surfaces for controlling dynamic fluid-structure interactions, such as biofilm formation.
Biography: Janna C. Nawroth is a postdoctoral Technology Development Fellow at the Wyss Institute for Biologically Inspired Engineering at Harvard University. She attended Heidelberg University, Germany, where she received her B.S. (2004) and M.S. (2007) in Biotechnology. For her master thesis, Nawroth joined Yale University as a research associate in computational biology with Professor Gordon Shepherd. After Yale, Nawroth attended the California Institute of Technology as a Moore Fellow and obtained her Ph.D. (2012) in Biology. Nawroth's Ph.D. research, with Professor John Dabiri, received Caltech's award for the Best Thesis in Nanotechnology and involved the study and design of muscle-powered pumps to manage microfluidic propulsion and particle transport. After her Ph.D., Nawroth spent a year as a Caltech Postdoctoral Fellow in Aeronautics collaborating with Professors John Dabiri, Eva Kanso (USC), Scott Fraser (USC), and Margaret-McFall-Ngai (U Hawaii) to study transport phenomena in ciliated surfaces. At the Wyss, she develops microfluidic devices and signal processing algorithms for exploring the mechanics and flow physics of dynamic tissues for applications in biomedical engineering, disease modeling, and biophysical research.
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Valerie Childress
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Inferring Network Internal State via Network Tomography
Thu, Mar 10, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ting He, IBM
Talk Title: Inferring Network Internal State via Network Tomography
Abstract: Timely and accurate knowledge of network internal state (e.g., delay/loss/jitter on links) is essential to efficient network operation and resource allocation. Obtaining such knowledge is, however, a highly nontrivial task in large-scale heterogeneous networks, where the existence of heterogeneous domains due to the difference in communication technology, protocol, ownership, and/or policy makes it difficult for a single monitoring system to receive global support throughout the network.
In this talk, I will review a promising approach for monitoring such networks by inferring the internal network state (e.g., link delay) from end-to-end measurements (e.g., path delay) taken between monitors, known as network tomography. The focus will be given to a key challenge in applying network tomography, called "lack of identifiability", i.e., the measurements cannot uniquely determine the network state. In contrast to previous works that resort to best-effort heuristics, I aim at guaranteeing identifiability via carefully designed measurements. Specifically, I (i) establish the fundamental condition on the network topology and monitor placement to achieve identifiability, (ii) develop the optimal monitor placement algorithm that guarantees identifiability using a minimum number of monitors, (iii) develop an efficient path construction algorithm that finds a set of linearly independent paths, whose measurements can uniquely determine the link metrics, and (iv) design probing experiments that allocate probes among the paths to minimize the error in estimating link parameters in the case of random link metrics. The above results are selected from publications at ICDCS'13, IMC'13, TON'14, and SIGMETRICS'15, and have won several best paper awards and nomination.
Biography: Ting He is a Research Staff Member at IBM T.J. Watson Research Center, where she has worked for over 8 years in the Wireless Network Research Group and the Network Analytics Research Group. Before that, she was a Graduate Research Assistant at the Adaptive Communications and Signal Processing Group at Cornell University in 2003-2007, where she received the Ph.D. degree in Electrical Engineering.
At IBM, Dr. He has worked as a primary researcher and task lead in several major research programs including the International Technology Alliance (ITA) program funded by US ARL and UK MoD, the Measurement Science in Cloud Computing program funded by NIST, and the Social Media in Strategic Communication (SMISC) program funded by DARPA. Her work aims at applying mathematical principles developed in signal processing, graph theory, information theory, stochastic optimization, and online learning to practical problems arising in the broad areas of network monitoring, performance analysis, and optimization.
Dr. He is a senior member of IEEE. She has served as the Membership Co-chair of ACM N2Women and the TPC of many communications and networking conferences, including IEEE INFOCOM, IEEE SECON, IEEE/ACM IWQoS, IEEE WiOpt, IEEE MILCOM, IEEE ICNC, IFIP Networking, etc. She received the Outstanding Contributor Award from IBM Research in 2009 and 2013, and the Most Collaboratively Complete Publications Award from ITA in 2015. Her papers won the Best Paper Award at IEEE ICDCS'13, the Outstanding Student Paper Award at ACM SIGMETRICS'15, the Best Paper Nomminee at ACM IMC'13, and the Best Student Paper Award at IEEE ICASSP'05. She received the Distinguished TPC Member Award for her service at IEEE INFOCOM'16. In school, she was an Outstanding College Graduate of Beijing and and an Outstanding Gradudate of Peking University in 2003, and a winner of the Excellent Student Awards and scholarships from Peking University from 1999 to 2002.
Host: Professor Bhaskar Krishnamachari
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Theodore Low
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The Pulmonary Challenge: Innovations in Lung Development, Stem Cells and Regeneration
Fri, Mar 11, 2016 @ 08:00 AM - 04:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: various, various
Talk Title: various
Abstract: The Hastings Center for Pulmonary Research (HCPR) at the Keck School of Medicine of USC presents an inaugural symposium at the University of Southern California's Health Sciences Campus located in Los Angeles, CA on Friday, March 11, 2016.
Host: Hastings Center for Pulmonary Research (HCPR)
More Info: https://www.eventbrite.com/e/the-pulmonary-challenge-innovations-in-lung-development-stem-cells-regeneration-tickets-19893233196
Location: Harlyne J. Norris Research Tower (NRT) - Aresty Auditorium
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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USC CCMB Symposium
Fri, Mar 11, 2016 @ 11:00 AM - 05:30 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: various, various
Talk Title: various
Abstract: "Head formation in mouse: Attributes of the gene regulatory network," Patrick Tam, Children's Medical Research Institute, University of Sydney
"Dental pulp mesenchymal stem cells in tooth growth," Paul Sharpe, Craniofacial Development and Stem Cell Biology, King's College London
"Epithelial-mesenchymal interactions during tooth development," Irma Thesleff, Institute of Biotechnology, University of Helsinki
"Using next generation sequencing to define monogenic causes of craniosynostosis," Andrew Wilke, Radcliffe Department of Medicine, University of Oxford
Host: Center for Craniofacial Molecular Biology (CCMB)
More Info: http://ccmbsymposium.usc.edu/
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: http://ccmbsymposium.usc.edu/
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PhD Seminar
Fri, Mar 11, 2016 @ 11:00 AM - 12:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Evangelos Pantazis, PhD Candidate, University of Southern California
Talk Title: A design methodology framework at the intersection of Architecture, Engineering and construction using Multi-Agent Systems
Abstract: This talk will review research on the prototyping of multi-agent systems (MAS) for architectural design. It proposes a design exploration methodology at the intersection of architecture, engineering, and construction. The motivation of the work includes exploring bottom up generative methods coupled with optimizing performance criteria including geometric complexity and objective functions for environmental, structural and fabrication parameters. The work focuses on the development of a design methodology and initial experiments to provide design solutions, which simultaneously satisfy complexly coupled and often contradicting objectives.
The prototypical experiments vary in complexity and focus on different design domains, namely: a) facade design, and the development of non structural building component (facade panels) that is adjusted based on the environmental performance of the facade b) shell design; and the development of structural building component (reciprocal frames) for form finding actual construction of lightweight shell structures using digital and robotic fabrication.
The developed system and algorithms are described, and initial results of the multi-agent derived efficiencies are presented
Biography: Evangelos Pantazis is currently pursuing a PhD at the Viterbi School of Engineering, Dept. of Civil and Environmental Engineering at the University of Southern California. Evangelos holds a Masters of Advanced studies in the field of Computer Aided Architectural Design from the ETH in Zurich (2012). He received his Diploma in Architecture with honors from Aristotles University of Thessaloniki (2010), and has also graduated from the MOKUME jewelry design school in 2007, where he was trained as a jeweler.
Professionally, Evangelos has gained experience in several international offices, including Graft in Berlin, Germany, Melhado Architectes in Londrina, Brasil and Studio Pei Zhu in Beijing/China. Soon after, he co-founded Topotheque design office, a studio that focuses on computational design with its various tangent disciplines such architecture, product deisgn and graphic and plastic art. His latest work is focusing on non-linear design strategies for architectural and design purposes, their integration with performance analyses and their materialization using digital fabrication.
Host: Burcin Becerik-Gerber
Location: 101
Audiences: Everyone Is Invited
Contact: Kaela Berry
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Munushian Seminar - Mark Lundstrom, Friday, March 11th at 2:00pm in EEB 132
Fri, Mar 11, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mark Lundstrom, Purdue University
Talk Title: Electrons and Phonons in Nanodevices
Abstract: The theory of electron transport in semiconductors has developed, evolved, and matured alongside the development of semiconductor technology from microelectronics to nanoelectronics. The field of thermal transport has an even longer history beginning with Fourier's Law, but electrons and phonons always go together - sometimes it is a problem, as in the self-heating of electronic devices, and sometimes it is the whole point, as in thermoelectrics. My goal in this talk is to discuss the remarkably simple conceptual picture of electron transport at the nanoscale that has emerged from decades of work on experiments and sophisticated transport theory and simulations and to explore its application to thermal transport. Two central concepts are the Landauer approach and the McKelvey-Shockley two-flux form of the Boltzmann equation. I'll discuss the similarities and differences of electron and phonon transport and some new insights into thermal transport that come from using concepts from electronics. Finally, I'll identify some issues that need to be addressed if we are to develop the comprehensive conceptual and computational framework for electrothermal transport that is needed to describe modern nanodevices.
Biography: Mark Lundstrom is the Don and Carol Scifres Distinguished Professor of Electrical and Computer Engineering at Purdue University. He received Ph.D. from Purdue University in 1980 and BEE and MSEE degrees from the University of Minnesota in 1973 and 1974. Between his MSEE and Ph.D. degrees, he worked at Hewlett-Packard Corporation on integrated circuit process development and manufacturing. At Purdue, his research has explored a wide range of semiconductor devices, the physics of carrier transport, and the modeling and numerical simulation of devices. His current focus is on energy conversion devices such as solar cells and thermoelectric devices and on the physics of the ultimate transistor. Lundstrom was the founding director of the Network for Computational Nanotechnology and nanoHUB.org, a science gateway that now serves a worldwide nanotechnology community of more than 300,000 individuals. He currently leads NEEDS, an NSF and industry-funded, multi-university initiative focused on new-era electronics, and he leads the nanoHUB-U initiative for on-line education. Dr. Lundstrom is a fellow of the Institute of Electrical and Electronic Engineers (IEEE), the American Physical Society, and the American Association for the Advancement of Science (AAAS). He has received several awards for his teaching and research, and is a member of the U.S. National Academy of Engineering.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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NL Seminar-Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
Fri, Mar 11, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sahil Garg, USC/ISI
Talk Title: Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
Series: Natural Language Seminar
Abstract: We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.
