Select a calendar:
Filter March Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for March
-
Taylor Berg-Kirkpatrick (Carnegie Mellon University) – Balancing Constraint and Flexibility in Unsupervised Models for Language Analysis
Thu, Mar 01, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Taylor Berg-Kirkpatrick, Carnegie Mellon University
Talk Title: Balancing Constraint and Flexibility in Unsupervised Models for Language Analysis
Series: Computer Science Colloquium
Abstract: Without careful consideration of the relationship between input and output, unsupervised learning problems can be under-constrained. This talk will discuss approaches for making unsupervised problems feasible by incorporating different types of inductive bias. First, we focus on a set of raw data analysis tasks related to the digital humanities, including historical document recognition, music transcription, and compositor attribution. For each of these tasks, strong prior knowledge about the causal process behind the data can be encoded into the model. We show how to leverage this casual knowledge as a helpful source of constraint, yielding systems that in some cases outperform their supervised counterparts. Next, we investigate several linguistic analysis tasks where causal structure is more difficult to encode. Here, we develop a new unsupervised model class that combines structured and continuous representations by leveraging the flexibility of neural networks. We show that incorporating a volume-preserving constraint on the neural component of our model makes learning well-behaved. Using this approach, we demonstrate start-of-the-art results on two standard unsupervised NLP tasks: part-of-speech induction and unsupervised dependency parsing.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.
Biography: Taylor Berg-Kirkpatrick joined the Language Technologies Institute at Carnegie Mellon University as an Assistant Professor in Fall 2016. Previously, he was a Research Scientist at Semantic Machines Inc. and, before that, completed his Ph.D. in computer science at the University of California, Berkeley. Taylor's research focuses on using machine learning to understand structured human data, including language but also sources like music, document images, and other complex artifacts.
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
Borrowing from Nature to Build Better Computers: DNA Data Storage and Near-Molecule Processing
Fri, Mar 02, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Luis Ceze, University of Washington
Talk Title: Borrowing from Nature to Build Better Computers: DNA Data Storage and Near-Molecule Processing
Abstract: DNA data storage is an attractive option for digital datastorage because of its extreme density, durability and eternal relevance. This is especially attractive when contrasted with the exponential growth in world-wide digital data production. In this talk, we will present our efforts in building an end-to-end system, from the computational component of encoding and decoding to the molecular biology component of random access, sequencing and fluidics automation. We will also discuss some early efforts in building a hybrid electronic/molecular computer system that has the potential to offer more than just data storage.
Biography: Luis Ceze is a Professor of Computer Science and Engineering at the University of Washington. His research focuses on the intersection between computer architecture, programming languages and biology. His current focus is on approximate computing and DNA-based data storage. He has co-authored over 100 papers in these areas, and had several papers selected as IEEE Micro Top Picks and CACM Research Highlights. His research has been featured prominently in the media including NewYork Times, Popular Science, MIT Technology Review, Wall Street Journal, among others. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship,the IEEE TCCA Young Computer Architect Award and UIUC Distinguished Alumni Award. He is a member of the DARPA ISAT and MEC study groups, and consults for Microsoft.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
-
W.V.T. RUSCH ENGINEERING HONORS COLLOQUIUM
Fri, Mar 02, 2018 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Andrea Armani, Mork Family Department of Chemical Engineering and Materials Science, USC
Talk Title: Field Trip to USC Michelson Hall Laboratories
Host: Dr. Prata & EHP
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Su Stevens
-
EE-EP Faculty Candidate - Deblina Sarkar, Friday, March 2nd at 2pm in EEB 132
Fri, Mar 02, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Deblina Sarkar, MIT
Talk Title: Green Electronics to Gray Matter: Ghost Walks, Mind Blowing and Brain Doping
Abstract: Excessive power consumption and dissipation of electronics with technology scaling, is a serious threat to the Information Society as well as to the environment and especially smacks a hard blow to the future of energy-constrained applications such as medical implants and prosthetics. This impending energy crisis has roots in the thermal distribution of carriers, which poses fundamental limitation on energy scalability of the present transistors.
In this talk, I will demonstrate the quantum mechanical transistor, that I developed, which beats the fundamental thermal limitations of present transistors. I will describe how this can be achieved by unique integration of heterogeneous material technologies including an atomically thin material, to make the electron waves propagate (tunnel) efficiently through an energy barrier (like a ghost walking through a wall). This device is the world's thinnest channel (6 atoms thick) sub-thermal tunnel-transistor. Thus, it has the potential to allow dimensional scalability to beyond Silicon scaling era and thereby to address the long-standing issue of simultaneous dimensional and power scalability.
Going beyond electronic computation, I will discuss about the biological computer: the brain, which can be thought of as an ultimate example of low power computational system. However, understanding the brain, requires deciphering the dense jungle of biomolecules that it is formed of. I will introduce the next-generation expansion microscopy technology, that I have developed, which helps to decipher the organization of biomolecular building blocks of brain by literally blowing out the brain by up to 100-fold. This technology reveals for the first time, a nanoscale trans-synaptic architecture in brain tissue and structural changes related to neurological diseases.
I will conclude with my research vision for how extremely powerful technologies can be built by fusing diverse research fields and how seamless integration of nanoelectronics-bio hybrid systems in the brain (brain doping), can create unprecedented possibilities for probing and controlling the biological computer and in future, help us transcend beyond our biological limitations.
[1] D. Sarkar et. al., Nature, 526 (7571), 91, 2015;
[2] D. Sarkar et. al., Nano Lett., 15 (5), 2852, 2015;
[3] D. Sarkar et. al., ACS Nano., 8 (4), 3992, 2014;
[4] D. Sarkar et. al., Society for Neuroscience, 2016.
[5] D. Sarkar et. al., International Conference on Nanoscopy, 2018.
