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Conferences, Lectures, & Seminars
Events for March
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Detecting paralinguistic information from speech and language for clinical applications: Algorithms and information limits
Wed, Mar 01, 2017 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Visar Berisha, Arizona State University
Talk Title: Detecting paralinguistic information from speech and language for clinical applications: Algorithms and information limits
Abstract: The ability to share our thoughts and ideas through spoken
communication is fragile. Even the simplest verbal response requires a
complex sequence of events. It requires thinking of the words that best
convey your message; sequencing these words appropriately; and then
sending signals to the muscles required to produce speech. The slightest
damage to the brain areas that orchestrate these events can manifest in
speech and language problems. These disturbances offer a window into
brain functioning. In the first part of this presentation, I will
present an overview of a number of projects where we use interpretable
measures of speech and language production as proxies for cognitive and
motor health. The algorithms behind this work have practical utility in
clinical applications and can help answer basic research questions
related to dysarthric speech production.
In the second part of the talk, I will discuss new results from
non-parametric statistical signal processing that allow us to
characterize the information limits in speech. In contrast to existing
methods based on machine learning, this work provides a framework to
answer fundamental questions such as 'What are the bounds on how well I
can recover a parameter of interest from speech?' or 'How well should an
optimally trained classifier work for a particular application?'
Biography: Visar Berisha is an Assistant Professor at Arizona State
University with a joint appointment in the School of Electrical Computer
and Energy Engineering and the Department of Speech and Hearing Science.
Prior to joining ASU, Berisha was a research scientist at MIT Lincoln
Laboratory and then Principal Research Engineer for a Fortune 500
company. His research interests include speech analytics, statistical
signal processing, and information theory. Much of his recent work spans
all three of these fields to answer basic questions related to the
limits of information in speech. His research has led to many academic
publications, several licensed patents, and a revenue-positive startup
company. Berisha's work has been featured in the Science section of the
New York Times, on National Public Radio, and a number of other national
media outlets.
Host: Shrikanth Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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CS Colloquium: Bistra Dilkina (Georgia Tech) -Challenges in Computational Sustainability
Wed, Mar 01, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Bistra Dilkina, Georgia Tech
Talk Title: Challenges in Computational Sustainability
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Computational sustainability is a new interdisciplinary research focused on computational problems that arise in the quest for sustainable development. The goal of sustainable development is to balance environmental, economic, and societal factors to "meet the needs of the present without compromising the ability of future generations to meet their own needs." In this talk, I will provide a sample of computational sustainability problems, from the areas of biodiversity conservation, energy, climate and environment monitoring. I will describe, for example, network design problems motivated by challenging planning problems in wildlife conservation. In this context, I will present a network design optimization framework for stochastic diffusion processes, such as species dispersal, fire spread, information propagation, and disease outbreak. I will also emphasize the unique opportunities for scalable constraint reasoning and optimization techniques to contribute to the new research
area of computational sustainability and describe our recent advances in improving the state-of-the-art in large-scale optimization by leveraging machine learning techniques to inform the design of combinatorial search algorithms.
Biography: Bistra Dilkina is an assistant professor in the College of Computing at the Georgia Institute of Technology and a Fellow at the Brook Byers Institute for Sustainable Systems. She received her PhD from Cornell University in 2012, and was a Post-Doctoral associate at the Institute for Computational Sustainability until 2013. Her research focuses on advancing the state of the art in combinatorial optimization techniques for solving real-world large-scale problems, particularly ones that arise in sustainability areas such as biodiversity conservation planning and urban planning. Her work spans discrete optimization, network design, stochastic optimization, and machine learning. She is also the co-director of the Data Science for Social Good (DSSG) Atlanta summer program, which partners student teams with government and nonprofit organizations to help solve some of their most difficult problems using analytics, modeling, prediction and visualization. Bistra has (co-)authored over 30 publications, and has won several awards, including Best Student Paper runner up at KDD 2016, Best Paper of the INFORMS ENRE Section, Lockheed Inspirational Young Faculty Award, Raytheon Faculty Fellowship, and Georgia Power Professor of Excellence Award.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MHI CommNetS Seminar
Wed, Mar 01, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Arpan Chattopadhyay, USC
Talk Title: Sequential decision algorithms for as-you-go deployment of wireless relay network along a line
Series: CommNetS
Abstract: We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, starting from a sink node (e.g., a base-station), and proceeds towards a source node (e.g., a sensor) which is at an a priori unknown location. At equally spaced locations, he makes link quality measurements to the previous relay, and deploys relays at some of these locations, with the aim to connect the source to the sink by a multihop wireless path. In this paper, we consider two approaches for impromptu deployment: (i) the deployment agent can only move forward (which we call a pure as-you-go approach), and (ii) the deployment agent can make measurements over several consecutive steps before selecting a placement location among them (the explore-forward approach). We consider a very light traffic regime, and formulate the problem as a Markov decision process, where the trade-off is among the power used by the nodes, the outage probabilities in the links, and the number of relays placed per unit distance. We obtain the structures of the optimal policies for the pure as-you-go approach as well as for the explore-forward approach. We also consider natural heuristic algorithms, for comparison. Numerical examples show that the explore-forward approach significantly outperforms the pure as- you-go approach in terms of network cost. Next, we propose learning algorithms for the explore-forward approach and the pure as-you-go approach, based on single and two timescale Stochastic Approximation, which asymptotically converge to the set of optimal policies, without using any knowledge of the radio propagation model. We demonstrate numerically that the learning algorithms can converge (as deployment progresses) to the set of optimal policies reasonably fast and, hence, can be practical model-free algorithms for deployment over large regions. Finally, we demonstrate the end-to-end traffic carrying capability of such networks via field deployment.
Biography: Arpan Chattopadhyay obtained his B.E. in Electronics and Telecommunication Engineering from Jadavpur University, Kolkata, India in the year 2008, and M.E. and Ph.D in Telecommunication Engineering from Indian Institute of Science, Bangalore in the year 2010 and 2015, respectively. Then he worked as a postdoc in the group DYOGENE of INRIA/ENS Paris. He joined EE department, USC as a postdoc from November 2016. His host is Prof. Urbashi Mitra. His research interests include optimization, learning and control of wireless networks and cyber-physical systems.
Host: Prof. Ashutosh Nayyar
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Astani Civil and Environmental Engineering Seminar
Thu, Mar 02, 2017 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Tzahi Cath, Colorado School of Mines
Talk Title: Challenges of Water Treatment on Watershed Behavior: Insights Gained Using Geophysical Methods
Abstract: TBA
Host: Dr. Amy Childress
Location: TBA
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Big Data, Streaming Graphs, and the Need for Innovations in Architecture
Fri, Mar 03, 2017 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Peter Kogge, University of Notre Dame
Talk Title: Big Data, Streaming Graphs, and the Need for Innovations in Architecture
Abstract: This talk will start with some insights gleaned from looking at real-world big data problems and how they are affected by architecture. The Emu migrating thread architecture is then introduced and compared. A general template for integrated big graph batch and streaming analytic processing is developed, and key graph operations, especially streaming, listed. A discussion follows on how the Emu architecture meshes well with such a dual-mode computing template, with some specific emphasis on machine learning functions.
Biography: Peter M. Kogge received his Ph.D. in EE from Stanford in 1973. From 1968 until 1994 he was with IBM's Federal Systems Division, and was appointed an IBM Fellow in 1993. In August, 1994 he joined the University of Notre Dame as first holder of the endowed McCourtney Chair in Computer Science and Engineering. He has served as both Department Chair and Associate Dean for Research, College of Engineering. He is an IEEE Fellow, a Distinguished Visiting Scientist at JPL, and a founder and Chief Scientist of Emu Solutions, Inc. His research interests are in massively parallel computing paradigms, processing in memory, and the relationship between massive non-numeric applications, emerging technology, and computer architectures.
He holds over 40 patents and is author of two books, including the first text on pipelining. His Ph.D. thesis led to the Kogge-Stone adder used in many microprocessors. Other projects included EXECUBE - the world's first multi-core processor and first processor on a DRAM chip, the IBM 3838 Array processor which was for a time the fastest floating point machine marketed by IBM, and the IOP - the world's second multi-threaded parallel processor which flew on every Space Shuttle. In 2008, he led DARPA's Exascale technology study group, which resulted in a widely referenced report on technologies and architectures for exascale computing, and has had key roles on many other HPC programs. His startup, Emu Solutions, has demonstrated the first scalable system that utilizes mobile threads to attack large-scale big data and big graph problems.
Dr. Kogge has received the Daniel Slotnick best paper award (1994), the IEEE Seymour Cray award for high performance computer engineering (2012), the IEEE Charles Babbage award for contributions to the evolution of massively parallel processing architectures (2014), the IEEE Computer Pioneer award (2015), and the Gauss best paper award for high performance computers (2015).
Host: Viktor Prasanna, EEB 200, prasanna@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Big Data, Streaming Graphs, and the Need for Innovations in Architecture
Fri, Mar 03, 2017 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Peter Kogge, University of Notre Dame
Talk Title: Big Data, Streaming Graphs, and the Need for Innovations in Architecture
Abstract: This talk will start with some insights gleaned from looking at real-world big data problems and how they are affected by architecture. The Emu migrating thread architecture is then introduced and compared. A general template for integrated big graph batch and streaming analytic processing is developed, and key graph operations, especially streaming, listed. A discussion follows on how the Emu architecture meshes well with such a dual-mode computing template, with some specific emphasis on machine learning functions.
Biography: x
Host: Viktor Prasanna, EEB 200, prasanna@usc.edu
Location: 248
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Mar 03, 2017 @ 02:30 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Scott Powell, VP Engineering at Jariet Technologies
Talk Title: CMOS Digital Microwave
Host: Profs. Hossein Hashemi, Mike Chen, Dina El-Damak, and Mahta Moghaddam
More Information: MHI Seminar Series IS - Scott Powell.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
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CS Colloquium: Adish Singla (ETH Zurich) - Learning With and From People
Mon, Mar 06, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Adish Singla, ETH Zurich
Talk Title: Learning With and From People
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
People are becoming an integral part of computational systems, fueled primarily by recent technological advancements as well as deep-seated economic and societal changes. Consequently, there is a pressing need to design new data science and machine learning frameworks that can tackle challenges arising from human participation (e.g. questions about incentives and users' privacy) and can leverage people's capabilities (e.g. ability to learn).