Biography: Sahil Garg is a PhD student, advised by Prof. Aram Galstyan, in computer science department of Viterbi school of engineering at University of Southern California. He is interested in problem oriented research. In the past, he developed machine learning, information theoretic algorithms for real world problems such as sensing environmental dynamics using mobile robotic sensors. In this talk, he is going to discuss his recent work on extracting bio-molecular interactions from bio-medical text using semantic parsing, especially in relevance to Cancer disease.
Host: Xing Shi 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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USC Energy Institute Seminar
Fri, Mar 11, 2016 @ 03:00 PM - 04:00 PM
Conferences, Lectures, & Seminars
Speaker: Dr. David Brown, ARPA-E Fellow, U.S. Department of Energy
Talk Title: How Can Technology Enable Greenhouse Gas Mitigation through Agriculture?
Host: USC Energy Institute
More Information: USCEI 2016 Seminar Series 031116.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Juli Legat
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Seminars in Biomedical Engineering
Mon, Mar 14, 2016 @ 12:30 PM - 01:49 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Monika Jadi, PhD, Postdoctoral Fellow Computational Neurobiology Laboratory Salk Institute for Biological Studies
Talk Title: Computation structures in the brain
Abstract: Discovering computational principles that drive information processing in the brain is critical to understanding neural correlates of sensory processing, brain-state and behavior. Structure in the nervous system, from a synapse to networks of neurons, is thought to have functional consequences on information processing in the brain. I will describe my work investigating the structural rules of synaptic signaling in individual neurons. Using biophysically inspired cable theory based models, this work has revealed a novel computational role for suppressive signaling that targets the distal tips of dendrites in post-synaptic neurons. At the network level, I have investigated structural rules of contextual processing, specifically those pertaining to oscillatory activity. Using analytically tractable as well as spiking models , my work proposes a novel mechanism for regulation of cortical oscillations that depends on the relative balance of signaling to subclasses of neurons. Finally, I will describe my recent work on neural correlates of attentive behavior along a cortical column, a canonical structure in the sensory cortex. Using machine-learning techniques, I find trial-by-trial predictors of attentive behavior that precede the sensory discriminandum as well as the resulting behavioral choice by several seconds.
Host: K. Kirk Shung, PhD
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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USC Stem Cell Seminar: Bill Lowry, UCLA
Tue, Mar 15, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Bill Lowry, UCLA
Talk Title: Manipulation of maturation in the human neural lineage
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: Our lab focuses on understanding the molecular mechanisms underlying cell fate decisions during development. We use in vitro models of human development with pluripotent stem cells to uncover the mechanisms by which cells acquire particular fates after germ layer specification. In a collaboration with the Plath lab, we reprogrammed human somatic cells to a pluripotent state and now use hiPSCs to model both development and disease in vitro. We have defined factors that affect the maturation of the neural progenitors as they produce either neurons or glia.
Host: Henry Sucov
More Info: https://calendar.usc.edu/event/speaker_bill_lowry_ucla?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.Vtj6pinFl04
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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Anthropomorphic Digital Reference Objects: Extensible Tools for Evaluating Quantitative Imaging Algorithms
Fri, Mar 18, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ryan Bosca , University of Wisconsin
Talk Title: Anthropomorphic Digital Reference Objects: Extensible Tools for Evaluating Quantitative Imaging Algorithms
Series: Medical Imaging Seminar Series
Abstract: Assessing and mitigating the various sources of bias and variance associated with MR image quantification algorithms (e.g., pharmacokinetic modeling of dynamic contrast enhanced MRI) is essential to the use of such algorithms in clinical research and practice. Grid based digital reference objects (DRO) have been used traditionally to assess such algorithms. More recently, a number of publicly available, normal patient derived, digital anthropomorphic tissue models have been developed. By assigning physical parameters to these tissue models in conjunction with a physiological model, for example, the general kinetic model (GKM), a new DRO can be generated. Furthermore, by incorporating a disease state (e.g., a tumor), the DRO can be made to more realistically represent standard operating conditions for quantitative imaging algorithms. Specifically, such DROs provide a means of assessing algorithm performance across a model parameter space and facilitating investigation of any spatially dependent biases and variances. This talk will provide an overview of the methodology for generating such DROs in addition to illustrating some potential example applications.
Biography: Dr. Bosca is a research associate in Medical Physics at the University of Wisconsin in Madison. He received his master degree in physics from the University of North Texas and his PhD in medical physics from the Graduate School of Biomedical Sciences at The University of Texas Health Science Center in Houston. His research interests include quantitative MR imaging, particularly pharmacokinetic modeling, the development of realistic digital phantoms to aid in assessing and mitigating sources of bias and variance in quantitative algorithms, and the application of multi parametric quantitative MR imaging (e.g., combing quantitative imaging biomarkers from dynamic contrast enhanced, diffusion tensor, dynamic susceptibility contrast MR techniques) for assessing treatment response. In addition, he is keenly interested in the development and validation of freely available, open source, software tools for use in quantitative imaging studies.
Host: Professor Krishna Nayak
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Chip-Based Stimulated Brillouin Scattering for Low-Power Microwave Photonic Signal Processing Applications
Fri, Mar 18, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Benjamin J. Eggleton, University of Sydney
Talk Title: Chip-Based Stimulated Brillouin Scattering for Low-Power Microwave Photonic Signal Processing Applications
Abstract: The last few years have seen major progress in harnessing on-chip photon-phonon interactions, leading to a wide range of demonstrations of new functionalities. Utilizing not only the optical response of a nonlinear waveguide - but also hypersound acoustic resonances - enables the realization of microwave devices with unprecedented performance. Here we overview on-chip Stimulated Brillouin scattering (SBS) with special emphasis on reconfigurable and broadband microwave signal processing schemes. We review the different material platforms and structures for on-chip SBS, ranging from chalcogenide rib waveguides to CMOS compatible hybrid silicon/silicon-nitride structures and silicon nanowires. We show that the paradigm shift in SBS research - from long length of fibers to chip scale devices - is now moving towards fully integrated photonic-phononic CMOS chips.
Biography: Professor Benjamin Eggleton is an ARC Laureate Fellow and Professor of Physics at the University of Sydney and Director of the ARC Centre for Ultrahigh bandwidth Devices for Optical Systems (CUDOS). He obtained his PhD degree in Physics from the University of Sydney, in 1996 and then joined Bell Laboratories, Lucent Technologies as a Postdoctoral Member of Staff. In 2000, he was promoted to Director within the Specialty Photonics Division of Bell Laboratories, where he was engaged in forward-looking research supporting Lucent Technologies business in optical fiber devices. He returned to the University of Sydney in 2003 as the founding Director of CUDOS and Professor in the School of Physics.
Professor Eggleton is a Fellow of the Optical Society of America, IEEE Photonic and the Australian Technology, Science and Engineering Academy (ATSE).He was the recipient of the 2011 Eureka Prize for Leadership in Science, the Walter Boas Medal of the Australian Institute of Physics and the OSA's Adolph Lomb Medal. Eggleton has published about 400 journal papers which have been cited >15,000 times with an h-number of > 59 (webofscience). He was President of the Australian Optical Society, is currently Editor-in-Chief for APL Photonics and serves on the Board of Governors for IEEE Photonics.
Host: Alan Willner, x04664, willner@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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CS Colloquium: Andrew Gordon Wilson (CMU) -Scalable Gaussian Processes for Scientific Discovery
Mon, Mar 21, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andrew Gordon Wilson, CMU
Talk Title: Scalable Gaussian Processes for Scientific Discovery
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Every minute of the day, users share hundreds of thousands of pictures, videos, tweets, reviews, and blog posts. More than ever before, we have access to massive datasets in almost every area of science and engineering, including genomics, robotics, and astronomy. These datasets provide unprecedented opportunities to automatically discover rich statistical structure, from which we can derive new scientific discoveries. Gaussian processes are flexible distributions over functions, which can learn interpretable structure through covariance kernels. In this talk, I introduce a Gaussian process framework which is capable of learning expressive kernel functions on massive datasets. I will show how this framework generalizes a wide family of scalable machine learning approaches, leverages the inductive biases of deep learning architectures, and allows one to exploit existing model structure for significant further gains in scalability and accuracy, without requiring severe assumptions. I will then discuss how we can use this framework for reverse engineering human learning biases, crime prediction using point processes, image inpainting, video extrapolation, modelling change points and the impacts of vaccine introduction, and discovering the structure and evolution of stars.
Biography: Andrew Gordon Wilson is a Postdoctoral Research Fellow in the Machine Learning Department at Carnegie Mellon University working with Eric Xing and Alexander Smola. Andrew received his PhD in machine learning from the University of Cambridge in 2014, supervised by Zoubin Ghahramani. Andrew's research interests include probabilistic machine learning, scalable inference, Gaussian processes, kernel methods, Bayesian modelling, nonparametrics, and deep learning. Andrew's work has received several awards, including the G-Research Outstanding Dissertation Award in 2014 and the Best Student Paper Award at the Conference on Uncertainty in Artificial Intelligence in 2011.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Mon, Mar 21, 2016 @ 12:30 PM - 01:49 PM
Conferences, Lectures, & Seminars
Speaker: Bryan Smith, Ph.D., Instructor, Department of Radiology Stanford University, Stanford, California
Talk Title: Nanoparticle imaging: Shifting paradigms to transform nanomedical diagnosis and therap
Biography: After receiving his Bachelors degree in Physics, Mathematics, and Biomedical Engineering at Tufts University, Bryan Smith completed his Ph.D. in Biomedical Engineering as an NSF Fellow at The Ohio State University working in cancer nanotechnology. He moved to Stanford University for his post-doctoral work, where he was awarded a Stanford Molecular Imaging Scholar NIH Fellowship as well as a Stanford Dean's Fellowship. He was granted a K99/R00 NIH Pathway to Independence award for his work in nanomedical imaging
Host: K. Kirk Shung, PhD
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Addressing Spectrum Scarcity through Optical Wireless Communications
Mon, Mar 21, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mohamed-Slim Alouini, King Abdullah University of Science and Technology
Talk Title: Addressing Spectrum Scarcity through Optical Wireless Communications
Abstract: Rapid increase in the use of wireless services over the last two decades has lead the problem of the radio-frequency (RF) spectrum exhaustion. More specifically, due to this RF spectrum scarcity, additional RF bandwidth allocation, as utilized in the recent past, is not anymore a viable solution to fulfill the demand for more wireless applications and higher data rates. The talk goes first over the potential offered by optical wireless communications to relieve spectrum scarcity. It then summarizes some of the challenges that need to be surpassed before such kind of systems can be massively deployed. Finally the talk offers an overview of some of the recent results for the determination of the capacity of optical wireless channels.