Biography: Deblina Sarkar is currently an MIT Translational Fellow and postdoctoral associate in the Synthetic Neurobiology group, while she had received her M.S. and Ph.D. in Electrical and Computer Engineering at UCSB. Her research aims to combine novel materials, nanoelectronics and synthetic biology to create a new paradigm for computational electronics and invent disruptive technologies for life-machine symbiosis.
Her work has led to more than 40 publications till date (citations: 1927, h-index: 18, i-10 index: 26 according to Google Scholar), several of which have appeared in popular press worldwide. Her PhD dissertation was honored as one of the top 3 dissertations throughout USA and Canada in the field of Mathematics, Physical sciences and all departments of Engineering by the Council of Graduate Schools in the period 2014-2016. She was UCSB's nominee for this nationwide contest, after winning the Lancaster Award for the best PhD Dissertation at UCSB in 2016. She is the recipient of numerous other awards and recognitions, including the U.S. Presidential Fellowship (2008), Outstanding Doctoral Candidate Fellowship (2008), being one of three researchers worldwide to win the prestigious IEEE EDS PhD Fellowship Award (2011), a "Bright Mind" invited speaker at the KAUST-NSF conference (2015), one of three winners of the Falling Walls Lab Young Innovator's Award at San Diego (2015), recipient of "Materials Research Society's Graduate Student Award" (2015), named a "Rising Star" in Electrical Engineering and Computer Science (2015), invited speaker at TEDx (2016) and recipient of MIT Translational Fellowship (2017).
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Mar 05, 2018 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Pramod Khargonekar, University of California, Irvine
Talk Title: Electric Grid Integration of Renewable Generation and Distributed Control
Abstract: The main goal of this presentation is to showcase the major challenges in integrating large amounts of solar and wind electric energy in power systems. I will begin with an overview of the key drivers for increased use of solar and wind electricity production: carbon emissions reduction for climate change mitigation, falling prices of wind and solar generation, and socio-economic policies and preferences. This will be followed by a description of the major obstacles and challenges in power systems operations and controls in using large amounts of wind and solar electricity while achieving reliability at low cost. I will next highlight some of the possible avenues to overcoming these obstacles where control systems technologies hold significant potential: harnessing demand side flexibility, energy storage and electric vehicles, and economic market operations. I will present some of our recent results along these directions. The talk will conclude with some thoughts on the evolutionary nature of electric energy system development and technological change, resilience of infrastructures and prospects for the future.
Biography: Pramod Khargonekar received B. Tech. Degree in electrical engineering in 1977 from the Indian Institute of Technology, Bombay, India, and M.S. degree in mathematics in 1980 and Ph.D. degree in electrical engineering in 1981 from the University of Florida, respectively. He has been on faculty at the University of Florida, University of Minnesota, The University of Michigan, and the University of California, Irvine. He was Chairman of the Department of Electrical Engineering and Computer Science from 1997 to 2001 and also held the position of Claude E. Shannon Professor of Engineering Science at The University of Michigan. From 2001 to 2009, he was Dean of the College of Engineering and Eckis Professor of Electrical and Computer Engineering at the University of Florida till 2016. He also served briefly as Deputy Director of Technology at ARPA-E, US Department of Energy in 2012-13. He was appointed by the National Science Foundation (NSF) to serve as Assistant Director for the Directorate of Engineering (ENG) in March 2013, a position he held till June 2016. In this position, Khargonekar led the ENG Directorate with an annual budget of more than $950 million. In addition, he served as a member of the NSF senior leadership and management team and participated in setting priorities and policies. In June 2016, he assumed his current position as Vice Chancellor for Research and Distinguished Professor of Electrical Engineering and Computer Science at the University of California, Irvine.
Khargonekar's research and teaching interests are centered on theory and applications of systems and control. His early work was on mathematical control theory, specifically focusing on robust control analysis and design. During the 1990's, he was involved in a major multidisciplinary project on applications of control and estimation techniques to semiconductor manufacturing. His current research and teaching interests include systems and control theory, machine learning, and applications to smart electric grid and neural engineering. He has been recognized as a Web of Science Highly Cited Researcher. He is a recipient of the NSF Presidential Young Investigator Award, the American Automatic Control Council's Donald Eckman Award, the Japan Society for Promotion of Science fellowships, the IEEE W. R. G. Baker Prize Award, the IEEE CSS George Axelby Best Paper Award, the Hugo Schuck ACC Best Paper Award, and the Distinguished Alumnus and Distinguished Service Awards from the Indian Institute of Technology, Bombay. He is a Fellow of IEEE and IFAC. At the University of Michigan, he received the Arthur F. Thurnau Professorship. In the past, he has served as Associate Editor for IEEE Transactions on Automatic Control, SIAM Journal of Control, Systems and Control Letters, and International J. of Robust and Nonlinear Control. He has been a member of the IEEE Control Systems Theory and Robust Control technical committee. He has also served as Chair and Member of the American Automatic Control Council's Donald Eckman Award Committee. He has served as Program Co-Chair of the American Control Conference. Recently, he was a member of the IEEE Smart Grid 2030 Vision committee.