In this talk, I will share my research efforts at the confluence of people and computing to address real-world problems. Specifically, I will focus on collaborative consumption systems (e.g. shared mobility systems and sharing economy marketplaces like Airbnb) and showcase the need to actively engage users for shaping the demand who would otherwise act primarily in their own interest. The main idea of engaging users is to incentivize them to switch to alternate choices that would improve the system's effectiveness. To offer optimized incentives, I will present novel multi-armed bandit algorithms and online learning methods in structured spaces for learning users' costs for switching between different pairs of available choices. Furthermore, to tackle the challenges of data sparsity and to speed up learning, I will introduce hemimetrics as a structural constraint over users' preferences. I will show experimental results of applying the proposed algorithms on two real-world applications: incentivizing users to explore unreviewed hosts on services like Airbnb and tackling the imbalance problem in bike sharing systems. In collaboration with an ETH Zurich spinoff and a public transport operator in the city of Mainz, Germany, we deployed these algorithms via a smartphone app among users of a bike sharing system. I will share the findings from this deployment.
Biography: Adish Singla is a PhD student in the Learning and Adaptive Systems Group at ETH Zurich. His research focuses on designing new machine learning frameworks and developing algorithmic techniques, particularly for situations where people are an integral part of computational systems. Before starting his PhD, he worked as a Senior Development Lead in Bing Search for over three years. He is a recipient of the Facebook Fellowship in the area of Machine Learning, Microsoft Research Tech Transfer Award, and Microsoft Gold Star Award.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Mon, Mar 06, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Wei Wu, PhD, Associate Professor, USC Viterbi Electrical Engineering/Electrophysics
Talk Title: Sub-5 nm Patterning and Applications
Host: Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Mon, Mar 06, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Aranya Chakrabortty, Associate Professor, North Carolina State University, Raleigh, NC
Talk Title: Cyber-Physical Challenges for Wide-Area Control of Power Systems
Abstract: In this talk I will present a novel cyber-physical architecture for wide-area control of power systems using massive volumes of Synchrophasor data. The first half of the talk will focus on the computational aspects of the control design, where I will present several recent results on a new design approach called "control inversion". By this approach, very large-dimensional power system models can be projected conveniently into lower dimensional spaces by exploiting the inherent clustering properties of the network dynamics; then, a reduced-order controller is designed for this simple model, and, finally, this controller is projected back to the full-dimensional network for actual implementation. The method not only improves the tractability of the design, but also provides significant savings in the number of communication links needed for feedback. In the second half of the talk, I will shift my attention towards two of the most important challenges in data communication arising in wide-area control- namely, sensitivity to delays and data sparsification. Using the concepts of modal participation factors and relative gain arrays, I will propose a distributed communication architecture by which control centers can implement a sparse realization of wide-area controllers with very little loss in the overall response. I will also describe a co-design strategy by which one can spot the most important generators in the system for the purpose of oscillation damping after any disturbance in real-time, and, thereafter, prioritize the communication of states from these special generators to minimize the overall delay in the feedback path. I will present simulations to illustrate the pros and cons of such data prioritization, and their associated protocol designs. The talk will end with some final remarks about the resilience of these wide-area protocols against denial-of-service and data manipulation attacks.
The overall goal of the talk will be to pinpoint some of the most challenging CPS problems for today's grid where power engineers can largely benefit from collaborations with communication engineers, computer scientists, and numerical analysts. The content will highlight my recent works with PhD students Nan Xue and Abhishek Jain at NC State, as well as my work with Dr. Anuradha Annaswamy and her postdocs from MIT.
Biography: Aranya Chakrabortty received his PhD degree in Electrical Engineering from Rensselaer Polytechnic Institute, Troy, NY, in 2008. Following that he was a postdoctoral research associate at the University of Washington Seattle, for a year. From 2009 to 2010, he was an Assistant Professor at Texas Tech University. Since 2010, Aranya has joined the Electrical and Computer Engineering department of North Carolina State University, where he is currently an Associate Professor. His research interests are in the general area of power system dynamics, modeling, stability, and control, with a special focus on wide-area monitoring and control using Synchrophasors. He is a senior member of IEEE, and currently serves as an Associate Editor of IEEE Transactions on Control Systems Technology. He received the NSF CAREER award in 2011.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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CS Colloquium: Philip Thomas (CMU) - Safe Machine Learning
Tue, Mar 07, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Philip Thomas, Carnegie Mellon University
Talk Title: Safe Machine Learning
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are safe-”that they do not, for example, cause harm to humans or act in a racist or sexist way-”is therefore not a hypothetical problem to be dealt with in the future, but a pressing one that we can and should address now.
In this talk I will discuss some of my recent efforts to develop safe machine learning algorithms, and particularly safe reinforcement learning algorithms, which can be responsibly applied to high-risk applications. I will focus on a specific research problem that is central to the design of safe reinforcement learning algorithms: accurately predicting how well a policy would perform if it were to be used, given data collected from the deployment of a different policy. Solutions to this problem provide a way to determine that a newly proposed policy would be dangerous to use without requiring the dangerous policy to ever actually be used.
Biography: Philip Thomas is a postdoctoral research fellow in the Computer Science Department at Carnegie Mellon University, advised by Emma Brunskill. He received his Ph.D. from the College of Information and Computer Sciences at the University of Massachusetts Amherst in 2015, where he was advised by Andrew Barto. Prior to that, Philip received his B.S. and M.S. in computer science from Case Western Reserve University in 2008 and 2009, respectively, where Michael Branicky was his adviser. Philip's research interests are in machine learning with emphases on reinforcement learning, safety, and designing algorithms that have practical theoretical guarantees.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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INFORMATION DROPOUT: LEARNING OPTIMAL REPRESENTATIONS THROUGH NOISY COMPUTATION
Tue, Mar 07, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Alessandro Achille, UCLA
Talk Title: INFORMATION DROPOUT: LEARNING OPTIMAL REPRESENTATIONS THROUGH NOISY COMPUTATION
Series: Natural Language Seminar
Abstract: The cross-entropy loss commonly used in deep learning is closely related to the information theoretic properties defining an optimal representation of the data, but does not enforce some of the key properties. We show that this can be solved by adding a regularization term, which is in turn related to injecting multiplicative noise in the activations of a Deep Neural Network, a special case of which is the common practice of dropout. Our regularized loss function can be efficiently minimized using Information Dropout, a generalization of dropout rooted in information theoretic principles that automatically adapts to the data and can better exploit architectures of limited capacity.
When the task is the reconstruction of the input, we show that our loss function yields a Variational Autoencoder as a special case, thus providing a link between representation learning, information theory and variational inference. Finally, we prove that we can promote the creation of disentangled representations of the input simply by enforcing a factorized prior, a fact that has been also observed empirically in recent work.
Our experiments validate the theoretical intuitions behind our method, and we find that Information Dropout achieves a comparable or better generalization performance than binary dropout, especially on smaller models, since it can automatically adapt the noise structure to the architecture of the network, as well as to the test sample.
Biography: Alessandro Achille is a PhD student in Computer Science at UCLA, working with Prof. Stefano Soatto. He focuses on variational inference, representation learning, and their applications to deep learning and computer vision. Before coming to UCLA, he obtained a Master's degree in Pure Math at the Scuola Normale Superiore in Pisa, where he studied model theory and algebraic topology with Prof. Alessandro Berarducci.
Host: Greg Ver Steeg
More Info: https://arxiv.org/abs/1611.01353
Location: Information Science Institute (ISI) - 6th Flr -CR#689 (ISI/Marina Del Rey)
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: https://arxiv.org/abs/1611.01353
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Epstein Seminar, ISE 651
Tue, Mar 07, 2017 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Daniel Robinson, Assistant Professor, Johns Hopkins University
Talk Title: Scalable Optimization Algorithms For Large-Scale Subspace Clustering
Host: Jong-Shi Pang
More Information: March 7, 2017_Robinson.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Colloquium: David Naylor (CMU) - Privacy in the Internet (Without Giving up Everything Else)
Thu, Mar 09, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Naylor, Carnegie Mellon University
Talk Title: Privacy in the Internet (Without Giving up Everything Else)
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Using the Internet inherently entails privacy risks. Each packet, potentially carrying information that users would rather keep private, is exposed to a network infrastructure operated by a number of third parties the user may not trust and likely cannot even identify. In some cases, the user may not even trust the recipient.
Techniques exist to protect user privacy, but they typically do so at the expense of other desirable properties. For example, anonymity services like Tor hide a packet's true sender, but weaken accountability by making it difficult for network administrators or law enforcement to track down malicious senders. Similarly, encryption hides application data from third parties, but prevents the use of middleboxes---devices that process packets in the network to improve performance (like caches) or security (like intrusion detection systems).
In this talk, I'll present techniques for managing these "Privacy vs. X" conflicts, including a new network architecture that re-thinks basic networking building blocks like packet source addresses and new secure communication protocols that explicitly balance data privacy with the benefits of middleboxes.
Biography: David is a Ph.D. student at Carnegie Mellon University, where he is advised by Peter Steenkiste. His primary research interests are computer networking, security, and privacy, but he is also interested in Web measurement and performance (http://isthewebhttp2yet.com and https://eyeorg.net). David received his B.S. from the University of Iowa in 2011, where he created the DDR inspired "Scrub Scrub Revolution," a handwashing training game for healthcare professionals. He is an NDSEG fellow and received an ACM SIGCOMM best paper award.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminar
Thu, Mar 09, 2017 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hung-Wei Tseng, NC State University
Talk Title: Modernizing Storage Systems for Big Data Applications
Abstract: Existing high-speed non-volatile storage systems leverage entrenched system stack developed for magnetic hard disk drive, leading to suboptimal performance and under-utilized system resources. As data set sizes of applications keep increasing, using conventional system stack for modern storage devices becomes a new performance bottleneck. For example, a database system can spend 80% of time in just fetching data from the storage system, leaving precious computing resource idle at the same time.
To improve the performance of serving data from storage systems, we need to revisit the block-based storage interface designed for slower, magnetic disk drives. In this talk, Hung-Wei will share his experience in modernizing the hardware/software interface for storage systems and achieve performance gain in computer systems. Hung-Wei will introduce his research projects including: (1) HippogriffDB, a GPU-based database system that balances the huge gap between the throughputs of the GPU and the SSD. (2) Morpheus-SSD that utilizes computing resources inside storage devices to create more efficient applications. (3) KAML that modernizes the conventional block-based I/O with a keyvalue-like interface.
Biography: Hung-Wei is currently an assistant professor in the Department of Computer Science and Department of Electrical and Computer Engineering, NC State University where he is now leading the Extreme Storage & Computer Architecture Laboratory. Prior to joining NCSU, Hung-Wei was a postdoctoral scholar of the Non-volatile Systems Laboratory with Professor Steven Swanson and a lecturer of the Department of Computer Science and Engineering at University of California, San Diego. His thesis work with Professor Dean Tullsen, data-triggered threads, was selected by IEEE Micro "Top Picks from Computer Architecture" in 2012.