Biography: Mohamed-Slim Alouini (S'94, M'98, SM'03, F'09) was born in Tunis, Tunisia. He received the Ph.D. degree in Electrical Engineering from the California Institute of Technology (Caltech), Pasadena, CA, USA, in 1998. He served as a faculty member in the University of Minnesota, Minneapolis, MN, USA, then in the Texas A&M University at Qatar, Education City, Doha, Qatar before joining King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah Province, Saudi Arabia as a Professor of Electrical Engineering in 2009. His current research interests include the modeling, design, and performance analysis of wireless communication systems.
Host: Andreas Molisch, molisch@usc.edu EEB 530, x04670
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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EE 598 Cyber-Physical Systems Seminar Series
Mon, Mar 21, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Marilyn Wolf, Georgia Institute of Technology
Talk Title: Service-oriented Architectures for Cyber-physical Systems
Abstract: Service-oriented architectures are widely used in information processing and Web technologies to provide scalable access to resources in distributed systems and extensible applications. However, many traditional service-oriented architectures are designed for transaction processing. In contrast, cyber-physical systems used for real-time control require quality-of-service constraints and graceful handling of failures to provide requested services. Our group is developing extended service-oriented models for use in smart energy grids and other applications. We will describe our work in service-oriented architectures, including model-based design and simulation.
Biography: Marilyn Wolf is Rhesa S. "Ray" Farmer Distinguished Chair in Embedded Computing Systems and Georgia Research Alliance Eminent Scholar at the Georgia Institute of Technology. She received her BS, MS, and PhD in electrical engineering from Stanford University in 1980, 1981, and 1984, respectively. She was with AT&T Bell Laboratories from 1984 to 1989. She was on the faculty of Princeton University from 1989 to 2007. Her research interests include cyber-physical systems, embedded computing, embedded video and computer vision, and VLSI systems. She has received the ASEE Terman Award and IEEE Circuits and Systems Society Education Award. She is a Fellow of the IEEE and ACM and an IEEE Computer Society Golden Core member.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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EE-EP Seminar - Dion Khodagholy, Monday, March 21st at 2:00pm in EEB 132
Mon, Mar 21, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dion Khodagholy, New York University Langone Medical Center
Talk Title: Large-Scale Organic Neural Interface Devices
Abstract: Recording from neural networks at the temporal resolution of action potentials is critical for understanding how information is processed in the brain. We developed an organic, conformable, biocompatible and scalable neural interface electrode and transistor arrays that can record both Local Field Potential (LFP) and extracellular action potentials without penetrating the brain surface. We recorded spiking activity in both rodent experiments and intra-operatively in patients undergoing epilepsy surgery using a large-scale surface probe designed to enable localization of fine LFP activity and the underlying neuronal entrainment. Large-scale, chronically recorded data generated by these devices has broad applicability to the understanding of physiologic and pathologic network activity, control of brain-machine interfaces, and therapeutic closed-loop devices.
Biography: Dion Khodagholy is a fellow at the Simon's Society of Fellows and a postdoctoral research associate in Prof. GyoÌrgy BuzsaÌki's laboratory at New York University Langone Medical Center (NYULMC). He received his Masters degree from University of Birmingham (UK) in Electronic and Communication Engineering. This was followed by a second Masters degree in Microelectronics at Ecole des Mines (France) combined with industry experience at Microelectronic Center of Provence. He attained his Ph.D. degree in Microelectronics at the Department of Bioelectronics (BEL) of Ecole des Mines with Prof. George Malliaras. At BEL, he focused on understanding organic semiconductor device physics and developing organic-based devices to interface with biology. His postdoctoral research at NYULMC is focused on three main domains: (i) design and development of large-scale, organic material-based neural interface devices; (ii) analysis of neural data acquired by these devices to understand large-scale neural network function; (iii) translation of these advancements to neural data acquisition systems in patients with epilepsy. His research explores the interface of electronics and the brain in the context of both applied and discovery sciences, with the ultimate goal of innovating in device engineering and neuroscience methods to improve diagnosis and treatment of neuropsychiatric disease.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CANCELLED — USC Stem Cell Seminar: Craig Jordan, University of Colorado Denver
Tue, Mar 22, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Craig Jordan, University of Colorado Denver
Talk Title: Metabolic properties of human leukemia stem cells
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: This seminar will be rescheduled.
Host: Rong Lu
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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Network Approaches to Data-Driven Problems: Fundamental Limits, Scalable Algorithms, and Applications
Tue, Mar 22, 2016 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Soheil Feizi, Massachusetts Institute of Technology
Talk Title: Network Approaches to Data-Driven Problems: Fundamental Limits, Scalable Algorithms, and Applications
Abstract: In large-scale data-driven problems, network modeling provides a unifying framework to succinctly represent data, reveal underlying data structures, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. In this talk, I will illustrate the use of spectral, combinatorial, and statistical inference techniques in learning the network topology and subsequent network analysis.
First, we introduce Network Maximal Correlation (NMC), a multivariate measure of nonlinear association suitable for large datasets. NMC infers transformations of variables to reveal underlying nonlinear dependencies among them. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning graphical models when variables have unknown nonlinear dependencies. Next, we discuss the problem of network alignment that aims to find a bijective mapping across two graphs so that, if two nodes are connected in one graph, their images are also connected in the other graph. This problem has a broad range of applications for comparative network analysis in systems biology, social sciences and engineering areas. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency, theoretically and experimentally, over several synthetic and real networks.
Biography: Soheil Feizi is a Ph.D. candidate in the Electrical Engineering and Computer Science (EECS) Department at Massachusetts Institute of Technology (MIT), co-supervised by Prof. Muriel Médard and Prof. Manolis Kellis. His research focuses on complex network analysis using tools and concepts from optimization, machine learning, statistical inference and information theory. Previously, he completed a M.Sc. in Electrical Engineering at MIT, where he received the Jacobs Presidential Fellowship and EECS Great Educators Fellowship, as well as an Ernst Guillemin Award for his Master of Science Thesis. He also received the best student award in Electrical Engineering at Sharif University of Technology from where he holds his B.Sc.
Host: Salman Avestimehr
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Suzanne Wong
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CS Colloquium: Olga Russakovsky (CMU) - The Human Side of Computer Vision
Wed, Mar 23, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Olga Russakovsky, Carnegie Mellon University
Talk Title: The Human Side of Computer Vision
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Intelligent agents acting in the real world need advanced vision capabilities to perceive, learn from, reason about and interact with their environment. In this talk, I will explore the role that humans play in the design and deployment of computer vision systems. Large-scale manually labeled datasets have proven instrumental for scaling up visual recognition, but they come at a substantial human cost. I will first briefly talk about strategies for making optimal use of human annotation effort for computer vision progress. However, no dataset can foresee all the visual scenarios that a real-world system might encounter. I will argue that seamlessly integrating human expertise at runtime will become increasingly important for open-world computer vision. I will introduce, and demonstrate the effectiveness of, a rigorous mathematical framework for human-machine collaboration. Looking ahead, in order for such collaborations to become practical, the computer vision algorithms we design will need to be both efficient and interpretable. I will conclude by presenting a new deep reinforcement learning model for human action detection in videos that is efficient, interpretable and more accurate than prior art, opening up new avenues for practical human-in-the-loop exploration.
Biography: Olga Russakovsky recently completed her PhD in computer science at Stanford and is now a postdoctoral fellow at Carnegie Mellon University. Her research is in computer vision, closely integrated with machine learning and human-computer interaction. Her work was featured in the New York Times and MIT Technology Review. She served as a Senior Program Committee member for WACV'16, led the ImageNet Large Scale Visual Recognition Challenge effort for two years, and organized multiple workshops and tutorials on large-scale recognition at premier computer vision conferences ICCV'13, ECCV'14, CVPR'15, ICCV'15 and CVPR'16. In addition, she founded and directs the Stanford AI Laboratory's outreach camp SAILORS (featured in Wired and published in SIGCSE'16) designed to expose high school students in underrepresented populations to the field of AI.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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2016 John Laufer Lecture
Wed, Mar 23, 2016 @ 01:00 PM - 03:00 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Mory Gharib, Hans W. Liepmann Professor of Aeronautics and Professor of Bioinspired Engineering, Division of Engineering and Applied Science at the California Institute of Technology in Pasadena, CA
Talk Title: On the Generation of Toroidal Micro-Plasmas in the Flow Field of Impinging Water-Jets
Abstract: There is a renewed interest in atmospheric pressure plasma (APP), also known as atmospheric pressure corona, for its broad scientific and industrial applications. As a weakly ionized non-equilibrium plasma, APP has no defined shape or volume and, in general, is unstable and non-uniform. Therefore, it is desirable to have a source of stable and uniform APP with defined morphologies for scientific investigations that could take advantage of the highly collisional state of the plasma medium. Here, we report an approach to produce atmospheric pressure micro-plasmas in which the plasma cloud presents a stable, and topologically-connected and self-confined toroidal shape. We show that this unique toroidal APP morphology can be uniquely generated when a high-speed laminar micro-jet of de-ionized water impinges on a di-electric solid surface. This toroidal micro-plasma shows a unique and previously unreported plasma resonance mode characterized by a strong and discrete radio frequency emission.
Biography: Professor Mory Gharib is the Hans W. Liepmann Professor of Aeronautics and Professor of Bioinspired Engineering, and is also Vice Provost for Research at Caltech, where in 2014 he was made Director of the Linde Institute for Economics and Management Sciences. He has been a professor at the Graduate Aeronautical Labs at Caltech since 1993, and before that was Professor of Fluid Mechanics in the Department of Applied Mechanics and Engineering Sciences at the University of California, San Diego.