Host: Mihailo Jovanovic, mihailo@usc.edu
More Information: khargonekar.jpg (JPEG Image, 1886 × 2693 pixels) - Scaled (32%).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
-
Biomedical Engineering Seminars
Mon, Mar 05, 2018 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Talk Title: TBA
Host: Professor Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
Epstein Institute Seminar, ISE 651
.jpg)
Mon, Mar 05, 2018 @ 01:00 PM - 02:00 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Bodhisattva Sen, Associate Professor, Columbia University
Talk Title: Nonparametric Convex Regression
Host: Prof. Jong-Shi Pang
More Information: March 5, 2018.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
-
Biomedical Engineering Department Guest Speaker
Mon, Mar 05, 2018 @ 01:00 PM - 02:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Henrik Jorntell, PhD,
Talk Title: Brain microcircuitry analysis in vivo for novel solutions in neuroengineering and biorobotics
Abstract: The talk will summarize findings and theories originating from in vivo intracellular recordings of a wide set of neocortical and subcortical neurons made in my lab during the last decade. Our focus has been to clarify the internal organization and physiology of brain microcircuitry, which we believe are important to explain and reverse-engineer multiple aspects of brain function. Our analysis started out with the cerebellum, whose role in motor control depends critically on the functions of the spinocerebellar systems and thereby the spinal cord circuitry, where we also made recordings. By combining this information, we also developed a theory for the circuitry-level organization of somatic motor control. A separate, but related, analysis was on the representation of haptic information in the cuneate nucleus of the brainstem. In contrast to classical reductionist approaches, where skin sensor information is considered represented in a pixel-wise fashion, our analysis focused on more natural forms of mechanical interactions and suggested a very different scheme of integration of haptic information. Our current work is focused on neocortical neurons, where we again move away from classical reductionist thinking and instead consider their role in forming the network and the functions that are made possible with that change of viewing angle. Altogether, these insights have made us steer away from the predominating sparse coding / grandmother neuron inspired theories of brain function. Instead, we believe brain operation is based on kernel- based representations residing across large populations of neurons -“ the advantages are that it allows for richer representation and generalization of learning to novel contexts, which together provides for more versatile system behavior. From this research stems multiple principles applicable to novel approaches in neuroengineering and biorobotics.
Host: Francisco Valero-Cuevas, PhD
Location: Corwin D. Denney Research Center (DRB) - 145/145A
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
CS Colloquium: TBA
Tue, Mar 06, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: TBA, TBA
Talk Title: TBA
Series: CS Colloquium
Abstract: TBA
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: TBA
Host: Muhammad Naveed / David Kempe
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Michelson Center for Convergent Biosciences Seminar
Tue, Mar 06, 2018 @ 12:00 PM - 01:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Professor Ali Khademhosseini, UCLA
Talk Title: Nano and Microfabricated Hydrogels for Regenerative Engineering
Abstract: Engineered materials that integrate advances in polymer chemistry, nanotechnology, and biological sciences have the potential to create powerful medical therapies. Our group aims to engineer tissue regenerative therapies using water-containing polymer networks, called hydrogels, that can regulate cell behavior. Specifically, we have developed photocrosslinkable hybrid hydrogels that combine natural biomolecules with nanoparticles to regulate the chemical, biological, mechanical and electrical properties of gels. These functional scaffolds induce the differentiation of stem cells to desired cell types and direct the formation of vascularized heart or bone tissues. Since tissue function is highly dependent on architecture, we have also used microfabrication methods, such as microfluidics, photolithography, bioprinting, and molding, to regulate the architecture of these materials. We have employed these strategies to generate miniaturized tissues. To create tissue complexity, we have also developed directed assembly techniques to compile small tissue modules into larger constructs. It is anticipated that such approaches will lead to the development of next-generation regenerative therapeutics and biomedical devices.
Biography: Ali Khademhosseini is Professor of Bioengineering, Chemical Engineering and Radiology at the University of California-Los Angeles (UCLA). He is the Founding Director of the Center for Minimally Invasive Therapeutics at UCLA as well as an Associate Director of the California NanoSystems Institute. He joined UCLA in Nov. 2017 from Harvard University where he was Professor of Medicine at Harvard Medical School (HMS) and where he directed the Biomaterials Innovation Research Center (BIRC), a leading initiative in making engineered biomedical materials. He is recognized as a leader in combining micro- and nano-engineering approaches with advanced biomaterials for regenerative medicine applications. In particular, his laboratory has pioneered numerous technologies and materials for controlling the architecture and function of engineered vascularized tissues. He is a recipient of the Presidential Early Career Award for Scientists and Engineers, the highest honor given by the US government for early career investigators. In 2011, he received the Pioneers of Miniaturization Prize from the Royal Society of Chemistry (RSC) for his contribution to microscale tissue engineering and microfluidics. In 2016, he received the Sr. Scientist Award of Tissue Engineering and Regenerative Medicine Society -Americas Chapter (TERMIS-AM) and in 2017 he received the Clemson Award of the Society for Biomaterials. He is also a fellow of the American Institute of Medical and Biological Engineering (AIMBE), Materials Research Society (MRS), Biomedical Engineering Society (BMES), Royal Society of Chemistry (RSC), Fellow of the Biomaterials Sciences and Engineering (FBSE) and American Association for the Advancement of Science (AAAS). He received his Ph.D. in bioengineering from MIT (2005), and MASc (2001) and BASc (1999) degrees from University of Toronto both in chemical engineering.
Host: Prof. Andrea Armani
Location: Michelson 102
Audiences: Everyone Is Invited
Contact: Andrea Armani
-
EE-EP Faculty Candidate - Sihong Wang, Wednesday, March 7th at 12pm in EEB 248
Wed, Mar 07, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sihong Wang, Stanford University
Talk Title: Merging Electronics with Living Systems: Intrinsically Stretchable and Self-Powered Electronics
Abstract: The vast amount of biological mysteries and biomedical challenges faced by human provide a prominent drive for seamlessly merging electronics with biological living systems (e.g. human bodies) to achieve long-term stable functions. Towards this trend, the main bottlenecks are the huge mechanical mismatch between the current form of rigid electronics and the soft biological tissues, as well as the limited lifetimes of the battery-based power supplies.