Host: Murali Annavaram
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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Lyman L. Handy Colloquia
Thu, Mar 09, 2017 @ 12:45 PM - 01:50 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Ronnie Borja , Stanford University
Talk Title: A constitutive framework for double-porosity materials with evolving internal structure
Series: Lyman Handy Colloquia
Host: Professor Birendra Jha
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Martin Olekszyk
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Viterbi Distinguished Lecture
Thu, Mar 09, 2017 @ 04:00 PM - 05:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Peter Shor, MIT
Talk Title: Capacities for Quantum Communication Channels
Series: Viterbi Lecture
Abstract: In 1948, Shannon discovered his famous formula for the capacity of a communication channel. This formula does not apply, however, to channels with significant quantum effects. For quantum channels, the question of capacity is much more complicated, as there are different capacities for sending classical information and for sending quantum information. We will discuss the capacities of quantum channels, and survey the historical development of the subject.
Biography: Peter Shor received a B.S. in Mathematics from Caltech in 1981, and a Ph.D. in Applied Mathematics from M.I.T. After a one-year postdoctoral fellowship at the Mathematical Sciences Research Institute in Berkeley, he took a job at AT&T Bell Laboratories, and stayed at AT&T until 2003. In 2003, he went to M.I.T., where he is the Morss Professor of Applied Mathematics.
Until 1994, he worked on algorithms for conventional computers and did research in probability and combinatorics. In 1994, after thinking about the problem on and off for nearly a year, he discovered an algorithm for factoring large integers into primes on a quantum computer (still hypothetical, but steadily becoming less so). Since then, he has mainly been investigating quantum computing and quantum information theory.
Among other awards, he has received the Nevanlinna Prize, the Goedel prize, and a MacArthur Fellowship. He is a member of the American Academy of Arts and Sciences and of the National Academy of Sciences.
Host: Professor Sandeep K. Gupta
Webcast: https://bluejeans.com/547084462Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://bluejeans.com/547084462
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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CS Invited Lecture: Richard Anfang - Life Lessons of a Wall St. CIO
Fri, Mar 10, 2017 @ 10:00 AM - 10:50 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Richard Anfang,
Talk Title: Life Lessons of a Wall St. CIO
Abstract: Please join special Guest Lecturer, CIO Richard Anfang, as he shares technology experience and insight from his 30+ year career on Wall Street. Open to all CS students
Richard will discuss technology, innovation, business strategy, and talent. He will also provide valuable career advice and his thoughts on the importance of mentorship.
Biography: Richard Anfang is a technology executive with over 30 years of experience working in global financial service organizations. He has held senior management positions as a business-aligned Chief Information Officer, managed enterprise technology infrastructure, and partnered closely with C-level executives. Anfang has an extensive track record delivering innovative technology solutions to solve business problems within the financial services sector.
Most recently, he worked at JPMorgan Chase where he was Chief Architect of the firm's Global Technology Infrastructure organization. From 2013 to early 2014, he was the Chief Information Officer of JPMorgan's Asset Management business. He was responsible for the business' strategic leadership of technology, overseeing over 3,000 technologists globally spanning the firm's global Investment Management and Private Bank businesses. From 2008 to 2012, he was the Chief Information Officer of JPMorgan's Worldwide Securities Services business.
Prior to joining JPMorgan, he spent over 24 years at Morgan Stanley. During that time, he held a number of senior Information Technology positions including Chief Technology Officer of the Prime Brokerage business, global head of the firm's Enterprise Infrastructure group, head of Equity and Fixed Income Sales & Trading applications development, and Chief Information Officer of Morgan Stanley's UK and European businesses.
Anfang has an extensive track record of delivering innovative solutions and solving business problems in the financial services industry. He has served on the advisory board of several technology service providers and has participated in numerous industry forums and committees.
He holds a Master of Science degree in Computer Science from the University of Pennsylvania (1983) and a Bachelor of Science degree in Computer Science from the University of Michigan (1981).
Host: CS Department
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 116
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Fri, Mar 10, 2017 @ 02:00 PM - 03:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Muyinatu , Assistant Professor & PULSE Lab Director, Johns Hopkins (Whiting School of Engineering)
Talk Title: TBA
Series: Distinguished Speaker Series, Dept. of Biomedical Engineering
Biography: Biography: Dr. Muyinatu A. Lediju Bell (informally known as "Bisi") is an assistant professor of Electrical and Computer Engineering with a joint appointment in the Biomedical Engineering Department. Dr. Bell obtained a Ph.D. in Biomedical Engineering from Duke University and a B.S. in Mechanical Engineering (BME minor) from Massachusetts Institute of Technology. In addition, Dr. Bell spent a year abroad as a Whitaker International Fellow, conducting research at the Institute of Cancer Research and Royal Marsden Hospital in the United Kingdom. Prior to joining the faculty, Dr. Bell was a postdoctoral fellow with the Engineering Research Center for Computer-Integrated Surgical Systems and Technology at Johns Hopkins University. She published over 40 scientific journal articles and conference papers, holds a patent for SLSC beamforming, and is the recipient of numerous awards, grants, and fellowships, including the prestigious NIH K99/R00 Pathway to Independence Award and the esteemed MIT Technology Review 35 Innovators Under 35 Award.
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Mar 10, 2017 @ 02:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Timothy O. Dickson, Research Staff Member, IBM T.J. Watson Research Center
Talk Title: Breaking from Tradition: New Approaches in CMOS Wireline Transceivers for 28-56Gb/s Serial Links
Host: Profs. Hossein Hashemi, Mike Chen, Dina El-Damak, and Mahta Moghaddam
More Information: MHI Seminar Series IS - Timothy Dickson.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Jenny Lin
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Learning agents that interact with humans
Fri, Mar 10, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: He He, Stanford Univ.
Talk Title: Learning agents that interact with humans
Series: Natural Language Seminar
Abstract: The future of virtual assistants, self driving cars, and smart homes require intelligent agents that work intimately with users. Instead of passively following orders given by users, an interactive agent must actively collaborate with people through communication, coordination, and user adaptation. In this talk, I will present our recent work towards building agents that interact with humans. First, we propose a symmetric collaborative dialogue setting in which two agents, each with some private knowledge, must communicate in natural language to achieve a common goal. We present a human-human dialogue dataset that poses new challenges to existing models, and propose a neural model with dynamic knowledge graph embedding. Second, we study the user-adaptation problem in quizbowl - a competitive, incremental question answering game. We show that explicitly modeling of different human behavior leads to more effective policies that exploits sub optimal players. I will conclude by discussing opportunities and open questions in learning interactive agents.
Biography: He He is a post-doc at Stanford University, working with Percy Liang. Prior to Stanford, she earned her Ph.D. in Computer Science at the University of Maryland, College Park, advised by Hal Daume III and Jordan Boyd Graber. Her interests are at the interface of machine learning and natural language processing. She develops algorithms that acquire information dynamically and do inference incrementally, with an emphasis on problems in natural language processing. She has worked on dependency parsing, simultaneous machine translation, question answering, and more recently dialogue systems.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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2017 William G. Spitzer Lecture
Fri, Mar 10, 2017 @ 03:30 PM - 04:30 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Prof. Supratik Guha, The Institute for Molecular Engineering, The University of Chicago
Talk Title: Nano-materials for Practical Applications: Opportunities in Computing and Cyberphysical Sensing Networks
Host: Dr. Anupam Madhukar
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: Aleessa Atienza
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SPRING BREAK (NO SEMINAR)
Mon, Mar 13, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: SPRING BREAK (NO SEMINAR), SPRING BREAK (NO SEMINAR)
Talk Title: SPRING BREAK (NO SEMINAR)
Host: Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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CS Yahoo! Machine Learning Seminar: Anshumali Shrivastava (Rice University) - Probabilistic Hashing for Scalable, Sustainable and Secure Machine Learning
Fri, Mar 17, 2017 @ 10:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Anshumali Shrivastava, Rice University
Talk Title: Probabilistic Hashing for Scalable, Sustainable and Secure Machine Learning
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: Large scale machine learning and data mining applications are constantly dealing with datasets at TB scale and the anticipation is that soon it will reach PB level. At this scale, simple data mining operations such as search, learning, and clustering become challenging.
In this talk, we will start with a basic introduction to probabilistic hashing (or fingerprinting) and the classical LSH algorithm. Then I will present some of my recent adventures with probabilistic hashing in making large-scale machine learning practical. I will show how the
idea of probabilistic hashing can be used to significantly reduce the computations in classical machine learning algorithms such Deep Learning (using our recent success with asymmetric hashing for inner products). I will highlight the computational bottleneck, i.e. the hashing time, and will show an efficient variant of minwise hashing. In the end, if time permits, I will demonstrate the use of probabilistic hashing for obtaining practical privacy-preserving
algorithms.
Biography: Anshumali Shrivastava is an assistant professor in the computer science department at Rice University. His broad research interests include large scale machine learning, randomized algorithms for big data systems and graph mining. He is a recipient of 2017 NSF CAREER Award. His research on hashing inner products has won Best Paper Award at NIPS 2014 while his work on representing graphs got the Best Paper Award at IEEE/ACM ASONAM 2014. He obtained his PhD in computer science from Cornell University in 2015.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Fri, Mar 17, 2017 @ 02:30 PM - 04:30 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: SPRING BREAK, NO CLASS, SPRING BREAK, NO CLASS
Talk Title: SPRING BREAK, NO CLASS
Series: Seminars in BME (Lab Rotations)
Host: Brent Liu, PhD
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Heterogeneous Attribute Embedding and Sequence Modeling for Recommendation with Implicit Feedback
Fri, Mar 17, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Kuan Liu, USC/ISI
Talk Title: Heterogeneous Attribute Embedding and Sequence Modeling for Recommendation with Implicit Feedback
Series: Natural Language Seminar
Abstract: Incorporating implicit feedback into a recommender system is a challenging problem due to sparse and noisy observations. I will present our approaches that exploit heterogeneous attributes and sequence properties within the observations. We build a neural network framework to embed heterogeneous attributes in an end-to-end fashion, and apply the framework to three sequence-based models. Our methods achieve significant improvements on four large scale datasets compared to state-of-the-art baseline models 30 to 90 percent relative increase in NDCG. Experimental results show that attribute embedding and sequence modeling both lead to improvements and, further, that our novel output attribute layer plays a crucial role. I will conclude with our exploratory studies that investigate why sequence modeling works well in recommendation systems and advocate its use for large scale recommendation tasks.