Professor Gharib is a member of the National Academy of Arts and Sciences, and of the National Academy of Engineering. He is a Charter Fellow of the National Academy of Inventors, and a Fellow of: the American Association for the Advancement of Science (AAAS), the American Physical Society (APS), the International Academy of Medical and Biological Engineering (IAMBE), the American Society of Mechanical Engineering (ASME), the American Institute for Medical and Biological Engineering (AIMBE), and the Institute of Physics (IP). He has more than 200 publications in refereed journals and 83 US patents.
More Information: photo3.jpg
Location: Ronald Tutor Campus Center (TCC) - Ballroom A
Audiences: Everyone Is Invited
Contact: Valerie Childress
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Mar 23, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Farzad Farnoud, Caltech
Talk Title: Stochastic and Information-theoretic Approaches to Analysis of Biological Data
Series: CommNetS
Abstract: The significant growth in the volume and variety of biological data over the past two decades has created plenty of opportunities for data analytics, with essential applications to biology and medicine. In this talk, I will present our work on aspects of analysis and fusion of biological data, leveraging tools from information theory, machine learning, and stochastic modeling. First, I will present an estimation framework for studying the rates of DNA tandem duplication and substitution mutations by analyzing DNA tandem repeat regions. These regions form about 3% of the human genome and are known to cause several diseases. The proposed method, obtained through a stochastic approximation framework, has smaller estimation error compared to previous work and enables the study of various factors affecting mutation rates through the study of a single genome. Second, I will describe HyDRA, a data fusion tool for gene prioritization, which is the task of computationally identifying genes that are most likely to cause a certain disease. HyDRA relies on novel distances between rankings and rank aggregation methods to combine data from various biological datasets. We show that it achieves better accuracy in identifying disease genes while being more scalable compared to the state-of-the-art methods.
Biography: Farzad Farnoud is a postdoctoral scholar at the California Institute of Technology. He received his MS degree in Electrical and Computer Engineering from the University of Toronto in 2008. From the University of Illinois at Urbana-Champaign, he received his MS degree in mathematics and his PhD in Electrical and Computer Engineering in 2012 and 2013, respectively. His research interests include the information-theoretic and probabilistic analysis of genomic evolutionary processes; rank aggregation and gene prioritization; and coding for flash memory and DNA storage. He is the recipient of the 2013 Robert T. Chien Memorial Award from the University of Illinois for demonstrating excellence in research in electrical engineering and the recipient of the 2014 IEEE Data Storage Best Student Paper Award.
Host: Dr. Salman Avestimehr
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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CS Colloquium: Linh Thi Xuan Phan (U. of Pennsylvania) - Timing Guarantees for Cyber-Physical Systems
Wed, Mar 23, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Linh Thi Xuan Phan, U. of Pennsylvania
Talk Title: Timing Guarantees for Cyber-Physical Systems
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Cyber-physical systems -- such as cars, pacemakers, and power plants -- need to interact with the physical world in a timely manner to ensure safety. It is important to have a way to analyze these systems and to prove that they can meet their timing requirements. However, modern cyber-physical systems are increasingly complex: they can involve thousands of tasks running on dozens of processors, many of which can have multiple cores or shared caches. Existing techniques for ensuring timing guarantees cannot handle this level of complexity. In this talk, I will present some of my recent work that can help to bridge this gap, such as overhead-aware compositional scheduling/analysis and multicore cache management. I will also discuss some potential applications, such as real-time cloud platforms and intrusion-resistant cyber-physical systems.
Biography: Linh Thi Xuan Phan is an Assistant Research Professor in the Department of Computer and Information Science at the University of Pennsylvania. Her interests include real-time systems, embedded systems, cyber-physical systems, and cloud computing. Her research develops theoretical foundations and practical tools for building complex systems with provable safety and timing guarantees. She is especially interested in techniques that integrate theory, systems, and application aspects. Recently, she has been working on methods for defending cyber-physical systems against malicious attacks, as well as on real-time cloud infrastructures for safety critical and mission-critical systems. Linh holds a Ph.D. degree in Computer Science from the National University of Singapore (NUS); she received the Graduate Research Excellence Award from NUS for her dissertation work.
Host: CS Department
More Info: https://bluejeans.com/333182321
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/333182321
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Efficient Redundancy Techniques to Reduce Delay in Cloud Systems
Thu, Mar 24, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Gauri Joshi, Massachusetts Institute of Technology
Talk Title: Efficient Redundancy Techniques to Reduce Delay in Cloud Systems
Abstract: Ensuring fast and seamless service to users is critical for today's cloud services. However, guaranteeing fast response can be challenging due to random service delays that are common in today's data centers. In this talk I explore the use the redundancy to combat such service variability. For example, replicating a computing task at multiple servers and then waiting for the earliest copy saves service time. But the redundant tasks can cost more computing resources and also delay subsequent tasks. I present a queuing-theoretic framework to answer fundamental questions such as:
1) How many replicas to launch?
2) Which queues to join?
3) When to issue and cancel the replicas?
This framework reveals surprising regimes where replication reduces both delay as well as resource cost. The task replication idea can also be generalized to analyze latency in content download from erasure coded storage. More broadly, this work lays the theoretical foundation for studying queues with redundancy, uncovering many interesting future directions in cloud infrastructure, crowdsourcing and beyond.
Biography: Gauri Joshi is a Ph.D candidate at MIT EECS where she completed an S.M. in 2012. Her research interests include probabilistic modeling, coding theory and statistical inference. Before coming to MIT, she completed a B.Tech and M. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Bombay in 2010. She has held summer internships at Google, Bell Labs and Qualcomm. Gauri's awards and honors include the Best Thesis Prize in Computer science at MIT (2012), Institute Gold Medal of IIT Bombay (2010), Claude Shannon Research Assistantship (2015-16), and Schlumberger Faculty for the Future fellowship (2011-2015).
Host: Viktor Prasanna
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Suzanne Wong
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CS Colloquium: Joseph Lim (MIT) - Toward Visual Understanding of the Physical World for Interaction
Thu, Mar 24, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Joseph Lim, MIT
Talk Title: Toward Visual Understanding of the Physical World for Interaction
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Recently, the computer vision community has made impressive progress on object recognition with deep learning approaches. However, for any visual system to interact with objects, it needs to understand much more than simply recognizing where the objects are. The goal of my research is to explore and solve physical understanding tasks for interaction -- finding an object's pose in 3D, interpreting its physical interactions, and understanding its various states and transformations. Unfortunately, obtaining extensive annotated data for such tasks is often intractable, yet required by recent popular learning techniques.
In this talk, I take a step away from expensive, manually labeled datasets. Instead, I develop learning algorithms that are supervised through physical constraints combined with structured priors. I will first talk about how to build learning algorithms, including a deep learning framework (e.g., convolutional neural networks), that can utilize geometric information from 3D CAD models in combination with real-world statistics from photographs. Then, I will show how to use differentiable physics simulators to learn object properties simply by watching videos.
Biography: Joseph Lim is a postdoctoral researcher at Stanford University. He received a PhD in Electrical Engineering and Computer Science at Massachusetts Institute of Technology, where he was advised by Professor Antonio Torralba. His research interests are in computer vision and machine learning. He is particularly interested in deep learning, structure learning, and multi-domain data. Joseph graduated with BA in Computer Science from UC Berkeley, where he worked under Professor Jitendra Malik.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AI Seminar
Thu, Mar 24, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Johan Bollen, Univeristy of Indiana
Talk Title: Social factors in the quantification of online happiness
Abstract: More than 1/7th of the worlds population is actively using social media to establish and maintain social relations across linguistics, geographic, and economic boundaries. The introduction of social media has however had contradictory effects. Whereas as a social species we require social relations for our well-being, recent results indicate that widespread social media use leads to increased feelings of dissatisfaction and reduced happiness. The key to this paradox may lie in the unequal distribution of social relations in social networks and their interaction with collective and individual subjective well-being. In this talk I will highlight two results of our investigations of how subjective well-being interacts with, and is shaped by, the structural properties of large-scale social networks. Our research provides a framework for understanding how online social networking may have contradictory effects on collective happiness and well-being, and how to mitigate these effects
Biography: Johan Bollen is associate professor at the Indiana University School of Informatics and Computing. He was formerly a staff scientist at the Los Alamos National Laboratory and an Assistant Professor at the Department of Computer Science of Old Dominion University. He obtained his PhD in Experimental Psychology from the Vrije Universiteit Brussel (VUB) in 2001. He has published more than 75 articles on computational social science, social media analytics, informetrics, and digital libraries. His research has been funded by the NSF, IARPA, and Andrew W. Mellon Foundation. Johan lives inBloomington, Indiana with his wife and daughter. In his free time he enjoys P90x and DJing in the local Bloomington clubs as DJ Angst.
Host: Emilio Ferrara
Location: 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
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The Business of Oil and Gas
Thu, Mar 24, 2016 @ 01:00 PM - 02:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Mr. Adel Heiba,
Talk Title: Real Options in Shale Plays: Opportunities for Data Science
Series: USC Energy Institute Seminar Series
Host: USC Energy Institute
Location: Ronald Tutor Hall of Engineering (RTH) - 324
Audiences: Everyone Is Invited
Contact: Juli Legat
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Astani Civil and Environmental Engineering Seminar
Thu, Mar 24, 2016 @ 02:00 PM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Roland Cusick, University of Illinois at Urbana-Champaign
Talk Title: Elucidating the potential of capacitive energy storage technologies for brackish water desalination
Abstract: See attachment
Host: Dr. Adam Smith
More Information: Cusick Announcement.pdf
Location: Thomas & Dorothy Leavey Library (LVL) - 17
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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CS Colloquium: Jason Polakis (Columbia U.) -Protecting Users in the Age of the Social Web
Thu, Mar 24, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jason Polakis, Columbia University
Talk Title: Protecting Users in the Age of the Social Web
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
In this talk I will focus on my research efforts to better understand and protect against such loss. I will start with a focused review on the importance of online privacy, and highlight the privacy risks of location proximity, which has been adopted by major web services and mobile apps. This work demonstrated novel threats that can neutralize existing countermeasures used by the industry and pinpoint a user's location with high accuracy within seconds. To protect users, I developed a practical defense in the form of privacy-preserving proximity that obfuscates the user's location, which has been adopted by Facebook and Foursquare. I will demonstrate how user privacy also affects security mechanisms, and present my analysis of the threat surface of Facebook's social authentication system. I will then present a novel social authentication system that is robust against advanced targeted attacks and prevents adversaries from compromising user accounts, and conclude by sharing my thoughts for future directions.