In this talk, I will first describe a new form of electronics with skin-like softness and stretchability, which is built upon a new class of intrinsically stretchable polymer materials and a new set of fabrication technology. As the core material basis, intrinsically stretchable polymer semiconductors have been developed through the physical engineering of polymer chain dynamics and crystallization based on the nanoconfinement effect. This fundamentally-new and universally-applicable methodology enables conjugated polymers to possess both high electrical-performance and extraordinary stretchability. Then, proceeding towards building electronics with this new class of polymer materials, the first polymer-applicable fabrication platform has been designed for large-scale intrinsically stretchable transistor arrays. As a whole, these renovations in the material basis and technology foundation have led to the realization of circuit-level functionalities for the processing of biological signals, with unprecedented mechanical deformability and skin conformability. In the second part of the talk, I will introduce the invention and development of triboelectric nanogenerators as a new technology for mechanical energy harvesting, which provides a solution for sustainably powering electronics. The discussion will span from the establishment of basic operation mechanisms, the design strategies of material and device structure towards high energy conversion efficiency, to the hybridization with Li-ion batteries for effective energy storage. Equipping electronics with human-compatible form-factors and biomechanically-driven power supplies has opened a new paradigm for wearable and implantable bio-electronic tools for biological studies, personal healthcare, medical diagnosis and therapeutics.
Biography: Sihong Wang is a postdoctoral fellow at Stanford University, working with Prof. Zhenan Bao. He received his PhD degree in Materials Science and Engineering (with Minor in Electrical Engineering) from the Georgia Institute of Technology under the supervision of Prof. Zhong Lin (Z.L.) Wang, and his Bachelor's degree from Tsinghua University. Currently, he is working on intrinsically stretchable polymer semiconductors and transistors for wearable and biomedical electronics. His PhD research had focused on nanogenerators for mechanical energy harvesting and their integrated energy storage systems. He was awarded MRS Graduate Student Award, Chinese Government Award for Outstanding Students Abroad, Top 10 Breakthroughs of 2012 by Physics World, etc.
Host: EE_Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Koopman Operator Theory in Dynamical Systems and Applications
Wed, Mar 07, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Igor Mezic, University of California Santa Barbara
Talk Title: Koopman Operator Theory in Dynamical Systems and Applications
Series: Joint Seminar Series on Cyber-Physical Systems and CommNetS-MHI
Abstract: There is long history of use of mathematical decompositions to describe complex phenomena using simpler ingredients. One example is the decomposition of string vibrations into its primary, secondary, and higher modes. Recently, a spectral decomposition relying on Koopman operator theory has attracted interest in science and engineering communities. The spectral decomposition is based on an extension of the Koopman-von Neumann formalism to dissipative, possibly infinite-dimensional systems, including those describing flow of viscous fluids at the fundamental level, but also thermal flows in buildings, and power grid dynamics, at a more applied level. At its mathematical foundations, it is a spectral theory of composition operators. We will present the foundations of the theory, the numerical analysis approach, and its applications in the variety of applied contexts.
Biography: Igor Mezic is currently a Professor and Director at the Center for Energy-Efficient Design and Head of Buildings and Design Solutions Group of the Institute for Energy Efficiency at the University of California, Santa Barbara. He received an M.S. degree in Mechanical Engineering from the University of Rijeka, Croatia in 1990 and a Ph.D. in Applied Mechanics from the California Institute of Technology in 1994. Before coming to UC Santa Barbara in 1995, he was a Postdoctoral Research Fellow at the Mathematics Institute at the University of Warwick, UK. From 2000-2001, he also served as an Associate Professor in the Division of Engineering and Applied Science at Harvard University. Igor Mezic's current research interests include dynamical systems theory of complex systems, including large-scale social systems. He was awarded the National Science Foundation CAREER Award for research on Nonlinear Dynamics and Control from Microscale to Macroscale (1999), as well as a Sloan Foundation Fellowship in Mathematics (1999) and the Axelby Outstanding Paper Award (2000). For his technology contributions, he was awarded the United Technologies Senior Vice Presidents Special Award (2007), and gave a number of plenary lectures. In addition to contributing his time and expertise to a significant number journals, panels, workshops, and conferences, Mezic has over 150 journal publications, has edited or co-written three books and has received numerous grants and industrial contracts. Mezic is a Fellow of the Society for Industrial and Applied Mathematics (SIAM) and the American Physical Society.
Host: Prof. Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
-
CAIS Seminar: Andrew Perrault (University of Toronto) – Developing and Coordinating Autonomous Agents for Efficient Electricity Markets
Wed, Mar 07, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andrew Perrault, University of Toronto
Talk Title: Developing and Coordinating Autonomous Agents for Efficient Electricity Markets
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Aggressive greenhouse gas reduction targets will necessitate a transformation of energy use systems, with increasing emphasis on electricity, which can be decarbonized more efficiently than other energy sources. Mr. Perrault argues that deploying consumer-representing autonomous agents can make this transformation less expensive by allowing attention-limited consumers to respond to changes in market conditions. The talk has two parts: in Part I, he develops a cooperative game theoretic model that illustrates the value of such agents in electricity markets. In Part II, he focuses on the problem of training such an agent using a new variant of preference elicitation called experiential elicitation.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Andrew Perrault is a PhD student at University of Toronto, supervised by Craig Boutilier. His research focuses on the application of AI to electricity markets and electricity use. He is the co-founder and co-lead developer at theschoolfund.org, a non-profit that crowdfunds scholarships for secondary school students in developing countries.
Host: Milind Tambe
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Jonas Mueller (MIT) – Learning Optimal Interventions under Uncertainty
Thu, Mar 08, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jonas Mueller, MIT
Talk Title: Learning Optimal Interventions under Uncertainty
Series: Computer Science Colloquium
Abstract: A basic goal of data analysis is learning which actions (ie. interventions) are best for producing desired outcomes. While advances in reinforcement learning and bandit/Bayesian optimization have shown great promise, these sequential methods are primarily limited to digital environments where iterating between modeling and experimentation is easy. Although more widely applicable, learning from a fixed (observational) dataset will inherently involve substantial uncertainty due to limited samples, and it is undesirable to prescribe actions whose outcomes are unclear.