Biography: Kuan Liu is a fifth year Ph.D. student at ISI/USC working with Prof. Prem Natarajan. Before that, He received a bachelor degree from Tsinghua University with a major in Computer Science. His research interests include machine learning, large scale optimization, deep learning, and applications to recommender systems, network analysis.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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CS Colloquium: Yuanjie Li (UCLA) - Stimulating Intelligence in the Mobile Networked Systems
Mon, Mar 20, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yuanjie Li, UCLA
Talk Title: Stimulating Intelligence in the Mobile Networked Systems
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
The mobile networked systems (4G and upcoming 5G) are at a critical stage of the technology revolution. Despite offering working solutions for billions of users, they are complex and closed: The infrastructure lacks guarantees for the right designs and operations, while the mobile client lacks the insights of the "black-box" network behaviors. Both fundamentally limit our understanding of why various problems could happen, and how to resolve them.
In this talk, I describe primitives that stimulate more infrastructure and client intelligence. For the infrastructure, I present verification and state management techniques that enforce provably correct designs and operations. For the client, I show how a data-driven system design allows it to be more active in improving its performance, reliability, and security. These results suggest that the future systems (5G) should be equipped with more intelligence, and make themselves easy to understand and use.
Biography: Yuanjie Li is a Ph.D. candidate in Computer Science at UCLA, advised by Professor Songwu Lu. His interests include the networked systems, mobile computing, and their security. He has won ACM MobiCom'16 Best Community Paper Award and UCLA Dissertation Year Fellowship in 2016. His work has resulted in an open-source community tool (MobileInsight) used by 130 universities and companies so far.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AI Seminar
Mon, Mar 20, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Xiang Ren, Computer Science PhD candidate at University of Illinois at UrbanaChampaign
Talk Title: EFFORT-LIGHT STRUCTMINE: TURNING MASSIVE CORPORA INTO STRUCTURES
Series: Recruitng Seminar
Abstract: The realworld data, though massive, are hard for machines to resolve as they are largely unstructured and in the form of natural-language text. One of the grand challenges is to turn such massive corpora into machine-actionable structures. Yet, most existing systems have heavy reliance on human effort in the process of structuring various corpora, slowing down the development of downstream applications.
In this talk, I will introduce a data-driven framework, EffortLight StructMine, that extracts structured facts from massive corpora without explicit human labeling effort. In particular, I will discuss how to solve three structure mining tasks under Effort-Light StructMine framework: from identifying typed entities in text, to fine-grained entity typing, to extracting typed relationships between entities. Together, these three solutions form a clear roadmap for turning a massive corpus into a structured network to represent its factual knowledge. Finally, I will share some directions towards mining corpus-specific structured networks for knowledge discovery.
Biography: Xiang Ren is a Computer Science PhD candidate at University of Illinois at Urbana-Champaign, working with Jiawei Han and the Data and Information System DAIS Research Lab. The research Xiang develops data-driven methods for turning unstructured text data into machine-actionable structures. More broadly, his research interests span data mining, machine learning, and natural language processing, with a focus on making sense of massive text corpora. His research has been recognized with several prestigious awards including a Google PhD Fellowship, a Yahoo!-DAIS Research Excellence Award, and a C. W. Gear Outstanding Graduate Student Award from UIUC Computer Science. Technologies he developed has been transferred to US Army Research Lab, NIH, Microsoft, Yelp and TripAdvisor
Host: Craig Knoblock
Webcast: http://webcastermshd.isi.edu/Mediasite/Play/6b83d48fc61f4e398d8d8bbdff0004e01dLocation: Information Science Institute (ISI) - 11th Floor Large CR #1135
WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/6b83d48fc61f4e398d8d8bbdff0004e01d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
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Seminars in Biomedical Engineering
Mon, Mar 20, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Zhongping Chen , Professor of Biomedical Engineering, UC Irvine, Beckman Laser Institute
Talk Title: Novel OCT for Biomedical Application
Host: Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Mon, Mar 20, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Brian Munsky, Assistant Professor, Colorado State University
Talk Title: Identification of stochastic models to predict single-cell gene regulation dynamics
Abstract: Stochastic fluctuations can cause identical cells or individual molecules to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, makes predictive understanding and control all but impossible. However, if we computationally examine fluctuations more closely and carefully match them to discrete stochastic analyses, we discover virtually untapped, yet powerful sources of information and new opportunities. In this talk, I will present our collaborative endeavors to integrate single-cell and single-molecule experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for signal-activated gene regulation. I will explain how we experimentally quantify transcription dynamics at high temporal and spatial resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding. We will examine how different data statistics (e.g., expectation values versus probability densities) contribute to model bias and uncertainty, and we will show how these affect predictive power. Finally, we will introduce a new approach to compute the Fisher Information Matrix, and we will illustrate its application for the improved design of single-cell experiments.
Biography: Dr. Munsky received B.S. and M.S. degrees in Aerospace Engineering from the Pennsylvania State University in 2000 and 2002, respectively, and his Ph.D. in Mechanical Engineering from the University of California at Santa Barbara in 2008. Following his graduate studies, Dr. Munsky worked at the Los Alamos National Laboratory -” as a Director's Postdoctoral Fellow (2008-2010), as a Richard P. Feynman Distinguished Postdoctoral Fellow in Theory and Computing (2010-2013), and as a Staff Scientist (2013). In 2014, he joined the Colorado State University Department of Chemical and Biological Engineering and the School of Biomedical Engineering, in which he is now an Assistant Professor. Dr. Munsky is best known for his discovery of Finite State Projection algorithm, which has enabled the efficient study of probability distribution dynamics for stochastic gene regulatory networks. Dr. Munsky's research interests are in the integration of discrete stochastic models with single-cell experiments to identify predictive models of gene regulatory systems. Dr. Munsky was the recipient of the 2008 UCSB Department of Mechanical Engineering best Ph.D. Dissertation award, the 2010 Leon Heller Postdoctoral Publication Prize, and the 2012 LANL Postdoc Distinguished Performance Award for his work in this topic. Dr. Munsky became a Keck Scholar in 2016. Dr. Munsky is the contact organizer of the internationally recognized, NIH-funded q-bio Summer School (q-bio.org), where he runs a course on single-cell stochastic gene regulation.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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USC Stem Cell Seminar: Flora Vaccarino, Yale University
Tue, Mar 21, 2017 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Flora Vaccarino, Yale University
Talk Title: TBD
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Host: USC Stem Cell
More Info: http://stemcell.usc.edu/events
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
Event Link: http://stemcell.usc.edu/events
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CS Colloquium: Justin Cheng (Stanford) - Antisocial Computing: Explaining and Predicting Negative Behavior Online
Tue, Mar 21, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Justin Cheng, Stanford University
Talk Title: Antisocial Computing: Explaining and Predicting Negative Behavior Online
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Antisocial behavior and misinformation are increasingly prevalent online. As users interact with one another on social platforms, negative interactions can cascade, resulting in complex changes in behavior that are difficult to predict. My research introduces computational methods for explaining the causes of such negative behavior and for predicting its spread in online communities. It complements data mining with crowdsourcing, which enables both large-scale analysis that is ecologically valid and experiments that establish causality. First, in contrast to past literature which has characterized trolling as confined to a vocal, antisocial minority, I instead demonstrate that ordinary individuals, under the right circumstances, can become trolls, and that this behavior can percolate and escalate through a community. Second, despite prior work arguing that such behavioral and informational cascades are fundamentally unpredictable, I demonstrate how their future growth can be reliably predicted. Through revealing the mechanisms of antisocial behavior online, my work explores a future where systems can better mediate interpersonal interactions and instead promote the spread of positive norms in communities.
Biography: Justin Cheng is a PhD candidate in the Computer Science Department at Stanford University, where he is advised by Jure Leskovec and Michael Bernstein. His research lies at the intersection of data science and human-computer interaction, and focuses on cascading behavior in social networks. This work has received a best paper award, as well as several best paper nominations at CHI, CSCW, and ICWSM. He is also a recipient of a Microsoft Research PhD Fellowship and a Stanford Graduate Fellowship.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Improved Myocardial Arterial Spin Labeled Perfusion Imaging
Tue, Mar 21, 2017 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hung Phi Do, Department of Physics and Astronomy, University of Southern California
Talk Title: Improved Myocardial Arterial Spin Labeled Perfusion Imaging
Series: Medical Imaging Seminar Series
Abstract: Coronary artery disease (CAD) affects more than 15.5 million Americans and causes approximately 310,000 deaths per year. Several different diagnostic tests are performed to diagnose and manage this disease. One of the most common is perfusion stress testing, primarily performed using single photon emission computed tomography (SPECT) or first-pass cardiovascular magnetic resonance (CMR). These methods require the use of ionizing radiation or exogenous contrast agents that carry associated risks to patients, especially those who require frequent assessment or have kidney dysfunction. Myocardial arterial spin labeling (ASL) is a promising MRI-based perfusion imaging method that can quantitatively measure myocardial tissue perfusion without the use of ionizing radiation or exogenous contrast agents. Its feasibility has been previously demonstrated by our lab, however several challenges remain, including low sensitivity, coarse spatial resolution, and limited spatial coverage. The contributions of this dissertation are (1) improving sensitivity, (2) exploring clinical applications, and (3) developing a new and advantageous labeling method for myocardial ASL.
Biography: Hung Phi Do is a Physics Ph.D. student working under the supervision of Prof. Nayak at the Magnetic Resonance Engineering Laboratory. His research focuses are MR physics and MR pulse sequence development for quantitative cardiovascular magnetic resonance. He received an M.S. in Electrical Engineering from the University of Southern California in 2014, a Diploma in Physics from the International Center for Theoretical Physics in 2009, and a B.S. in Physics from the Hanoi National University of Education in 2007.
Host: Prof. Krishna Nayak
Location: Hughes Aircraft Electrical Engineering Center (EEB) - PHE223
Audiences: Everyone Is Invited
Contact: Talyia White
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MHI Seminar Series - Visitor Program
Tue, Mar 21, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Magnús Már Halldórsson, Professor at Reykjavik University's School of Computer Science
Talk Title: Algorithms and Models for the Capacity of Arbitrary Wireless Networks
Abstract: At the heart of wireless network operation is the fundamental question of their capacity: How much communication can be achieved in a network, utilizing all the tools and diversity available: power control, scheduling, routing, channel assignment and rate adjustment?
The obvious aims of obtaining general purpose algorithms to solve this question run into two (walls) challenges:
- How to model communication and interference faithfully, and
- How to reason algorithmically in the more accurate models, which are also more intricate and harder to analyze.
We overview recent progress in developing algorithms for capacity and scheduling in the physical (or SINR) model with good performance guarantees on arbitrary networks. In particular, we indicate how many of the complications of the physical models can be abstracted away, at a small cost in performance. We also outline various efforts to add additional realism to the models, while maintaining generality and algorithmic tractability. We conclude with open questions and challenges.