This lecture will be available to stream HERE.
Biography: Jason Polakis is a postdoctoral research scientist at Columbia University. He earned his PhD in 2014 from the Computer Science Department of the University of Crete, Greece, where he was supported by the Foundation of Research and Technology Hellas (FORTH). He is broadly interested in identifying the security and privacy limitations of Internet technologies, designing robust defenses and privacy-preserving techniques, and enhancing our understanding of the online ecosystem and its threats. His research has revealed significant flaws in popular services, and major vendors such as Google, Facebook and Foursquare have deployed his proposed defenses. His work has been published in top tier security conferences (Security and Privacy, CCS, and NDSS) as well as other top tier computer science conferences (WWW).
Host: CS Department
More Info: https://bluejeans.com/313531059
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/313531059
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AI Seminar-The Structure of Sequences Mining and Interpreting Networks from Event Log Data
Fri, Mar 25, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Brian Keegan, Harvard University
Talk Title: The Structure of Sequences Mining and Interpreting Networks from Event Log Data
Series: Artificial Intelligence Seminar
Abstract: Network science provides a rich set of theories and methods to understand the structure and dynamics of complex social, information, and biological systems. These approaches traditionally demand data with explicitly declared dyadic relationships or interactions such as friendship or affiliation. However, socio-technical systems like Wikipedia, Github, or Twitter often encode latent relationships within event logs and other databases. Temporal adjacencies in these event logs reveal sequences of actions that have complex and non-random properties that illuminate hidden structures within peer production systems. Using several case studies, I describe how complex networks can be extracted from event logs to understand the behavior of both users and artifacts within these systems. These networks encode a variety of rich structural and dynamic data distinct from traditional network approaches and illustrate user social roles within distributed collaboration as well as context and shifting interests of users based on their contributions. This approach has rich implications for mixed-methods research as it allows researchers to collapse large-scale event log data into more parsimonious network representations that can motivate qualitative analysis, visualization, and statistical modeling of complex behavior in socio-technical systems.
Biography: Brian Keegan is a research associate and data scientist for the Harvard Business School Hbx online learning platform. He received his PhD from the Northwestern University School of Communication in 2012 and was a post-doctoral research fellow in network and computational social science at Northeastern University until 2014. His research analyzes the structure and dynamics of online knowledge collaborations such as Wikipedia, Twitter, and online education under high-tempo and bursty conditions.
Host: Emilio Ferrara
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=0b39bdb4046d4835af24d94a23ddf6061dLocation: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=0b39bdb4046d4835af24d94a23ddf6061d
Audiences: Everyone Is Invited
Contact: Peter Zamar
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Imaging Seminar - Junjie Yao, Friday, March 25th at 2:00pm in EEB 132
Fri, Mar 25, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Junjie Yao, Washington University in St. Louis, Missouri
Talk Title: Photoacoustic Imaging beyond Traditional Limits
Abstract: By physically combining electromagnetic and ultrasonic waves, photoacoustic imaging (PAI) has proven powerful for multi-scale anatomical, functional, and molecular imaging. In PAI, a short-pulsed laser beam illuminates the biological tissue to generate a small but rapid temperature rise, which leads to emission of ultrasonic waves due to thermoelastic expansion. The high-frequency ultrasonic waves are detected outside the tissue by an ultrasonic transducer to form an image that maps the original optical energy deposition in the tissue. PAI seamlessly combines the rich optical absorption contrast of biological tissue with the high optically- or acoustically-determined spatial resolutions.
My talk will focus on three recent advances of PAI on exciting new fronts. First, PAI has broken the optical-diffraction limit and achieved super-resolution (~80 nm) imaging by using non-linear photobleaching or photo-switching dynamics, extending its applications into sub-cellular nano-dimensions. Second, by using a novel pulse-width-based single-wavelength method, ultra-fast photoacoustic microscopy has achieved a 1D oxygenation imaging rate of 100 kHz, allowing label-free imaging of mouse brain activity with high spatial-temporal resolution. Third, taking advantage of the newly developed near-infrared non-fluorescent phytochrome BphP1, which can be reversibly switched on and off, PAI has achieved more than 200 times enhancement in detection sensitivity in reporter gene imaging, allowing early-stage cancer imaging at ~10 mm in tissue with a detection sensitivity of ~20 cancer cells.
Biography: Dr. Junjie Yao received his B.E. and M.E. degrees in Biomedical Engineering from Tsinghua University, Beijing, in 2006 and 2008, respectively. He received his Ph.D. degree in Biomedical Engineering at Washington University, St. Louis (WUSTL), in 2013. He is currently a postdoctoral research associate at WUSTL.
Dr. Junjie Yao's research interest is in photoacoustic imaging technologies in life sciences, especially in functional brain imaging and early cancer detection. Dr. Yao has published more than 60 articles in peer-reviewed journals such as Nature Methods, Nature Medicine, PNAS, and PRL. These publications have received more than 1650 world-wide citations in the last five years. He (co-)invented photoacoustic Doppler-bandwidth flowmetry, photoacoustic oxygen metabolic microscopy, photo-imprint super-resolution photoacoustic microscopy, and reversibly-switchable photoacoustic tomography. Dr. Yao's future research will center on developing novel photoacoustic technologies and translating the laboratory imaging advances into diagnostic and therapeutic applications for a broad range of diseases, especially brain disorders and cancers.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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The Underpinning of the Industrial Internet of Things
Fri, Mar 25, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jan Van Bruaene, VP of Engineering Real-Time Innovations
Talk Title: The Underpinning of the Industrial Internet of Things
Abstract: In this presentation, you will learn about the industrial internet of things and how it differs from the consumer internet of things. We'll also cover why building a connectivity platform for the industrial internet of thing is hard. We'll dig deeper into the underlying technology: Data Distribution Service (DDS). We'll cover data centricity, different communication patterns, and quality of service.
Finally, you will also learn about research and career opportunities at RTI.
Biography: Jan joined RTI in 2006 and has over 19 years of experience in technical and customer-facing leadership roles at companies such as Sun Microsystems and VLSI Technology. He has led professional services, support, and engineering organizations and has experience in middleware, and infrastructure software, operating system s and network chip development.
Jan came to RTI as a senior application engineer and was responsible for developing a new support organization which achieved a record-setting 98 percent customer satisfaction rate. Jan led a team of application services engineers delivering system design and custom implementations using RTI Connext technology and middleware. For the past four years, Jan has managed RTI's R&D organization.
Jan graduated with a MS equivalent degree in Electronics, Digital Communications (Summa Cum Laude) from KIHK in Geel, Belgium.
Host: Bhaskar Krishnamachari
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Shane Goodoff
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NL Seminar-Capturing More Linguistic Structure with Graph-Structured Parsing
Fri, Mar 25, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Jonathan Kummerfeld, Univ. of Berkeley
Talk Title: Capturing More Linguistic Structure with Graph-Structured Parsing
Series: Natural Language Seminar
Abstract: The correct interpretation of any sentence is obscured by a vast array of alternatives. Previous work on disambiguating meaning has focused on representations of syntax using tree structures. Simplifying syntax in this way often means leaving out long-distance relations between words, providing less information to downstream tasks such as dialog and question answering. We propose a new algorithm that is able to efficiently search over graph structures, fully capturing argument structures as a directed acyclic graph. Our dynamic program uniquely decomposes structures, and is sound and complete with respect to the class of one-endpoint crossing graphs.
Biography: Jonathan is a Ph.D. candidate at UC Berkeley working on natural language processing with Dan Klein. His research focuses on new algorithms for interpreting text and analyzing system behavior. In particular, he has built search-based error analysis tools for syntactic parsing and coreference resolution, and a graph-based syntactic parser.
Host: Xing Shi 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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CS Colloquium: David Fouhey (CMU) -Towards A Physical and Human-Centric Understanding of Images
Mon, Mar 28, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Fouhey, CMU
Talk Title: Towards A Physical and Human-Centric Understanding of Images
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
One primary goal of AI from its very beginning has been to develop systems that can understand an image in a meaningful way. While we have seen tremendous progress in recent years on naming-style tasks like image classification or object detection, a meaningful understanding requires going beyond this paradigm. Scenes are inherently 3D, so our understanding must also capture the underlying 3D and physical properties. Additionally, our understanding must be human-centric since any man-made scene has been built with humans in mind. Despite the importance of obtaining a 3D and human-centric understanding, we are only beginning to scratch the surface on both fronts: many fundamental questions, in terms of how to both frame and solve the problem, remain unanswered.
In this talk, I will discuss my efforts towards building a physical and human-centric understanding of images. I will present work addressing the questions: (1) what 3D properties should we model and predict from images, and do we actually need explicit 3D training data to do this? (2) how can we reconcile data-driven learning techniques with the physical constraints that exist in the world? and (3) how can understanding humans improve traditional 3D and object recognition tasks?
Biography: David Fouhey is a Ph.D. student at the Robotics Institute of Carnegie Mellon University, where he is advised by Abhinav Gupta and Martial Hebert. His research interests include computer vision and machine learning with a particular focus on scene understanding. David's work has been supported by both NSF and NDSEG fellowships. He has spent time at Microsoft Research and University of Oxford's Visual Geometry Group.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Mon, Mar 28, 2016 @ 12:30 PM - 01:49 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Huizhong Tao, PhD, Associate Professor of Cell & Neurobiology Zilkha Neurogenetic Institute
Talk Title: Dissecting Neural Circuits for Visual Processing and
Abstract: The long-term goal of my lab is to understand the neural circuits underlying cortical processing of sensory information and sensory evoked behaviors. We have combined a set of cutting-edge techniques, including in vivo whole-cell patch-clamp recording, two-photon imaging guided recording and optogenetics, to dissect local and long-range synaptic circuits for specific visual cortical processing functions. I will present our recent data on the circuits underlying the auditory modulation of orientation selectivity of visual cortical neurons, and those underlying the visual cortical modulation of an innate visual behavior.