In this talk, I will consider such settings from a Bayesian perspective and formalize the of role of uncertainty in data-driven actions. Adopting a Gaussian process framework, I will introduce a conservative definition of the optimal intervention which can be either tailored on an individual basis or globally enacted over a population. Subsequently, these ideas are extended to structured sequence data via a recurrent variational autoencoder model. In both cases, gradient methods are employed to identify the best intervention and a key theme of the approach is carefully constraining this optimization to avoid regions of high uncertainty. Various applications of this methodology will presented including gene expression manipulation, therapeutic antibody design, and revision of natural language.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.
Biography: Jonas Mueller is a Computer Science Ph.D. student at MIT working with Tommi Jaakkola and David Gifford. His research interests lie in developing machine learning methods to advance both statistical science and artificial intelligence applications. Integrating ideas from optimal transport, deep learning, Bayesian/bandit optimization, and interpretable modeling, much of his work has been motivated by applications in bioinformatics and natural language processing. Previously, Jonas studied Math and Statistics at UC Berkeley, where he was awarded the Departmental Citation, and he recently also spent some time at Microsoft Research.
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
Biomedical Engineering Department Guest Speaker
Thu, Mar 08, 2018 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Naveed Ejaz, PhD, SENIOR POSTDOCTORAL RESEARCHER IN CLINICAL AND HEALTHY HUMAN MOTOR CONTROL; BRAIN-MIND INSTITUTE, WESTERN UNIVERSITY, LONDON ONTARIO, CANADA
Talk Title: How does the brain control dexterous hand function (and how does function recover after injury)
Abstract: It is hard to over-state the importance of our hands in daily life; they are the primary means with which we manipulate the environment around us. Evidence from invasive studies in non-human primates has demonstrated that hand function is controlled by interactions between motor circuits in cortical and subcortical brain areas. Since such invasive investigations in humans are not possible, the question of how cortical brain areas organize to facilitate dexterous control, and the extent to which (if at all) subcortical pathways contribute to hand function in man is unknown. In this seminar, I will draw upon multiple studies from my research program to answer these two questions. First, I will use functional magnetic resonance imaging to characterize the population response of neurons in the neocortex that are critical for dexterous hand control. I will provide evidence that the population response appears to be shaped by experiential use of the hand, and will further demonstrate the nature of plasticity in the associated circuits by using individuals with hand amputation as a model of neocortical deafferentation. Next, I will discuss evidence for a new model of hand recovery after stroke, one that relies on the ability of subcortical brain structures to provide compensatory control of the hand after damage to the neocortex. Throughout the seminar, I will briefly highlight how my research program provides tools that can be used to investigate hand function as a function of development, ageing, and disease, as well as provide hints on how to recover dexterous control in patients after neural injuries (e.g. stroke, cervical spondylotic myelopathy).
Host: Francisco Valero-Cuevas, PhD
Location: DRB 145/145A
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
EE Seminar - Bridging Control Theory and Machine Learning
Thu, Mar 08, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Bin Hu, Postdoctoral Researcher, University of Wisconsin-Madison
Talk Title: Bridging Control Theory and Machine Learning
Abstract: The design of modern intelligent systems relies heavily on techniques developed in the control and machine learning communities. On one hand, control techniques are crucial for safety-critical systems; the robustness to uncertainty and disturbance is typically introduced by a model-based design equipped with sensing, actuation, and feedback. On the other hand, learning techniques have achieved the state-of-the-art performance for a variety of artificial intelligence tasks (computer vision, natural language processing, and Go). The developments of next-generation intelligent systems such as self-driving cars, advanced robotics, and smart buildings require leveraging these control and learning techniques in an efficient and safe manner.
This talk will focus on fundamental connections between robust control and machine learning. Specifically, we will present a control perspective on the empirical risk minimization (ERM) problem in machine learning. ERM is a central topic in machine learning research, and is typically solved using first-order optimization methods which are developed in a case-by-case manner. First, we will discuss how to adapt robust control theory to automate the analysis of such optimization methods including the gradient descent method, Nesterov's accelerated method, stochastic gradient descent (SGD), stochastic average gradient (SAG), SAGA, Finito, stochastic dual coordinate ascent (SDCA), stochastic variance reduction gradient (SVRG), and Katyusha momentum. Next, we will show how to apply classical control design tools (Nyquist plots and multiplier theory) to develop new robust accelerated methods for ERM problems. Finally, we will conclude with some long-term research vision on the general connections between our proposed control-oriented tools and reinforcement learning methods.
Biography: Bin Hu received the B.Sc. in Theoretical and Applied Mechanics from the University of Science and Technology of China in 2008, and received the M.S. in Computational Mechanics from Carnegie Mellon University in 2010. He received the Ph.D. in Aerospace Engineering and Mechanics at the University of Minnesota in 2016, advised by Peter Seiler. He is currently a postdoctoral researcher in the optimization group of the Wisconsin Institute for Discovery at the University of Wisconsin-Madison. He is interested in building fundamental connections between the techniques used in the control and machine learning communities. His current research focuses on tailoring robust control theory (integral quadratic constraints, dissipation inequalities, jump system theory, etc) to automate the analysis and design of stochastic optimization methods for large-scale learning tasks. He is also particularly interested in the connections between model-based control and model-free reinforcement learning.