This is based on joint work with Tigran Tonoyan
Biography: Prof. Magnús Már Halldórsson from Reyjkjavik University in Iceland will visit USC in late March 2017. He is a leading expert in algorithms for distributed computing and wireless networks. He has been the Chair of top conferences in the area including PODC 2014 and ICALP 2015. In 2017 he is leading the organization a Dagstuhl conference on "Foundations of Wireless Networking" together with Profs. C. Fragouli (UCLA), K. Jamieson (Princeton) and B. Krishnamachari (USC).
Host: Bhaskar Krishnamachari
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Cathy
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CS Colloquium: Nihar Shah (UC Berkeley) - Learning from People
Tue, Mar 21, 2017 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nihar Shah, UC Berkeley
Talk Title: Learning from People
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of "permutation-based" models that are considerably richer than classical models, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge, I present a class of "multiplicative" incentive mechanisms, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice, yielding several-fold improvements over prior art.
Biography: Nihar B. Shah is a PhD candidate in the EECS department at the University of California, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012, and the SVC Aiya Medal from the Indian Institute of Science for the best master's thesis in the department. His research interests include statistics and machine learning, with a current focus on applications to learning from people.
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 CommNetS
Wed, Mar 22, 2017 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Corey Baker, UC San Diego
Talk Title: When Disaster Strikes: Supplementing Centralized Infrastructure with Opportunistic Communication
Series: CommNetS
Abstract: Reliance on Internet connectivity is detrimental where modern networking technology is lacking, power outages are frequent, or network connectivity is sparse or non-existent (i.e., developing countries, natural disasters, and in-field military scenarios). Realization of the limitations resulting from reliance on Internet and cellular connectivity were prevalent in Hurricane Matthew (2016), which killed over 1000 people and destroyed cellular infrastructure. As an alternative, deploying resilient networking technology can facilitate the flow of information in resource-deprived environments to disseminate life saving data. In addition, leveraging opportunistic communication can supplement cellular networks to assist with keeping communication channels open during high-use and extreme situations. This talk will discuss the progress of a research platform and middleware that enables opportunistic communication and in vivo evaluation of delay tolerant routing schemes when the Internet is interrupted or unavailable by leveraging node relationships to create a delay tolerant social network. The solutions discussed in this talk further include applications related to IoT, mobile healthcare, and smart city environments.
Biography: Corey E. Baker, Ph.D., is a University of California President's Postdoctoral Fellow in the Electrical and Computer Engineering Department at the University of California, San Diego and is mentored by Professor Ramesh Rao. Dr. Baker's research interests are in the area of cyber physical systems specializing in opportunistic wireless communication for the Internet of Things (IoT), smart cities, smart homes, and mobile health environments. Particularly, Dr. Baker is interested in pragmatic applications and the fundamental issues related to real-world resource availability in today's operating systems for opportunistic wireless communication. Dr. Baker received a B.S. degree in Computer Engineering from San Jose State University, a M.S. in Electrical and Computer Engineering from California State University, Los Angeles, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Florida where he was advised by Professor Janise McNair. Corey has served on the board of directors of the National Society of Black Engineers (NSBE) numerous times as a two term National Treasurer and CFO, two term National Treasurer Emeritus, and as the Region 6 Chairperson. Dr. Baker is currently a NSBE Region 6 Finance Zone Advisor. Formerly, Dr. Baker was the official blogger for GEM and blogged about topics to promote success amongst STEM graduate students which included securing graduate school funding, navigating Ph.D. programs, and publishing.
Host: Prof. Ashutosh Nayyar
Location: 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Neuro-Gastroenterologic Engineering
Wed, Mar 22, 2017 @ 10:45 AM - 11:45 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Todd P. Coleman, Associate Professor/UCSD
Talk Title: Neuro-Gastroenterologic Engineering
Abstract: The discoordination between the central and autonomic nervous systems is increasingly being identified as playing a key role in affecting neurological, psychiatric, and gastroenterologic problems; the causal role that the enteric nervous system may play in Parkinson's disease serves as an example. However, traditionally, the brain and GI system have been studied scientifically and treated clinically, separately. There is a dearth of approaches to use engineering perspectives to better measure, characterize, and provide actionable insight about the GI system as well as its interplay with the brain. In this talk, we will discuss our recent contributions to address this unmet need. Specifically, we will discuss our recent development of novel methods to assess the GI system with high-resolution multi-electrode surface potential recordings, an approach that non-invasively characterizes propagation velocity and propagation patterns consistent with gastric serosal slow wave myoelectric activity, which had not been accomplished until now. We will also highlight novel applied probability methods to interpret these classes of dynamic multi-channel physiologic datasets, including directed information graphs, a new class of probabilistic graphical models that provides minimal descriptions of causal relationships in multiple time series. To enable the recording of multiple physiologic time series simultaneously and unobtrusively, we will lastly discuss our development of multi-electrode arrays embedded within skin-mounted adhesives for ambulatory monitoring. We will highlight how all of these methods and technologies are being used within the context of neuro-gastroenterologic engineering and how there is transformational potential to improve health, reduce healthcare costs, and advance science.
Biography: Todd P. Coleman received B.S. degrees in electrical engineering (summa cum laude), as well as computer engineering (summa cum laude) from the University of Michigan. He received M.S. and Ph.D. degrees from MIT in electrical engineering, and did postdoctoral studies at MIT in neuroscience. He is currently an Associate Professor in Bioengineering at UCSD, where he directs the Neural Interaction Laboratory. Dr. Coleman's research has been featured on CNN, BBC, and the New York Times. Dr. Coleman has been selected as a National Academy of Engineering Gilbreth Lecturer and a TEDMED speaker.
Host: Dr. Sandeep Gupta
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Seminar Announcement: Soft Robots for Delicate and Effective Interactions with Humans: Multi-Scale Soft Biomedical Robots
Wed, Mar 22, 2017 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Tomasso Ranzani, Postdoctoral Fellow, Harvard University
Talk Title: Soft Robots for Delicate and Effective Interactions with Humans: Multi-Scale Soft Biomedical Robots
Abstract: Soft robots are constructed from compliant materials, resulting in machines that can safely interact with the natural environments. Given their inherent compliance, they are particularly suitable for exploring and interacting with unstructured environments, and manipulating soft, delicate, and irregular objects. These properties make soft robots particularly promising for biomedical applications, such as wearable and medical devices, given the highly compliant and delicate structures of the body. On the other hand, the compliance of soft robots limits their ability to effectively apply forces on objects whose stiffness is comparable to the one of the robot itself, leading to the challenge of matching the compliance of soft devices with the environment or objects they will encounter. During this talk, I will describe progress in soft robotics and its potential for revolutionizing biomedical devices. I will introduce a soft manipulator inspired by the structure and the manipulation capabilities of the octopus tentacle, which is able to selectively tune its stiffness to address the challenge of impedance matching. I will also introduce the potential of soft robotics at the millimeter and micrometer scales, addressing the challenge of manufacturing complex meso-scale three-dimensional soft structures using two-dimensional processes involving laser machining, lamination, and soft lithography. These manufacturing processes could pave the way for soft microrobots as well as a new class of deployable, small, and safe medical devices.
Biography: Tommaso Ranzani received the Master's degree in biomedical engineering from the University of Pisa, Pisa, Italy, in 2010 and the Ph.D. degree in BioRobotics in 2014 at the BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy. During his Ph.D., he explored soft robotic technologies to develop a bioinspired manipulator, which integrates design principles from biological systems for performing advanced procedures in minimally invasive surgery. He is currently a postdoctoral fellow at the Harvard Microrobotics Laboratory and at the Harvard Biodesign Laboratory working on different manufacturing paradigms, materials, and actuation technologies to develop novel mm-scale robotic tools and structures able to overcome current challenges in medicine and surgery. His research interests include soft and bioinspired robotics, medical robotics and advanced manufacturing.
Host: Department of Aerospace and Mechanical Engineering
More Info: http://ame-www.usc.edu/seminars/3-22-17-ranzani.shtml
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
Event Link: http://ame-www.usc.edu/seminars/3-22-17-ranzani.shtml
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CS Colloquium: Long Lu (Stony Brook University) - New OS and Programming Support for Securing Mobile and IoT Platforms
Thu, Mar 23, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Long Lu, Stony Brook University
Talk Title: New OS and Programming Support for Securing Mobile and IoT Platforms
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Software running on mobile and IoT platforms increasingly falls victim to new attacks, which cause device compromises and privacy leaks that are often more severe than their counterparts on conventional computers. My research finds that new attacks on these platforms are possible primarily due to a gap between the evolving security needs of software and the legacy security support provided by operating systems and programming tools.
In this talk, I will first overview my recent works that aim to bridge this gap by rethinking the principles and designs of security mechanisms in operating systems, compilation toolchains, and TEEs (Trusted Execution Environments). I will then present two systems that address a critical yet previously unmet security need of today's apps, namely in-app isolation. The first system introduces a new OS-managed code execution unit, called shred, to compensate thread and process. A shred is a segment of a thread execution. Code inside a shred can access, in addition to the regular virtual memory, a private memory region. Using shreds, programmers can now protect sensitive in-memory code and data against untrusted code running in the same process or thread. The second system enables comprehensive security policy enforcement at the sub-app granularities, preventing mutually distrusting app modules from abusing each other's resources and privileges. In the final part of the talk, I will discuss my ongoing and future works on laying the system foundation for securing IoT platforms.
Biography: Long Lu is an Assistant Professor of Computer Science and the director of RiS3 Lab at Stony Brook University. Long's research spans the broad area of systems and software security. His recent work is focused on application and operating system security for emerging platforms, such as mobile and IoT/CPS devices. He designs code and data protection mechanisms, program analysis techniques, and user-facing software tools to prevent real attacks. He constantly publishes in the top-tier computer security conferences and is frequently invited to serve on their program committees. His research outcomes have been adopted by IBM, Microsoft, NEC, and Samsung. His work is currently funded by NSF, ONR, ARO, and AFRL. Long is a recipient of the NSF CAREER Award and the Air Force Faculty Fellowship. He holds a Ph.D. in Computer Science from Georgia Tech.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Matthew Brown (UCLA) -Typed Self-Applicable Meta-Programming
Thu, Mar 23, 2017 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matthew Brown, UCLA
Talk Title: Typed Self-Applicable Meta-Programming
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Meta-programming is a fundamental technique in computer science. It allows high levels of abstraction to be utilized with low cost. Meta-programs like compilers, interpreters, and program optimizers make high-level programming languages efficient, providing increased programmer productivity and performance comparable to lower-level languages. Self-applicable meta-programming makes meta-programming first-class, enabling many powerful
techniques. However, meta-programming and particularly self-applicable meta-programming is often complex, error-prone and difficult to debug. For these reasons it has untapped potential to provide benefits in many areas. Typed meta-programming uses modern techniques for type checking meta-programs to make them less error-prone and easier to understand and debug. It also brings the power of self-applicable meta-programming to statically-typed languages, ending a long-persisting trade-off between static and dynamic type checking. In this talk I discuss foundational results in typed self-applicable meta-programming.