Host: K. Kirk Shung, PhD
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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EE 598 Cyber-Physical Systems Seminar Series
Mon, Mar 28, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Krishnendu Chakrabarty, Professor, Duke University
Talk Title: Digital Microfluidic Biochips: From Manipulating Droplets to A Cyberphysical System for Quantitative Gene-Expression Analysis
Abstract: Advances in microfluidics have led to the emergence of biochips for automating laboratory procedures in molecular biology. These devices enable the precise control of nanoliter volumes of biochemical samples and reagents. As a result, non-traditional biomedical applications and markets (e.g., high-throughout DNA sequencing, portable and point-of-care clinical diagnostics, protein crystallization for drug discovery), and fundamentally new uses are opening up for ICs and systems. This lecture will first introduce electrowetting-based digital microfludic biochips and provide an overview of market drivers such as immunoassays and DNA sequencing. The audience will next learn about design automation and reconfiguration aspects of microfluidic biochips. Synthesis tools will be described to map assay protocols from the lab bench to a droplet-based microfluidic platform and generate an optimized schedule of bioassay operations, the binding of assay operations to functional units, and the layout and droplet-flow paths for the biochip. The role of the digital microfluidic platform as a "programmable and reconfigurable processor" for biochemical applications will be highlighted. The speaker will describe dynamic adaptation of bioassays through cyberphysical system integration and sensor-driven on-chip error recovery.
Finally, the speaker will highlight recent advances in utilizing cyberphysical integration for quantitative gene-expression analysis. This framework is based on a real-time resource-allocation algorithm that responds promptly to decisions about the protocol flow received from a firmware layer. Results will be presented on how this adaptive framework efficiently utilizes on-chip resources to reduce time-to-result without sacrificing the chip's lifetime.
Biography: Krishnendu Chakrabarty received the B. Tech. degree from the Indian Institute of Technology, Kharagpur, in 1990, and the M.S.E. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 1992 and 1995, respectively. He is now the William H. Younger Distinguished Professor of Engineering in the Department of Electrical and Computer Engineering and Professor of Computer Science at Duke University. He also serves as Director of Graduate Studies for Electrical and Computer Engineering. Prof. Chakrabarty is a recipient of the National Science Foundation Early Faculty (CAREER) award, the Office of Naval Research Young Investigator award, the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper award (2015), and 11 best paper awards at major IEEE conferences. He is also a recipient of the IEEE Computer Society Technical Achievement Award (2015) and the Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur (2014). He is a Research Ambassador of the University of Bremen, Germany. He has been a Visiting Professor at University of Tokyo, Japan (2013), a Chair Professor at Tsinghua University, China (2009-2014), and a Visiting Chair Professor at National Cheng Kung University, Taiwan (2012-2013).
Prof. Chakrabarty's current research projects include: testing and design-for-testability of integrated circuits; digital microfluidics, biochips, and cyberphysical systems; optimization of enterprise systems and smart manufacturing. He is a Fellow of ACM, a Fellow of IEEE, and a Golden Core Member of the IEEE Computer Society. He holds seven US patents, with several patents pending. He was a 2009 Invitational Fellow of the Japan Society for the Promotion of Science (JSPS). He is a recipient of the 2008 Duke University Graduate School Dean's Award for excellence in mentoring, and the 2010 Capers and Marion McDonald Award for Excellence in Mentoring and Advising, Pratt School of Engineering, Duke University. He served as a Distinguished Visitor of the IEEE Computer Society during 2005-2007 and 2010-2012, and as a Distinguished Lecturer of the IEEE Circuits and Systems Society during 2006-2007 and 2012-2013. Currently he serves as an ACM Distinguished Speaker.
Prof. Chakrabarty served as Editor-in-Chief of IEEE Design & Test of Computers (2010-2012) and ACM Journal on Emerging Technologies in Computing Systems (2010-2015). Currently he serves as the Editor-in-Chief of IEEE Transactions on VLSI Systems. He is also an Associate Editor of IEEE Transactions on Computers, IEEE Transactions on Biomedical Circuits and Systems, IEEE Transactions on Multiscale Computing Systems, and ACM Transactions on Design Automation of Electronic Systems.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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CS Colloquium: Tuo Zhao (Johns Hopkins University) - Compute Faster and Learn Better: Machine Learning via Nonconvex Model-based Optimization
Mon, Mar 28, 2016 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tuo Zhao , Johns Hopkins University
Talk Title: Compute Faster and Learn Better: Machine Learning via Nonconvex Model-based Optimization
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Nonconvex optimization naturally arises in many machine learning problems (e.g. sparse learning, matrix factorization, and tensor decomposition). Machine learning researchers exploit various nonconvex formulations to gain modeling flexibility, estimation robustness, adaptivity, and computational scalability. Although classical computational complexity theory has shown that solving nonconvex optimization is generally NP-hard in the worst case, practitioners have proposed numerous heuristic optimization algorithms, which achieve outstanding empirical performance in real-world applications.
To bridge this gap between practice and theory, we propose a new generation of model-based optimization algorithms and theory, which incorporate the statistical thinking into modern optimization. Particularly, when designing practical computational algorithms, we take the underlying statistical models into consideration (e.g. sparsity, low rankness). Our novel algorithms exploit hidden geometric structures behind many nonconvex optimization problems, and can obtain global optima with the desired statistics properties in polynomial time with high probability.
Biography: Tuo Zhao is a PhD student in Department of Computer Science at Johns Hopkins University (http://www.cs.jhu.edu/~tour). His research focuses on high dimensional parametric and semiparametric learning, large-scale optimization, and applications to computational genomics and neuroimaging. He was the core member of the JHU team winning the INDI ADHD 200 global competition on fMRI imaging-based diagnosis classification in 2011. He received Siebel scholarship in 2014 and Baidu's research fellowship in 2015
Host: CS Department
More Info: https://bluejeans.com/741584974
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/741584974
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CS Colloquium: Bogdan Vasilescu (UC Davis) - Lessons in Social Coding: Software Analytics in the Age of GitHub
Mon, Mar 28, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Bogdan Vasilescu, UC Davis
Talk Title: Lessons in Social Coding: Software Analytics in the Age of GitHub
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Social media has forever changed the ways in which we communicate and work, programming included. This "social coding" movement (code is meant to be shared!) made popular by GitHub has come to represent a paradigm shift in software development, especially in the open-source world. For example, the "pull request" model has made it easier than ever before for newcomers to submit contributions to a project. As a result, teams are becoming increasingly larger, more distributed, and more diverse. At the same time, the incentives for contributing have evolved. For example, one's social coding activity is starting to replace one's resume, and directly influence their hourly wage. Today, GitHub reports 12 million users and over 30 million repositories, with popular projects having communities the size of small cities. These numbers are unprecedented in open-source!
This new, social way of developing software opens a great many questions. How do people choose which projects to contribute to? Does prior technical experience matter, or do people learn on the job? Is it efficient to work on many projects in parallel? How does diversity in software teams affect productivity and code quality? What are the main factors that slow down pull request reviews? How does automation help developers do more with less? Does continuous integration help to ensure higher quality code? I will try to answer some of these questions in this talk.
Biography: Bogdan Vasilescu is currently a postdoctoral researcher at University of California, Davis (USA), where he is a member of the Davis Eclectic Computational Analytics Lab (DECAL). He received his PhD and MSc in Computer Science at Eindhoven University of Technology, both with cum laude distinction. His PhD dissertation won the best dissertation award from the Dutch Institute for Programming Research and Algorithmics in 2015. Follow him on Twitter @b_vasilescu
Host: CS Department
More Info: https://bluejeans.com/958446198
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/958446198
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CS Colloquium: David Levin (Disney Research Boston) - Physically-Based Simulation for Animation and Fabrication
Tue, Mar 29, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Levin, Disney Research Boston
Talk Title: Physically-Based Simulation for Animation and Fabrication
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Physics-based simulation has become a transformative tool for solving problems in computer animation and computational fabrication. In this talk I will discuss how leveraging unique abstractions, new discretizations and data-driven techniques can allow us to animate and fabricate a wide-range of phenomena with improved performance, robustness and accuracy. I'll show how layered discretizations can enable photoshop like editing of physically-based animations, how Eulerian methods can be used to robustly simulate deforming objects in close contact, how 3D printing and simulation can produce new musical instruments, and more. I'll conclude by discussing the important challenges facing physics-based animation and fabrication now and in the future.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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USC Stem Cell Seminar: Ali H. Brivanlou, The Rockefeller University
Tue, Mar 29, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Ali H. Brivanlou, The Rockefeller University
Talk Title: Self-understanding of self-organization
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: The earliest aspects of human embryogenesis remain a complete mystery. Using human embryonic stem cells (hESCs), we have recently developed an in vitro platform that provides, for the first time, a window into early human development. We use this platform to study the physical and molecular mechanisms underlying human gastrulation. We integrate this information to develop a quantitative model of human fate determination.
Host: Neil Segil
More Info: https://calendar.usc.edu/event/speaker_ali_h_brivanlou_the_rockefeller_university?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.Vtj6NynFl04
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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RECRUITING SEMINAR
Tue, Mar 29, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mayank Kejriwal, University of Texas at Austin
Talk Title: Populating a Linked Data Entity Name System
Series: Recruitng Seminar
Abstract: Resource Description Framework (RDF) is a graph-based data model used to publish data as a Web of Linked Data. RDF is an emergent foundation for large-scale data integration. An Entity Name System (ENS) is a thesaurus for entities, and is a crucial component in a data integration architecture. Populating a Linked Data ENS is equivalent to solving an Artificial Intelligence problem called instance matching, which concerns identifying pairs of entities referring to the same underlying entity.
This talk describes a system that automatically populates an ENS in a domain-independent fashion. Automation is addressed through inexpensive but well-performing heuristics that are used to generate a training set, which is employed by other machine learning algorithms in the pipeline. Data-driven alignment algorithms are adapted to deal with structural heterogeneity in RDF graphs. The full system is scaled by implementing it on cloud infrastructure using MapReduce algorithms.
Biography: Mayank Kejriwal is finishing up his Ph.D in Computer Science at the University of Texas at Austin under the supervision of Daniel P. Miranker. His research focuses on instance-level information integration in the Semantic Web, and has been published in the International Conference on Data Mining, the Journal of Web Semantics, the International Semantic Web Conference, and the Extended Semantic Web Conference, where he won a best paper award at the 4th annual Know@LOD workshop. Prior to joining UT Austin in 2012, he obtained a dual undergraduate degree in Computer Engineering and Engineering Physics from the University of Illinois at Urbana-Champaign.