Host: Ashutosh Nayyar, ashutosn@usc.edu, x02353
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
W.V.T. RUSCH ENGINEERING HONORS COLLOQUIUM
Fri, Mar 09, 2018 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Mark Cranney, Chief Commercial Officer at SignalFx, Prior Operating Partner at Andreessen Horowitz (Silicon Valley VC Firm)
Talk Title: Metrics that Matter to Venture Capitalists
Host: Dr. Prata & EHP
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Su Stevens
-
Yasser Khan, Friday, March 9th at 2pm in EEB 248
Fri, Mar 09, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yasser Khan, University of California, Berkeley
Talk Title: Integration of Printed Sensors to Flexible Hybrid Electronics for Wearable Health Monitoring
Abstract: In the era of "electronic skin" and "human intranet", the potential of wearable sensors that can monitor vital signs, analytes in bodily fluids, and biosignals is immense. Fabrication of wearables to date heavily relies on conventional semiconductor processing, which is expensive and has limited large-area scalability. Taking advantage of the unique manufacturing capabilities of printed electronics, we can now design wearables that are soft, lightweight, and skin-like. In addition, using soft and conformable sensors, we can significantly improve the signal-to-noise ratio (SNR) due to the high fidelity sensor-skin interface. In this talk, I will first present printed and flexible all-organic optoelectronic oximeter sensors, which can measure pulse rate and oxygenation accurately both in the transmission and reflection mode. Then I will introduce the design and fabrication of flexible and printed gold electrode arrays that are ideal for bioimpedance tomography, electrocardiography (ECG) and electromyography (EMG). Finally, a key enabling technology for wearables - flexible hybrid electronics (FHE) will be presented. The implementation of FHE in an integrated multi-sensor platform will be discussed, where sensors fabricated using solution processable functional inks are interfaced to rigid electronics for health and performance monitoring.
Biography: Yasser Khan is a Ph.D. candidate in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, in Prof. Ana Claudia Arias' Group. He received his B.S. in Electrical Engineering from the University of Texas at Dallas in 2010, and M.S. in Electrical Engineering from King Abdullah University of Science and Technology in 2012. Yasser's research focuses mainly on wearable medical devices, with an emphasis on flexible bioelectronic and biophotonic sensors.
Yasser received the EECS departmental fellowship at UC Berkeley, discovery scholarship and graduate fellowship at KAUST, and best presentation and poster awards at MRS meetings. He is a big proponent of flexible hybrid electronics, which brings together flexible sensors and silicon ICs under the same platform and utilizes these two different technologies to their strengths. His research vision is to implement a massive number of flexible and printed sensors for medical, structural, and industrial monitoring.
Host: EE-Electrophysics
Location: Estrella Housing Partners (EHP) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
EE-EP Faculty Candidate - Yasser Khan, Friday, March 9th at 2pm in EEB 248
Fri, Mar 09, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yasser Khan, University of California, Berkeley
Talk Title: Integration of Printed Sensors to Flexible Hybrid Electronics for Wearable Health Monitoring
Abstract: In the era of "electronic skin" and "human intranet", the potential of wearable sensors that can monitor vital signs, analytes in bodily fluids, and biosignals is immense. Fabrication of wearables to date heavily relies on conventional semiconductor processing, which is expensive and has limited large-area scalability. Taking advantage of the unique manufacturing capabilities of printed electronics, we can now design wearables that are soft, lightweight, and skin-like. In addition, using soft and conformable sensors, we can significantly improve the signal-to-noise ratio (SNR) due to the high fidelity sensor-skin interface. In this talk, I will first present printed and flexible all-organic optoelectronic oximeter sensors, which can measure pulse rate and oxygenation accurately both in the transmission and reflection mode. Then I will introduce the design and fabrication of flexible and printed gold electrode arrays that are ideal for bioimpedance tomography, electrocardiography (ECG) and electromyography (EMG). Finally, a key enabling technology for wearables - flexible hybrid electronics (FHE) will be presented. The implementation of FHE in an integrated multi-sensor platform will be discussed, where sensors fabricated using solution processable functional inks are interfaced to rigid electronics for health and performance monitoring.
Biography: Yasser Khan is a Ph.D. candidate in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, in Prof. Ana Claudia Arias' Group. He received his B.S. in Electrical Engineering from the University of Texas at Dallas in 2010, and M.S. in Electrical Engineering from King Abdullah University of Science and Technology in 2012. Yasser's research focuses mainly on wearable medical devices, with an emphasis on flexible bioelectronic and biophotonic sensors.
Yasser received the EECS departmental fellowship at UC Berkeley, discovery scholarship and graduate fellowship at KAUST, and best presentation and poster awards at MRS meetings. He is a big proponent of flexible hybrid electronics, which brings together flexible sensors and silicon ICs under the same platform and utilizes these two different technologies to their strengths. His research vision is to implement a massive number of flexible and printed sensors for medical, structural, and industrial monitoring.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Astani Civil and Environmental Engineering Seminar
Fri, Mar 09, 2018 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Mohammad Hanifehzadeh and Evangelos Pantazis, Astani CEE Ph.D. Students
Talk Title: Multi-Hazard Performance of Reinforced Concrete Dry Casks Subjected To Dynamic Mechanical Load
Abstract: See Attachment
More Information: M. Hanifehzadeh 392018.pdf
Location: Ray R. Irani Hall (RRI) - 101
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
-
Biomedical Engineering Seminars
Mon, Mar 12, 2018 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Talk Title: TBA
Host: Professor Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
SPRING BREAK **NO ISE 651 EPSTEIN SEMINAR**
Tue, Mar 13, 2018 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: N/A, N/A
Talk Title: N/A
Host: N/A
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
-
Aerospace and Mechanical Engineering Seminar
Wed, Mar 14, 2018 @ 10:15 AM - 11:15 AM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: José A. Camberos, Air Force Research Laboratory's Multidisciplinary Science and Technology Center
Talk Title: Energy Conversion and Storage Explored with Synchrotron X-ray Tomography and Modeling
Abstract: The world is experiencing an era of rapid change and globalization in which increased competition for resources, access to Information Technology, and changing demographics has the potential to shift the balance of power. In this era, the U. S. Air Force is facing conditions that diverge significantly from the strategic environment of the last two decades as potential adversaries use emergent globalized technology and manufacturing infrastructure to rapidly develop sophisticated military capabilities that create more contested operational environments. The challenge is to ensure our Defense forces obtain the best technology, at the right time, while affordably meeting mission needs. Specifically, future Air Force missions will require all the modern systems performance characteristics: supersonic dash speed, efficient super-cruise, stealth, flexible weapon payloads, maneuverability, active and passive defensive systems and countermeasures, small logistical footprint, and extended standoff ranges beyond that of current systems. To meet the challenge, the Air Force Research Laboratory's Multidisciplinary Science and Technology Center is developing (conceptual) design capabilities that integrate multiple technical disciplines, effectively, efficiently, and affordably. The payoff envisioned will mitigate the adverse performance impact that comes with unwanted or unanticipated systems interactions and will proactively enable the discovery and exploitation of new phenomena for the development of revolutionary aerospace systems.