Biography: Matt Brown is PhD candidate at UCLA, working in the compilers lab under Jens Palsberg. He holds a Bachelor's degree from UC Santa Cruz and a Master's from UCLA. His research focus is typed self-applicable meta-programming, which uses typed program representation techniques to ensure correctness properties of self-applicable meta-programs like self-interpreters. Other research interests include type systems, program verification, concurrency, and functional programming languages. He was recently a part-time lecturer at Loyola Marymount University and has six years of industry experience.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Civil and Environmental Engineering Seminar
Fri, Mar 24, 2017 @ 11:00 AM - 12:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Susan Hubbard, University of California, Berkeley
Talk Title: Geophysical Approaches for Quantifying Watershed Structure and Function
Host: Dr. Felipe de Barros
More Information: Hubbard Seminar Announcement.pdf
Location: 102
Audiences: Everyone Is Invited
Contact: Kaela Berry
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AI Seminar: EVOLUTION OF NEURAL NETWORKS
Fri, Mar 24, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Risto Miikkulainen, Univ. of Texas
Talk Title: EVOLUTION OF NEURAL NETWORKS
Series: Artificial Intelligence Seminar
Abstract: Evolution of artificial neural networks has recently emerged as a powerful technique both in deep networks and reinforcementlearning. While the performance of deep learning networks depends crucially on the network architecture; with neuroevolution, it ispossible to discover such architectures automatically. While reinforcement learning works well when the environment is fully observable, neuroevolution makes it possible to disambiguate hidden state through memory. In this tutorial, I will review 1 neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, 2 ways of combining gradient-based training with evolutionary methods, and 3 applications of neuroevolution to control, robotics, artificial life, and games.
Biography: Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a Research Fellow at Sentient Technologies, Inc. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing, and self-organization of the visual cortex; he is an author of over 370 articles in these research areas. He is an IEEE Fellow, member of the Board of Governors of the Neural Network Society, and an action editor of Cognitive Systems Research and IEEE Transactions on Computational Intelligence and AI in Games.
Host: Mayank Kejriwal
Webcast: http://webcastermshd.isi.edu/Mediasite/Play/f0024e2d2140457586ec2ed6a78026b01dLocation: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/f0024e2d2140457586ec2ed6a78026b01d
Audiences: Everyone Is Invited
Contact: Peter Zamar
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Seminars in Biomedical Engineering
Fri, Mar 24, 2017 @ 02:30 PM - 04:30 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Aditya Rajagopal, PhD, Founder & CTO, Chromacode; Visiting Associate (Visiting Faculty), Caltech
Talk Title: Engineering Methods for Biological Measurement
Series: Seminars in BME (Lab Rotations)
Host: Brent Liu, PhD
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Mar 24, 2017 @ 02:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Aydin Babakhani, Assistant Professor, Rice University
Talk Title: Silicon-based Integrated Sensors and Systems with On-chip Antennas From Picosecond Pulse Radiators to Miniaturized Spectrometers
Host: Profs. Hossein Hashemi, Mike Chen, Dina El-Damak, and Mahta Moghaddam
More Information: MHI Seminar Series IS - Aydin Babakhani.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
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NL Seminar - Intuitive Interactions with Black-box Machine Learning
Fri, Mar 24, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sameer Singh, UCI
Talk Title: Intuitive Interactions with Black-box Machine Learning
Series: Natural Language Seminar
Abstract: Machine learning is at the forefront of many recent advances in natural language processing, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned. It is incredibly difficult to understand, predict, or "fix" the behavior of NLP models that have been deployed. In this talk, I propose interpretable representations that allow users and machine learning models to interact with each other: enabling machine learning models to provided explanations as to why a specific prediction was made and enabling users to inject domain knowledge into machine learning. The first part of the talk introduces an approach to estimate local, interpretable explanations for black-box classifiers and describes an approach to summarize the behavior of the classifier by selecting which explanations to show to the user. I will also briefly describe work on "closing the loop", i.e. allowing users to provide feedback on the explanations to improve the model, for the task of relation extraction, an important subtask of natural language processing. In particular, we introduce approaches to both explain the relation extractor using logical statements and to inject symbolic domain knowledge into relational embeddings to improve the predictions. I present experiments to demonstrate that an interactive interface is effective in providing users an understanding of, and an ability to improve, complex black-box machine learning systems.
Biography: Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interactive machine learning applied to information extraction and natural language processing. Till recently, Sameer was a Postdoctoral Research Associate at the University of Washington. He received his PhD from the University of Massachusetts, Amherst in 2014, during which he also interned at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was selected as a DARPA Riser, was awarded the Adobe Research Data Science Award, won the grand prize in the Yelp dataset challenge, has been awarded the Yahoo! Key Scientific Challenges fellowship, and was a finalist for the Facebook PhD fellowship. Sameer has published more than 30 peer-reviewed papers at top-tier machine learning and natural language processing conferences and workshops.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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CS Colloquium: David Held (UC Berkeley) - Robots in Clutter: Learning to Understand Environmental Changes
Mon, Mar 27, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Held, UC Berkeley
Talk Title: Robots in Clutter: Learning to Understand Environmental Changes
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Robots today are confined to operate in relatively simple, controlled environments. One reason for this is that current methods for processing visual data tend to break down when faced with occlusions, viewpoint changes, poor lighting, and other challenging but common situations that occur when robots are placed in the real world. I will show that we can train robots to handle these variations by modeling the causes behind visual appearance changes. If robots can learn how the world changes over time, they can be robust to the types of changes that objects often undergo. I demonstrate this idea in the context of autonomous driving, and I will show how we can use this idea to improve performance for every step of the robotic perception pipeline: object segmentation, tracking, and velocity estimation. I will also present some recent work on learning to manipulate objects, using a similar framework of learning environmental changes. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives.
Biography: David Held is a post-doctoral researcher at U.C. Berkeley working with Pieter Abbeel on deep reinforcement learning for robotics. He recently completed his Ph.D. in Computer Science at Stanford University with Sebastian Thrun and Silvio Savarese, where he developed methods for perception for autonomous vehicles. David has also worked as an intern on Google's self-driving car team. Before Stanford, David was a researcher at the Weizmann Institute, where he worked on building a robotic octopus. He received a B.S. and M.S. in Mechanical Engineering at MIT and an M.S. in Computer Science at Stanford, for which he was awarded the Best Master's Thesis Award from the Computer Science Department.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Mon, Mar 27, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Jean-Michel Maarek, PhD, Professor of Engineering Practice, USC Biomedical Engineering
Talk Title: Flexible Devices
Host: Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Mon, Mar 27, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Massimo Franceschetti, Professor, University of California San Diego
Talk Title: The value of information in event triggering: can we beat the data-rate theorem?
Abstract: In networked control, data-rate theorems relate the amount of information that the feedback channel between estimator and controller must be able to supply to guarantee stability, to the amount of information requested by the plant. They represent a cornerstone of the theory of cyber-physical systems (CPS) and have been studied for more than a decade. On the other hand, the need to use distributed resources efficiently in CPS has led to event-triggering control techniques based on the idea of sending information in an opportunistic manner between the controller and the plant. After reviewing the basics of the data rate theorems, we illustrate how these are to be modified in the presence of an event-triggered implementation. The main observation is that the act of triggering reveals information about the system's state and can be exploited for stabilization, thus effectively invalidating "classic" formulations of the theorem. An extended formulation reveals a phase transition behavior of the transmission rate required for stabilization as a function of the communication delay. It is shown that for low values of the delay the timing information carried by the triggering events is large and the system can be stabilized with any positive rate. On the other hand, when the delay exceeds a certain threshold that depends on the given triggering strategy, the timing information alone is not enough to achieve stabilization and the rate must begin to grow, eventually becoming larger than what required by the classic data-rate theorem. The critical point where the transmission rate equals the one imposed by the data-rate theorem occurs when the delay equals the inverse of the entropy rate of the plant, representing the intrinsic rate at which the system generates information. At this critical point, the timing information supplied by event triggering is completely balanced by the information loss due to the communication delay.
Biography: Massimo Franceschetti received the Laurea degree (with highest honors) in computer engineering from the University of Naples, Naples, Italy, in 1997, the M.S. and Ph.D. degrees in electrical engineering from the California Institute of Technology, Pasadena, CA, in 1999, and 2003, respectively. He is Professor of Electrical and Computer Engineering at the University of California at San Diego (UCSD). Before joining UCSD, he was a postdoctoral scholar at the University of California at Berkeley for two years. He has held visiting positions at the Vrije Universiteit Amsterdam, the Ecole Polytechnique Federale de Lausanne, and the University of Trento. His research interests are in physical and information-based foundations of communication and control systems. He is co-author of the book "Random Networks for Communication" published by Cambridge University Press. Dr. Franceschetti served as Associate Editor for the IEEE Transactions on Information Theory (2009-2012) and for the IEEE Transactions on Control of Network Systems (2013-16) and as Guest Associate Editor of the IEEE Journal on Selected Areas in Communications (2008, 2009). He is currently serving as Associate Editor of the IEEE Transactions on Network Science and Engineering. He was awarded the C. H. Wilts Prize in 2003 for best doctoral thesis in electrical engineering at Caltech, the S.A. Schelkunoff Award in 2005 for best paper in the IEEE Transactions on Antennas and Propagation, a National Science Foundation (NSF) CAREER award in 2006, an Office of Naval Research (ONR) Young Investigator Award in 2007, the IEEE Communications Society Best Tutorial Paper Award in 2010, and the IEEE Control theory society Ruberti young researcher award in 2012.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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NL Seminar - ANALYZING THE LANGUAGE OF FOOD ON SOCIAL MEDIA
Mon, Mar 27, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Stephen Kobourov , University of Arizona
Talk Title: ANALYZING THE LANGUAGE OF FOOD ON SOCIAL MEDIA
Series: Natural Language Seminar
Abstract: We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have most predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo referenced heatmaps, semantics-preserving wordclouds and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.