Host: Craig Knoblock
Webcast: Webcast:http://webcasterms1.isi.edu/mediasite/Viewer/?peid=cd1440ac1ea54794b12eab29e42d60ee1dLocation: Information Science Institute (ISI) - 11th floor Large CR
WebCast Link: Webcast:http://webcasterms1.isi.edu/mediasite/Viewer/?peid=cd1440ac1ea54794b12eab29e42d60ee1d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
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Cisco: Stream Processing in Practice
Tue, Mar 29, 2016 @ 11:00 AM - 12:20 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Dr. Debojyoti Dutta, Cisco: Office of the CTO
Talk Title: Cisco: Stream Processing in Practice
Abstract: Networking is an example of a streaming paradigm in systems. We revisit the basics of networking and networked processing in particular and show how the same basic principles can be used to design real-world scalable streaming systems. We will touch upon streaming computations, frameworks, and event processing, via real world examples. We will cover what it takes to build streaming engines (e.g. a network switch or a data platform like http://ciscozeus.io). In addition we will also cover applied algorithms that work well for such streaming models.
Biography: Dr. Dutta is actively developing streaming analytics solutions for operational insight and actions including optimizing infrastructure for I/O(/data) intensive applications on Openstack, and other scalable platforms for cloud computing and software defined networks. His work has spanned social collaboration techniques, software defined networks, applied algorithms for data mining, IoT platforms, and Cloud Ops. Dr. Dutta is a USC alum and graduated with his PhD from USC in 2004.
Host: Alefiya Hussain
Location: Mark Taper Hall Of Humanities (THH) - 210
Audiences: Everyone Is Invited
Contact: Alefiya Hussain
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EE-EP Seminar - Renjie Zhou, Tuesday, March 29th at 2:00pm in EEB 132
Tue, Mar 29, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Renjie Zhou, George R. Harrison Spectroscopy Laboratory at MIT
Talk Title: Quantitative Phase Microscopy: a label-free platform for material metrology and biological imaging
Abstract: Advances in imaging sensors and computer chips have enabled us to record holograms on cameras and reconstruct objects information with high fidelity and fast speed. The marriage of digital holography and optical microscopy gave birth to quantitative phase imaging (QPI). QPI precisely maps the amplitude and phase information associated with the electromagnetic field scattered by an object. Recent efforts have pushed QPI instruments to achieve sensitivity better than 10-3, corresponding to less than 1 nm surface height changes, or conversely 10-4 refractive index variations in transparent biological structures. Importantly, QPI is a label-free method, without using fluorescence markers, which has opened many noninvasive imaging applications.
This talk will focus on the instrumentation and image formation of novel QPI systems and highlight their applications in two important domains, namely material metrology and biological imaging. First, I will outline the QPI potential in material characterization and wafer defect inspection. In particular, I will show our wafer metrology instrument development and its capability for densely patterned semiconductor wafer defect inspection, detecting deep sub-wavelength patterning defects in 22nm and 9nm node silicon wafers. After that, I will move my focal point to the development of QPI-based biological imaging techniques. Especially, I will talk about solving the inverse scattering problem for determining the structure of cells in 3D, which led to the invention of white-light diffraction tomography (WDT). WDT is compatible with most exiting phase contrast microscopes, thus, it can potentially complement fluorescence imaging by providing additional biophysical markers. At the end, I will discuss some potential research areas along the QPI direction, including neuron activity imaging, stem cell identification, and cell mechanics characterization.
Biography: Renjie Zhou is a postdoctoral associate at George R. Harrison Spectroscopy Laboratory at MIT, where his research centers on developing ultra-sensitive interferometric microscopy systems and high throughput 3D imaging methods for biomedical applications. Dr. Zhou received PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC) in 2014. His dissertation focused on developing wafer defect inspection instruments and solving 3D inverse scattering problems for cell imaging. Dr. Zhou has co-authored over 40 peer-reviewed journal and conference papers and filed 4 US patent applications. He has received a number of research awards including the Arnold Beckman Fellowship from the Beckman Foundation, Scholarship in Optics & Photonics and Newport Spectra - Physics Research Excellence Travel Grant from SPIE; Jean Bennett Memorial Student Travel Grant finalist from OSA; P. D. Coleman Outstanding Research Award, Yuen T. Lo Outstanding Graduate Research Award, and Sundaram Seshu International Student Fellowship from UIUC. In addition, Dr. Zhou's research work was featured in Nature, NSF, OSA, and SPIE news.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Nisarg Shah (CMU) - Optimal Social Decision Making
Tue, Mar 29, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nisarg Shah, Carnegie Mellon University
Talk Title: Optimal Social Decision Making
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
How can computers help ordinary people make collective decisions about real-life dilemmas, like which restaurant to go to with friends, or even how to divide an inheritance? I will present an optimization-driven approach that draws on ideas from AI, theoretical computer science, and economic theory, and illustrate it through my research in computational social choice and computational fair division. In both areas, I will make a special effort to demonstrate how fundamental theoretical questions underlie the design and implementation of deployed services that are already used by tens of thousands of people (spliddit.org), as well as upcoming services (robovote.org).
Biography: Nisarg Shah is a Ph.D. candidate in the Computer Science Department at Carnegie Mellon University, advised by Ariel Procaccia. His broad research agenda in algorithmic economics includes topics such as computational social choice, fair division, game theory (both cooperative and noncooperative), and prediction markets. He focuses on designing theoretically grounded methods that have practical implications. Shah is the winner of the 2013-2014 Hima and Jive Graduate Fellowship and the 2014-2015 Facebook Fellowship.
Host: CS Department
More Info: https://bluejeans.com/246853239
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/246853239
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Continuous Mobile Vision Systems for Efficiency and Privacy
Wed, Mar 30, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Robert LiKamWa, Rice University
Talk Title: Continuous Mobile Vision Systems for Efficiency and Privacy
Abstract: The future of computing is in allowing our devices to see what we see. I envision wearable systems that continuously interpret vision data for real-time analytics. Today's system software and imaging hardware are ill-suited for this goal of "continuous mobile vision." Current systems, highly optimized for photography, fail to achieve sufficient energy efficiency or privacy preservation. This talk provides a rethinking of the vision system stack that includes application frameworks, operating system and sensor hardware to improve efficiency by two orders of magnitude. This cross layer rethinking contributes: (1) a split-process application framework that eliminates redundancy in data movement and processing across multiple concurrent applications, (2) operating system optimizations for energy proportional image capture, and (3) a mixed-signal image sensor architecture that processes data in the analog domain to eliminate the efficiency bottleneck of analog-digital conversion. The talk will briefly share future plans to further continuous mobile vision by exploiting the hardware/software boundary for improved energy efficiency and effective privacy preservation, opening the door to integrate our devices with our real world-environments and ultimately, our own lives.
Biography: Robert LiKamWa is a final-year PhD Student at Rice University. As a Mobile Systems researcher, he operates at the intersection of Operating Systems and Computer Architecture. His dissertation research focuses on system support for continuous mobile vision. He has interned and collaborated with Microsoft Research and Samsung Mobile Processor Innovation Lab on various projects related to vision systems. LiKamWa is supported by a Texas Instruments Graduate Fellowship, and received best paper awards from ACM MobiSys 2013 and PhoneSense 2011.
Host: Konstantinos Psounis
Location: 248
Audiences: Everyone Is Invited
Contact: Suzanne Wong
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AI SEMINAR
Wed, Mar 30, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Dr. Oren Etzioni, Chief Executive Officer of the Allen Institute for AI
Talk Title: Myths and Facts about the Future of AI
Series: AI Seminar
Abstract: AI recent success has led to excess. We see headlines like : Artificial Intelligence is Coming, and it Could Wipe Us Out if We are Not Careful, Professor Warns. While some successes are real (for example, AlphaGos amazing Go playing), many challenges remain. My talk will put AlphaGo (and related learning systems) in context, and attempt to debunk some of the popular myths about AI. I will conclude by talking about AI2s mission of AI for the Common Good-”as illustrated by our AI-based scientific search engine: Semantic Scholar (www.semanticscholar.org).
Biography: Dr. Oren Etzioni is Chief Executive Officer of the Allen Institute for Artificial Intelligence. He has been a Professor at the University of Washington's Computer Science department since 1991, receiving several awards including Seattle's Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He was also the founder or co-founder of several companies including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013), and the author of over 100 technical papers that have garnered over 25,000 citations. The goal of Oren's research is to solve fundamental problems in AI, particularly the automatic learning of knowledge from text. Oren received his Ph.D. from Carnegie Mellon University in 1991, and his B.A. from Harvard in 1986.
Host: Craig Knoblock
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2d841545cb9d4a61bfc960a713d84e821dLocation: Information Science Institute (ISI) - 1135 - 11th fl Large CR
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2d841545cb9d4a61bfc960a713d84e821d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
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Aerospace and Mechanical Engineering Seminar Series
Wed, Mar 30, 2016 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Ran Gabai, Dynamics and Mechatronics Laboratory, Faculty of Mechanical Engineering, Technion IIT, Haifa, Israel
Talk Title: Acoustic Levitation and Propulsion Based on Traveling Waves Control
Series: Aerospace and Mechanical Engineering Seminar Series
Abstract: Acoustic levitation is generated by inducing ultrasonic vibrations to a surface above which a levitated object is held by elevated pressure. A thin film of gas separating the vibrating surface and the levitated body exhibits both rapid fluctuations and a rise in the average pressure. An application being researched currently involves the handling of silicon wafers in clean rooms with no mechanical contact thus eliminating a significant contamination sources. The elevated pressure is capable of levitating objects weighting several kg by a vibrating surface 100mm in diameter. By creating a traveling pressure wave, it is possible to add propelling forces to the levitating component thus creating a contactless transportation system. By sensing the position of the levitated object one can control, in a closed loop feedback scheme, the levitation height and the planar position and orientation.
The dynamics of the mechanical structure has to be carefully tailored to enhance the electromechanical efficiency leading to sufficient amplitudes of the ultrasonic vibrations to provide appropriate levels of acoustic levitation and traveling waves. Ultrasonic structural traveling waves are generated by exciting two modes of vibrations that are tuned, in real time, to generate the required traveling wave direction and amplitude. Small structural uncertainties spoil the symmetry of the structure and detune the conditions for traveling waves. Therefore, an optimization process is introduced to experimentally map the exact traveling wave excitation conditions.
This work presents the analytical background, numerical simulations, and several experimental set-ups validating the applicability of acoustic levitation and propulsion.