Host: Department of Aerospace and Mechanical Engineering
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
-
EE Seminar: Achieving Ultra-High Reliability for Emerging Applications in Future Wireless Systems
Mon, Mar 19, 2018 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Meryem Simsek, TU Dresden, Germany and ICSI Berkeley
Talk Title: Achieving Ultra-High Reliability for Emerging Applications in Future Wireless Systems
Abstract: Wireless communication systems have been evolving since the first generation. With the fifth generation (5G) of wireless systems, the focus is not only on the evolutionary aspect of increased data rate, but also on novel performance metrics for emerging applications, such as autonomous driving, industrial automation, and Tactile Internet applications. In this context, the wireless system design has increasingly turned its focus on guaranteeing extremely high reliability and low latency. Hence, the developments of 5G systems require leveraging novel techniques to cope with the heterogeneity of applications and to achieve their stringent requirements.
This talk focuses on the definition of reliability in wireless systems and on fundamental techniques to achieve reliability requirements in 5G networks. Firstly, definitions and concepts of reliability theory, which provides a mathematical tool to evaluate and improve the reliability and availability of technical components and systems, are applied and extended to wireless networks. Then, the signal-to-interference-plus-noise ratio (SINR) is identified as a major metric to study the impact of the wireless link quality on high availability. For addressing new requirements imposed on emerging 5G applications, e.g. outage probabilities of 10-7 or less, a highly accurate modelling of the SINR is needed. A stochastic model of the SINR including the shadow fading, noise power, and best server policy is presented as an alternative to highly complex wireless system simulations providing extreme accuracy and a tool to evaluate the outage probability at any position in any given wireless network. As diversity techniques, such as multi-point connectivity which are also supported by the 5G systems, are widely accepted to be key to achieve high reliability, the proposed SINR model is extended to multi-point transmission. Numerical evaluations reveal the applicability of the model to multi-point connectivity. However, unlike the general understanding, it will be shown that ensuring low outage probabilities does not necessarily imply improved reliability in multi-user systems, in which resources are shared. In this regard, a novel matching theory-based algorithm aiming for guaranteeing reliability requirements in a multi-cellular, multi-user system will be presented. The proposed algorithm yields a maximum gain of 150% as compared to fixed multi-point approaches. The talk will be concluded with a research vision for how the results obtained so far can be extended to design highly flexible and autonomous tools for investigating future wireless systems, which simultaneously support multiple services with diverse requirements. These tools will open the new era for studying the feasibility of emerging applications under given conditions and the coexistence of various use cases with diverse and (partially) competing requirements, for developing novel concepts and end-to-end solutions for intelligent and predictive resource management in wireless systems, and for applying and implementing these concepts and solutions into real systems.
Biography: Meryem Simsek is a Principal Investigator at the International Computer Science Institute Berkeley and a senior Research Group Leader at the Technical University Dresden. She earned her Dipl.-Ing. degree in Electrical Engineering and Information Technology and her Ph.D. on "Learning-Based Techniques for Intercell-Interference Coordination in LTE-Advanced Heterogeneous Networks" from the University of Duisburg-Essen, Germany in 2008 and 2013, respectively. Her current research focuses on modelling and optimizing emerging wireless systems, heterogeneous wireless networks, achieving high reliability and low latency in 5G networks and Tactile Internet applications. Further research interests are based on developing novel tools for network management, wireless edge automation, and autonomous wireless networks and implementing these tools into real systems. She is the recipient of the fellowships by the German Physical Society (2004-2005) and the German National Academic Foundation, which is only granted to the outstanding 0.5% students in Germany (2004-2008). She holds the titles of the first electrical engineering student who has graduated before the regular duration of study and the best Diplom-graduate in Electrical Engineering at the University of Duisburg-Essen (2008). Meryem Simsek received the IEEE Communications Society Fred W. Ellersick Prize 2015 for IEEE Communications Magazine paper "When Cellular Meets WiFi in Wireless Small Cell Networks". In addition, she has initiated and is chairing the IEEE Tactile Internet Technical Committee and is serving as the secretary of the IEEE P1918.1 standardization working group, which she has co-initiated. She is also holding the position of the "industry and student activities coordinator" in the IEEE Women in Communications Engineering (WICE) committee.
Host: Andreas Molisch, molisch@usc.edu, x04670
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
EE-EP Faculty Candidate - Suhas Kumar, Monday, March 19th at 12:00pm in EEB 132
Mon, Mar 19, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Suhas Kumar, Hewlett Packard Labs, Palo Alto, CA
Talk Title: Computing with Chaos
Abstract: As we realize that many profoundly important problems, such as decoding cancerous genes, prime factorization for cryptography, accurate weather prediction, etc., cannot be solved efficiently even with the best of our digital computers, we need look for new computing paradigms beyond the ageing von Neumann architecture, Boltzmann tyranny, and the Turing limit.