Biography: Stephen Kobourov is a Professor of Computer Science at the University of Arizona. He completed BS degrees in Mathematics and Computer Science at Dartmouth College in 1995, and a PhD in Computer Science at Johns Hopkins University in 2000. He has worked as a Research Scientist at AT&T Research Labs, a Hulmboldt Fellow at the University of Tubingen in Germany, and a Distinguished Fulbright Chair at Charles University in Prague.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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USC Stem Cell Seminar: Hongkui Deng, Peking University
Tue, Mar 28, 2017 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Hongkui Deng, Peking University
Talk Title: TBD
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Host: USC Stem Cell
More Info: http://stemcell.usc.edu/events
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
Event Link: http://stemcell.usc.edu/events
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CS Colloquium: Yeongjin Jang (Georgia Tech) - Protecting Computing System Interactions
Tue, Mar 28, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yeongjin Jang, Georgia Tech
Talk Title: Protecting Computing System Interactions
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Computing platforms are evolving from desktops to Smartphones to the Internet of things (IoT) devices. In this change, computer systems have started embedding an amazing variety of interaction points in both software and hardware forms. While such changes have made everyday life easier by enabling various convenient features, protecting these systems has become much more difficult. This is not only because system complexity has increased with the integration of more interactions and often conflicts with the existing security mechanisms, but also because improper security practices or incomplete security checks result from faster production cycles that generally lead to more security holes.
In this talk, Yeongjin will present his research on protection of computing system interactions. First, he will present Gyrus, a user interaction monitoring system that reflects user's intention to network traffic monitoring. Gyrus can protect user-to-network interactions such as sending message online and online banking. Next, he will present a result of security analysis on user I/O in operating systems,
in which he discovered computer accessibility as a new attack vector. The analysis found 12 new attacks in popular operating systems, and he discusses countermeasures against the vulnerabilities to keep the affected systems secure.
Biography: Yeongjin Jang is a Ph.D. candidate in Computer Science at the Georgia Institute of Technology. His research focuses on security and privacy problems of computing systems, which include operating systems, mobile systems, and computing hardware.
His research results are recognized for their highly practical impact, as noted by one award and two nominations for the CSAW best applied research paper. Moreover, his research has been widely covered in popular media including Forbes, Wired, MIT Technology Review, and more.
Yeongjin received his M.S. from Georgia Tech in 2016 and B.S. from KAIST in 2010.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Seminar, ISE 651
Tue, Mar 28, 2017 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ilan Adler, Professor, UC Berkeley
Talk Title: Incentive Compatible Mechanisms for the Secretary Problem
Host: Prof. Sheldon Ross
More Information: March 28, 2017_Adler.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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MHI CommNetS seminar
Wed, Mar 29, 2017 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Cédric Josz, Laboratory for Analysis and Architecture (LAAS CNRS)
Talk Title: Application of Polynomial Optimization to Electricity Transmission Networks
Series: CommNetS
Abstract: Multivariate polynomial optimization where variables and data are complex numbers is a non-deterministic polynomial-time hard problem that arises in various applications such as electric power systems, signal processing, imaging science, automatic control, and quantum mechanics. Complex numbers are typically used to model oscillatory phenomena which are omnipresent in physical systems. We propose a complex moment/sum-of-squares hierarchy of semidefinite programs to find global solutions with reduced computational burden compared with the Lasserre hierarchy for real polynomial optimization. We apply the approach to large-scale sections of the European high-voltage electricity transmission grid. Thanks to an algorithm for exploiting sparsity, instances with several thousand variables and constraints can be solved to global optimality.
Biography: Cédric Josz is currently pursuing a postdoctoral project under the supervision of Jean Bernard Lasserre in the Laboratory for Analysis and Architecture (LAAS CNRS) in Toulouse, France. His work is funded by a European Research Council Advanced Grant and deals with non-convexity in optimization. He received a PhD in applied mathematics from the University of Paris VI in 2016 in collaboration with the French transmission system operator (Rte) and the French Institute for Research in Computer Science and Automation (INRIA).
Host: Prof. Rahul Jain
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Amgen Seminar: Chawita (Jelly) Netirojjanakul
Wed, Mar 29, 2017 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Chawita (Jelly) Netirojjanakul, Amgen
Talk Title: Structure guided engineering of antibody-small molecule hybrids
Series: USC/Amgen Seminar Series
Host: USC/Amgen
More Info: http://stemcell.usc.edu/events
Location: TBD
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: http://stemcell.usc.edu/events
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MHI CommNetS seminar
Wed, Mar 29, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Scott Moura, UC Berkeley
Talk Title: Increasing Battery Potential: Estimation & Control of Electrochemical Models
Series: CommNetS
Abstract: Batteries are ubiquitous. However, today's batteries are expensive, range-limited, power-restricted, die too quickly, and charge too slowly. Batteries are conservatively operated because their control systems treat the internal electrochemical dynamics as a black-box. Given real-time estimates of the electrochemical states, however, one can safely operate batteries near their physical limits, thus significantly enhancing performance beyond current state-of-art battery management systems. This talk reviews recent advancements in enhanced battery performance via estimation and control of PDE electrochemical models.
First, we review battery electrochemistry. Second, we discuss canonical state-of-charge (SOC), state-of-health (SOH), and other so-called SOx estimation algorithms. Third, we present recent theoretical results in state estimation and optimal control with PDE models. Finally, we close with exciting new opportunities for next-generation battery management systems.
Biography: Scott Moura is an Assistant Professor at the University of California, Berkeley in Civil & Environmental Engineering and Director of eCAL. He received the Ph.D. degree from the University of Michigan in 2011, the M.S. degree from the University of Michigan in 2008, and the B.S. degree from the UC Berkeley, in 2006 - all in Mechanical Engineering. He was a postdoctoral scholar at UC San Diego in the Cymer Center for Control Systems and Dynamics, and a visiting researcher in the Centre Automatique et Systemes at MINES ParisTech in Paris, France. He is a recipient of the O. Hugo Shuck Best Paper Award, Carol D. Soc Distinguished Graduate Student Mentoring Award, Hellman Faculty Fellows Award, UC Presidential Postdoctoral Fellowship, National Science Foundation Graduate Research Fellowship, University of Michigan Distinguished ProQuest Dissertation Honorable Mention, University of Michigan Rackham Merit Fellowship, and Distinguished Leadership Award. He has received multiple conference best paper awards, as an advisor & student. His research interests include control & estimation theory for PDEs, optimization, machine learning, batteries, electric vehicles, and the smart grid.
Host: Prof. Insoon Yang
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Gianluca Lazzi - Thursday, March 30th at 10:30am in EEB 132
Thu, Mar 30, 2017 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Gianluca Lazzi, Dept. of Electrical and Computer Engineering, The University of Utah
Talk Title: Bioelectromagnetics for Neuroimplants: from Wireless Power and Data Transfer to Direct Neurostimulation
Abstract: During the past decade, we have witnessed remarkable progress in neural implants, and more generally in the development of systems that interface with the human body for recording neural activity or vital signs or stimulating the neural system. The challenges toward the development of true biomimetic systems are daunting: nonetheless, electrical or magnetic systems that can partially restore neural functions or offer therapeutic solutions have recently shown tremendous progress and potential. Prospects for electroneural interfaces to further evolve and offer a viable solution to various disorders are high.
In this talk, we will utilize examples of electric and magnetic neurostimulators, such as the artificial retina to restore partial vision to the blind, cortical neurostimulators, and magnetic peripheral neurostimulators, to introduce advances in computational bioelectromagnetics and physical neurointerfaces that enabled the progress of neurostimulating and neurorecording systems, with particular emphasis on coil-based systems for wireless power and data transfer, direct magnetic neurostimulation, multiscale computational models and methods, and liquid metal based stretchable systems.
Biography: Gianluca Lazzi, PhD, MBA, is a USTAR Professor and the Chair of the Department of Electrical and Computer Engineering (ECE) at the University of Utah.
Gianluca is a Fellow of the IEEE and a Fellow of the AIMBE. He has received numerous awards for his work, including the IEEE Wheeler Award, a R&D100 Award, a URSI Young Scientist Award, the BEMS "Curtis Carl Johnson Award," a NSF CAREER Award and a Whitaker Foundation Young Investigator Award. His research interests are in the fields of bioelectromagnetics, liquid metal electronics, antennas, wireless electromagnetics, and electric and magnetic neurostimulation. He has published over 200 papers in journals, conference proceedings, and books. Gianluca's research work has been featured in publications such as Forbes, the Economist, MSNBC, MIT Technology Review, and several others. Gianluca has been the Editor-in-Chief (EiC) from 2008 to 2013 of one of the leading journals in the field of antennas and propagation, IEEE Antennas and Wireless Propagation Letters (AWPL), which reached nearly 2,000 submissions in 2013 during his tenure - a growth of 400% in submissions. He serves or served IEEE in numerous other roles, including being the Chair of the IEEE Sensors Technical Achievement Award Committee (2011-2012), Chair of the IEEE Sensors Council Fellow Committee (2013-2015), Chair of Publications of the IEEE Antennas and Propagation Society (2013-Present), a member of AdCom of the IEEE Antennas and Propagation Society (2014-Present), and a member of the Editorial Board of the Proceedings of the IEEE (2011-Present). He was one of the speakers at a recent Grand Challenges in Life Science Symposium, held at the National Academies, which resulted in the position paper "Grand Challenges in Interfacing Engineering With Life Sciences and Medicine" published in IEEE TBME. He was the General Co-Chair of the 2014 IEEE Microwave Symposium on RF and Wireless Technologies for Biomedical Applications (London, UK).
In 2015, Dr. Lazzi cofounded the company Bend LLC with a private equity firm. Bend LLC is focused on the commercialization of liquid metal technology for sensor integration in athletic apparel and consumer electronics.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Kevin Jamieson (UC Berkeley) - Efficient scalable algorithms for adaptive data collection
Thu, Mar 30, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Kevin Jamieson, UC Berkeley
Talk Title: Efficient scalable algorithms for adaptive data collection
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
In many applications, data-driven discovery is limited by the rate of data collection: the skilled labor it takes to operate a pipette, the time to execute a long-running physics simulation, the patience of an infant to remain still in an MRI, or the cost of labeling large corpuses of complex images. A powerful paradigm to extract the most information with such limited resources is active learning, or adaptive data collection, which leverages already-collected data to guide future measurements in a closed loop. But being convinced that data-collection should be adaptive is not the same thing as knowing how to adapt in a way that is both sample efficient and reliable. In this talk, I will present several examples of my provably reliable -- and practical -- adaptive data collection algorithms being applied in the real-world. In particular, I will show how my adaptive algorithms are used each week to crowd-source the winner of the New Yorker Magazine Cartoon Caption Contest. I will also discuss my application of adaptive learning concepts at Google to accelerate the tuning of deep networks in a highly parallelized environment of thousands of GPUs.