Biography: Ran Gabai is a post-doctoral researcher at the Dynamics and Mechatronics Laboratory at the Technion working with Prof. Izhak Bucher. He earned his PhD (2008) at the Faculty of Mechanical engineering at the Technion as well as his M.S. (2003) and B.S. (2000). His research focuses on dynamic and vibrations, mechatronics, signal processing, control, and embedding digital brains in dynamical systems. Dr. Gabai is the co-founder and CTO of a start-up company developing a Coriolis-based mass flow meter.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Valerie Childress
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Seminar-Algorithms for detecting atypical language use in autism spectrum disorders
Thu, Mar 31, 2016 @ 10:00 AM - 11:20 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jan van Santen, Center for Spoken Language Understanding, Institute on Development and Disability, Oregon Health & Science University
Talk Title: Algorithms for detecting atypical language use in autism spectrum disorders
Abstract: The DSM-5 lists repetitiveness and impaired reciprocal social interaction as core symptoms of autism spectrum disorder (ASD), but does not list language impairment. Yet, language use is often atypical in ASD, being a natural modality for core symptoms to manifest. Standard language measures are not optimal for capturing these characteristics because they are too structured: Analysis of natural language samples is needed. However, such analysis is time consuming and inexact when conducted manually. Computational methods are needed.
We developed and applied algorithms to transcripts of Autism Diagnostic Observation Schedule (ADOS) sessions with children ages 4-8 with high-functioning ASD, Specific Language Impairment (SLI), or Typical Development (TD). The ASD group was divided into children with SLI (ALI) and without (ALN). Results showed ASD-specific atypicalities in verbatim and topical repetitiveness, discourse marker use, type of disfluencies, and other features of language use. These results attest to the feasibility of computing ASD-specific characteristics from natural language samples, tapping into multiple aspects of core ASD symptoms. Their usefulness is demonstrated by the intricate pattern of differences and similarities between the ASD and SLI groups and the ALI and ALN groups.
Biography: Jan van Santen obtained his PhD in Mathematical Psychology at the University of Michigan in 1979. He worked initially on visual perception and image processing at New York University and Bell Labs, and then switched to speech technology in 1985. He developed the prosody generation components of the Bell Labs text-to-speech system. In 2000 he became the Director of the Center for Spoken Language Understanding, now part of the Oregon Health & Science University. Here, he became one of the pioneers of a growing new field: the application of Natural Language Processing algorithms to neurological and neurodevelopmental disorders for diagnostics, remediation, and assistive communication, with special emphasis on autism spectrum disorders. In his spare time, he runs a startup, BioSpeech, that works on algorithms for processing biological sounds, including not only speech but also snoring and rodent calls.
He has written over 100 peer-reviewed papers, was an editor of Speech Communication and of the Journal of Mathematical Psychology, was the editor of a book, Progress in Speech Synthesis, and has seven patents.
Host: Shrikanth Narayanan & Daniel Bone
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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Cyber-Physical System Design Using Contracts
Thu, Mar 31, 2016 @ 10:00 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Pierluigi Nuzzo, University of California, Berkeley
Talk Title: Cyber-Physical System Design Using Contracts
Abstract: The realization of complex cyber-physical systems is creating design and verification challenges that will soon become insurmountable with today's engineering practices. While model-based design tools are already facilitating several design tasks, harnessing the complexity of the Internet-of-Things scenario is only deemed possible within a unifying methodology. This methodology should help interconnect different tools, possibly operating on different system representations, to enable scalable design space exploration and early detection of requirement inconsistencies.
In this talk, I show how a contract-based approach provides a formal foundation for a compositional and hierarchical methodology for cyber-physical system design, which can address the above challenges, and encompass both horizontal and vertical integration steps. I use assume guarantee contracts and their algebra (e.g. composition, conjunction, and refinement) to support the entire design process and enable concurrent development of system architectures and control algorithms. In the methodology, the design is carried out as a sequence of refinement steps from a high-level specification to an implementation built out of a library of components at the lower level. Top-level system requirements are represented as contracts, by leveraging a set of formal languages, including mixed integer-linear constraints and temporal logic. Contracts are then refined by combining synthesis and optimization-based methods. I propose a set of optimization-based algorithms for efficient selection of cost-effective architectures under safety, reliability, and performance constraints over a large, mixed discrete-continuous design space. I demonstrate the effectiveness of the approach on industrial design examples, including aircraft electric power distribution and environmental control systems, showing, for instance, that optimal selection of industrial-scale power system architectures can be performed in a few minutes. Finally, I conclude by presenting future research directions towards a full-fledged integrated framework for system design.
Biography: Pierluigi Nuzzo is a Postdoctoral Scholar at the Department of Electrical Engineering and Computer Sciences of the University of California, Berkeley. He received the Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2015. He also holds the Laurea (M.Sc.) degree in Electrical Engineering (summa cum laude) from the University of Pisa and the Sant'Anna School of Advanced Studies, Pisa, Italy. Before joining U.C. Berkeley, he was a Researcher at IMEC, Leuven, Belgium, and the University of Pisa, working on the design of energy-efficient A/D converters, frequency synthesizers for reconfigurable radio, and design methodologies for mixed-signal integrated circuits. His research interests include: methodologies and tools for cyber-physical system and mixed-signal system design; contracts, interfaces, and compositional methods for embedded system design; energy-efficient analog and mixed-signal circuit design. Pierluigi received First Place in the operational category and Best Overall Submission in the 2006 DAC/ISSCC Design Competition, a Marie Curie Fellowship from the European Union in 2006, the University of California at Berkeley EECS departmental fellowship in 2008, the U.C. Berkeley Outstanding Graduate Student Instructor Award in 2013, and the IBM Ph.D. Fellowship in 2012 and 2014.
Host: Dr. Massoud Pedram
Location: 248
Audiences: Everyone Is Invited
Contact: Suzanne Wong
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CS Colloquium: Baris Kasikci (EPFL) - Stamping Out Concurrency Bugs
Thu, Mar 31, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Baris Kasikci, EPFL
Talk Title: Stamping Out Concurrency Bugs
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The shift to multi-core architectures in the past ten years pushed developers to write concurrent software to leverage hardware parallelism. The transition to multi-core hardware happened at a more rapid pace than the evolution of associated programming techniques and tools, which made it difficult to write concurrent programs that are both efficient and correct. Failures due to concurrency bugs are often hard to reproduce and fix, and can cause significant losses.
In this talk, I will first give an overview of the techniques we developed for the detection, root cause diagnosis, and classification of concurrency bugs. Then, I will discuss how the techniques we developed have been adopted at Microsoft and Intel. I will then discuss in detail Gist, a technique for the root cause diagnosis of failures. Gist uses hybrid static-dynamic program analysis and gathers information from real user executions to isolate root causes of failures. Gist is highly accurate and efficient, even for failures that rarely occur in production. Finally, I will close by describing future work I plan to do toward solving the challenges posed to software systems by emerging technology trends.
Biography: Baris Kasikci completed his Ph.D. in the Dependable Systems Laboratory (DSLAB) at EPFL, advised by George Candea. His research is centered around developing techniques, tools, and environments that help developers build more reliable and secure software. He is interested in finding solutions that allow programmers to better reason about their code, and that efficiently detect bugs, classify them, and diagnose their root cause. He especially focuses on bugs that manifest in production, because they are hard and time-consuming. He is also interested in efficient runtime instrumentation, hardware and runtime support for enhancing system security, and program analysis under various memory models.
Baris is one of the four recipients of the VMware 2014-2015 Graduate Fellowship. During his Ph.D., he interned at Microsoft Research, VMware, and Intel. Before starting his Ph.D., he worked as a software engineer for four years, mainly developing real-time embedded systems software. Before joining EPFL, he was working for Siemens Corporate Technology. More details can be found at http://www.bariskasikci.org/.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MFD - Chemical Engineering and Materials Science Distinguished Lecture: Nathan Price
Thu, Mar 31, 2016 @ 12:45 PM - 02:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Nathan Price, Univ. of California, San Diego
Talk Title: Harnessing big data for biological and medical discovery
Series: MFD Distinguished Lecture
Host: Prof. Nicholas Graham
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Jason Ordonez
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CS Colloquium: Konrad Kording (Northwestern University) - Neural Cryptography
Thu, Mar 31, 2016 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Konrad Kording, Northwestern University
Talk Title: Neural Cryptography
Series: CS Colloquium
Abstract: Neuroscience is slowly transitioning into a data rich discipline and large data sets allow new approaches. Brain decoders use neural recordings to infer what someone is thinking, viewing, or their intended movement. The problem has always been phrased as a supervised learning problem. Here we introduce a new method for brain decoding that does not require supervised data, i.e. the knowledge of the intended movement while the neural activity is recorded. Our approach is inspired by code breaking techniques used in cryptography where it is asked which mapping from from encrypted to decrypted text leads to text that most resembles the known structure of language. Analogously, we find a transformation of neural data (decoder) that aligns the distribution of the decoder output with the distribution of the user's intended movement. On a standard primate center-out reaching task, we demonstrate that we can obtain similar performance with that of a decoder with access to supervised data. However, current datasets are still too small to ask many relevant questions about neural computation and I am collaborating with neuroengineers to change that.
Host: CS Department
More Info: https://bluejeans.com/942986114
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/942986114
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CS Colloquium: Cynthia Sung (MIT CSAIL) - Computational Tools for Robot Design: A Composition Approach
Thu, Mar 31, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Cynthia Sung, MIT CSAIL
Talk Title: Computational Tools for Robot Design: A Composition Approach
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
As robots become more prevalent in society, they must develop an ability to deal with more diverse situations. This ability entails customizability of not only software intelligence, but also of hardware. However, designing a functional robot remains challenging and often involves many iterations of design and testing even for skilled designers. My goal is to create computational tools for making functional machines, allowing future designers to quickly improvise new hardware.
In this talk, I will discuss one possible approach to automated design using composition. I will describe our origami-inspired print-and-fold process that allows entire robots to be fabricated within a few hours, and I will demonstrate how foldable modules can be composed together to create foldable mechanisms and robots. The modules are represented parametrically, enabling a small set of modules to describe a wide range of geometries and also allowing geometries to be optimized in a straightforward manner. I will also introduce a tool that we have developed that combines this composition approach with simulations to help human designers of all skill levels to design and fabricate custom functional robots.
Biography: Cynthia Sung is a Ph.D. candidate in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT). She received a B.S. in Mechanical Engineering from Rice University in 2011 and an M.S. in Electrical Engineering and Computer Science from MIT in 2013. Her research interests include computational design, folding theory, and rapid fabrication, and her current work focuses on algorithms for synthesis and analysis of engineering designs.
Host: CS Department
More Info: https://bluejeans.com/727861390
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/727861390