Biography: Kevin Jamieson is a postdoctoral researcher working with Professor Benjamin Recht in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is interested in the theory and practice of machine learning algorithms that sequentially collect data using an adaptive strategy. This includes active learning, multi-armed bandit problems, and stochastic optimization. Kevin received his Ph.D. from the University of Wisconsin - Madison under the advisement of Robert Nowak. Prior to his doctoral work, Kevin received his B.S. from the University of Washington, and an M.S. from Columbia University, both in electrical engineering.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Jyotirmoy V. Deshmukh (Toyota Technical Center) -Ninja Temporal Logic: Making formal methods relevant in engineering practice
Thu, Mar 30, 2017 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jyotirmoy V. Deshmukh, Toyota Technical Center
Talk Title: Ninja Temporal Logic: Making formal methods relevant in engineering practice
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
The software that controls the operation of critical systems such as vehicles, medical devices, buildings, and transportation infrastructures is getting smarter due to the increased demands for autonomy. The push for increased automation is a worthy goal, but can we do so without compromising the safety and reliability of such systems?
Furthermore, can formal methods truly improve a design engineer's productivity? In this talk, I will introduce some of the most important questions facing academic and industrial development of software for the cyber-physical systems of tomorrow. We will consider solutions based on the use of formal logics, that, on one hand allow rigorous reasoning about system designs, while on the other, do not place an undue burden on the engineer. In particular, I will explain how formal requirements using real-time temporal logics have had an impact in the development of cutting-edge alternate-energy vehicles and advanced control problems within Toyota. I will guide the audience through an ecosystem built around temporal logic that permits automatic testing, efficient monitoring, requirement engineering and controller synthesis for highly complex automotive systems. The talk covers topics from what I consider the trifecta for designing reliable cyber-physical systems: formal logic, machine learning, and control theory, and will lay out my vision for future research and open problems within this domain.
Biography: Jyotirmoy V. Deshmukh is a Principal Engineer at Toyota R&D. He received his Ph.D. from the University of Texas at Austin under the supervision of E. Allen Emerson on topics including tree automata, verifying data structure libraries, static analysis for concurrent programs and program repair. He worked as a post-doctoral researcher at the University of Pennsylvania with Rajeev Alur's research group, investigating theoretical models of streaming computation and program synthesis techniques. For the last five years at Toyota, Jyo's research has focused on the design and analysis of industrial cyber-physical systems. Drawing on areas such as hybrid systems, real-time temporal logics, control theory, machine learning and dynamical systems theory, Jyo has been attempting to bridge the gap between academic research and its applicability to industrial-scale systems.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Munushian Speaker - Mark Horowitz, Friday, March 31st at 2:00pm in EEB 132
Fri, Mar 31, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mark Horowitz, Yahoo! Founders Professor at Stanford University
Talk Title: Innovation in a Post-Moore's Law World
Abstract: For the past half century the world has enjoyed the benefits of many innovations enabled by Moore's Law scaling of silicon technology. While Intel claims that scaling is still healthy, most other organization see issues today, and many more issues ahead. Regardless of whether it has started to happen already, it will eventually stop, and that point is that that far away.
This talk will quickly review the basics behind silicon scaling, the current power problem, and current approaches to continue Moore's Law after scaling slows (think 3-D and new technologies). I will then describe why I am not optimistic about any of the new technologies rescuing Moore's Law (though there has been some interesting progress on the quantum side), and why I think that computing will be CMOS based for the foreseeable future. The net effect, which already exists today, is that the value of electronic technology has moved from being technology driven to be application driven. In an application driven world, successful products include many "cupholders", small low cost additions that improve the user experience, so enabling them is essential.
The rest of the talk is my view of how the design process and the industry must adapt if it wants to continue to create high-value products. In application driven value scenarios, the technologies that win are those that have low development costs, since most ideas fail. This has profound ramifications for both how we design chips, and how we design systems using chips. In both areas we need to enable people to try to create new innovative hardware solutions and to do that requires create enough design scaffolding to enable the equivalents of Apple's IStore/Google Play for hardware design.
Biography: Mark Horowitz is the Yahoo! Founders Professor at Stanford University and was chair of the Electrical Engineering Department from 2008 to 2012. He co-founded Rambus, Inc. in 1990 and is a fellow of the IEEE and the ACM and a member of the National Academy of Engineering and the American Academy of Arts and Science. Dr. Horowitz's research interests are quite broad and span using EE and CS analysis methods to problems in molecular biology to creating new design methodologies for analog and digital VLSI circuits.
Host: EE-Electrophysics
More Info: minghsiehee.usc.edu/about/lectures
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: minghsiehee.usc.edu/about/lectures
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Seminars in Biomedical Engineering
Fri, Mar 31, 2017 @ 02:30 PM - 04:30 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Shankar Subramaniam, PhD, Professor, Departments of Bioengineering, Chemistry and Biochemistry, Cellular and Molecular Medicine, and Nano Engineering University of California, San Diego
Talk Title: TBA
Series: Seminars in BME (Lab Rotations)
Biography: Shankar Subramaniam is a Professor of Bioengineering, Chemistry and Biochemistry, Cellular and Molecular Medicine and Nano Engineering. He is currently the Chair of the Bioengineering Department at the University of California at San Diego. He holds the inaugural Joan and Irwin Jacobs Endowed Chair in Bioengineering and Systems Biology. He was the Founding Director of the Bioinformatics Graduate Program at the University of California at San Diego. He also has adjunct Professorships at the Salk Institute for Biological Studies and the San Diego Supercomputer Center. He is also a Guest Professor at the Center for Molecular Biology and Neuroscience at the University of Oslo in Norway and Professor at the Center for Cardiovascular Bioinformatics and Modeling at Johns Hopkins University. Prior to moving to UC San Diego, Dr. Subramaniam was a Professor of Biophysics, Biochemistry, Molecular and Integrative Physiology, Chemical Engineering and Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). He was the Director of the Bioinformatics and Computational Biology Program at the National Center for Supercomputing Applications and the Co-Director of the W.M. Keck Center for Comparative and Functional Genomics at UIUC. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE) and is a recipient of Smithsonian Foundation and Association of Laboratory Automation Awards and his research work is described below. In 2002 he received the Genome Technology All Star Award. In 2008 he was awarded the Faculty Excellence in Research Award at the University of California at San Diego. His research spans several areas of bioinformatics and systems biology.
Dr. Subramaniam has played a key role in raising national awareness for training and research in bioinformatics. He served as a member of the National Institute for Health (NIH) Director's Advisory Committee on Bioinformatics, which resulted in the Biomedical Information Science and Technology Initiative (BISTI) report. The report recognized the dire need for trained professionals in Bioinformatics and recommended the launching of a strong NIH funding initiative. Dr. Subramaniam served as the Chair of a NIH BISTI Study Section. Dr. Subramaniam has also served on Bioinformatics and Biotechnology Advisory Councils for Virginia Tech, the University of Illinois at Chicago, and on the Scientific Advisory Board of several Biotech and Bioinformatics Companies. Dr. Subramaniam has served as a member of the State of Illinois Governor's initiative in Biotechnology and an advisor and reviewer of the State of North Carolina initiative in Biotechnology. He is currently an overseas advisor for the Department of Biotechnology of the Government of India, and a member of a European Science Foundation Panel.
Host: Stacey Finley, PhD
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Astani Civil and Environmental Engineering Ph.D. Seminar
Fri, Mar 31, 2017 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Christopher Higgins, Colorado School of Mines
Talk Title: Passive technologies for treatment of urban stormwater: Biochar-amemnded bioinfiltration systems and biohydrochemically enhanced stream-water treatment
Abstract: Despite substantial water quality challenges associated with urban stormwater, stormwater managers typically prioritize storm flow reduction rather than pollutant removal. Common stormwater pollutants of concern can vary greatly by region, but often include nutrients, metals, pathogens, and trace organic chemicals. In this seminar, two novel technologies for removal of chemical contaminants from stormwater will be discussed. Bioinfiltration systems have shown potential to afford dual benefits of preventing contamination of urban receiving waters while augmenting urban water storage. The addition of a black carbon sorbent (biochar) to these systems may be especially effective for enhanced removal of traceorganic chemicals (TOrCs). Efforts to calibrate and verify a forward model for intraparticle diffusion-limited TOrC transport will be discussed, as well as the potential for transformation product generation in these systems. Further, a novel approach for treating stormwater while conveying it will be presented. This approach, termed Biohydrochemical Enhancement structures for Streamwater Treatment, BEST, relies on subsurface modifications to streambed hydraulic conductivity to drive efficient hyporheic exchange. When coupled with subsurface geomedia enhancements, BEST modules show significant promise for treating urban stormwater contaminants with minimal impacts to other stream functions. Together, these passive technologies suggest that the enhancement of natural processes in urban water infrastructure may have significant benefits to urban water quality.
Biography: Dr. Christopher P. Higgins is an Associate Professor in the Department of Civil and Environmental Engineering at the Colorado School of Mines. Dr. Higgins earned his PhD and MS from Stanford in Civil and Environmental Engineering, and his BA from Harvard in Chemistry and Chemical Biology. Before joining the faculty of CSM in 2009, he completed a postdoctoral appointment at Johns Hopkins. His research focuses on the movement of contaminants in the environment. In particular, he studies chemical fate and transport in natural and engineered systems as well as bioaccumulation in plants and animals. Contaminants under study in his laboratory include poly- and perfluoroalkyl substances used in stain-repellent fabrics and firefighting foams, nanoparticles, wastewater-derived pharmaceuticals and personal care products, trace organic chemicals in urban stormwater, and trace metals.
Host: Dr. Daniel McCurry
Location: John Stauffer Science Lecture Hall (SLH) - 102
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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NL Seminar Towards the Machine Comprehension of Text
Fri, Mar 31, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Danqi Chen, Stanford Univ.
Talk Title: Towards the Machine Comprehension of Text
Series: Natural Language Seminar
Abstract: In this talk, I will first present how we advance this line of research. I will show how simple models can achieve nearly state of the art performance on recent benchmarks, including the CNN Daily Mail datasets and the Stanford Question Answering Dataset. I will focus on explaining the logical structure behind these neural architectures and discussing advantage as well as limits of current approaches. Lastly I will talk about our recent work on scaling up machine comprehension systems, which attempt to answer open domain questions at the full Wikipedia scale. We demonstrate the promise of our system, as well as set up new benchmarks by evaluating on multiple existing QA datasets.
Biography: Danqi Chen is a PhD candidate in Computer Science at Stanford University, advised by Professor Christopher Manning. Her main research interests lie in deep learning for natural language processing and understanding, and she is particularly interested in the intersection between text understanding and knowledge reasoning. She has been working on machine comprehension, question answering, knowledge base population and dependency parsing. She is a recipient of Facebook fellowship and Microsoft Research Womens Fellowship and an outstanding paper award in ACL 16. Prior to Stanford, she received her BS from Tsinghua University.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