Although chaos sounds antithetical to solving problems, many of the finest computers in nature, from neural circuits in the brain, to evolutionary natural selection, operate at the "edge of chaos" within a "locally active" region, to produce "complexity and emergence". Here I will illustrate how these purely mathematical constructs, firmly established less than a decade ago, can be utilized via electronics to construct efficient computing systems. Taking this rather different route also necessitates a completely revamped research into all the building blocks of a computing system, including discovering relevant nonlinear material properties, constructing radically new locally active device models, and designing a device + problem-centric system architecture. I will use an illustrative example, where we discovered a strange thermal property of a material during its Mott transition that exhibited local activity and controlled electronic chaos, an ensemble of which was used to build a transistorless analogue Hopfield neural network. This scalable and programmable non-von Neumann network utilized chaos to find the global minimum (the best solution) of any constrained optimization problem, and was able to solve the NP-hard traveling salesman problem 1000 times faster than the world's best digital supercomputer.
Biography: Suhas Kumar is a Postdoctoral Researcher and Principal Investigator at Hewlett Packard Labs, Palo Alto, CA. He earned a Ph.D. from Stanford University in 2014. He leads a group that investigates novel physical properties of materials and devices relevant to new forms of physics-driven and bio-inspired computing. His latest work includes a practical demonstration of the idea of using chaos to accelerate solutions to NP-hard problems. His research has been featured in dozens of scientific publications, conferences, patent applications, and popular media. His contributions were recently acknowledged with the Klein Scientific Development award.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Biomedical Engineering Seminars
Mon, Mar 19, 2018 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Talk Title: TBA
Host: Professor Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
Biomedical Engineering Department Guest Speaker
Mon, Mar 19, 2018 @ 01:00 PM - 02:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Aldo Faisal,
Talk Title: Data-Driven Neuroengineering from Action to Cognition
Abstract: Our work centres on non-invasive neuroengineering that combines cross-disciplinary machine learning and robotics approaches together with experimental methods in the field of neuroscience and experimental psychology. Our goal is to achieve a data-driven understanding of human behaviour to infer intentions and translate this into technology that helps people in health and disease. I will illustrate our research program by reviewing our research efforts in, building an Atlas of Behaviour using wearable sensors capturing the vast majority of human perceptual input and motor output in daily lives. The development of novel machine learning methods for the data- driven understanding of human behaviour to decode intention and control robotic interfaces for human augmentation, and clinical translation of our research in patients with neurodegenerative disorders.
Host: Ellis Meng, PhD
Location: Corwin D. Denney Research Center (DRB) - 145/145A
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Mar 19, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ali Jadbabaie, Massachusetts Institute of Technology
Talk Title: Near-Optimal Sparse Sensor and Actuator Selection
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: In this talk, I present our recent efforts in developing rigorous approaches to sparse sensor and actuator selection in large-scale linear dynamical systems. While sparse sensor and actuator selection is known to be NP-Hard, using tools from optimal experiment design and submodular optimization, we develop a framework for near- optimal sensor and actuator selection with provable approximation guarantees using greedy algorithms. We then extend these results to develop a robust variant of the approximations themes, where the optimization of sensor selection is performed in presence of an adversary who can cause a subset of sensors to fail. Next, using recent developments in graph sparsification and column selection literature, we show how to select a sparse subset of sensors or actuators while guaranteeing performance with respect to the fully sensed or actuated system (and not the optimal sparse one). As a corollary we show that by utilizing a time varying sense or actuator selection schedule, one can guarantee near-optimal sensing/control performance by selecting a dimension-independent (constant) number of sensors or actuators. Joint work with Vassilis Tzoumas (Penn), Milad Siami (MIT), and Alex Olshevsky (BU)
Biography: Ali Jadbabaie is the JR East Professor of Engineering and Associate Director of the Institute for Data, Systems and Society at MIT, where he is also on the faculty of the department of civil and environmental engineering and a principal investigator in the Laboratory for Information and Decision Systems (LIDS), and the director of the Sociotechnical Systems Research Center, one of MIT's 13 research laboratories. He received his Bachelors (with high honors) from Sharif University of Technology in Tehran, Iran, a Masters degree in electrical and computer engineering from the University of New Mexico, and his PhD in control and dynamical systems from the California Institute of Technology. He was a postdoctoral scholar at Yale University before joining the faculty at Penn in July 2002 where he was the Alfred Fitler Moore a Professor of Network Science. He was the inaugural editor-in-chief of IEEE Transactions on Network Science and Engineering, a new interdisciplinary journal sponsored by several IEEE societies. He is a recipient of a National Science Foundation Career Award, an Office of Naval Research Young Investigator Award, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council, and the George S. Axelby Best Paper Award from the IEEE Control Systems Society. His students have been winners and finalists of student best paper awards at various ACC and CDC conferences. He is an IEEE fellow and a recipient of the 2016 Vannevar Bush Fellowship from the office of Secretary of Defense, and a member of the National Academies of Science, Engineering, and Medicine's Intelligence Science and Technology Expert Group (ISTEG). His current research interests are in distributed decision making and optimization, multi-agent coordination and control, network science, and network economics.
Host: Ketan Savla, ksavla@usc.edu
More Information: jadbabaie.jpg (JPEG Image, 711 × 938 pixels) - Scaled (93%).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
-
Sonny Astani Civil and Environmental Engineering Seminar
Mon, Mar 19, 2018 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Gabriel Raviv, Lecturer,Technion Israel Institute of Technology
Talk Title: Modeling of Near Misses Related to Crane Work at Construction Sites and
Abstract: See attachment
Host: Dr. Lucio Soibelman
More Information: Raviv Announcement - 3-19-2018.pdf
Location: Ray R. Irani Hall (RRI) - 101
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
-
TBA
Tue, Mar 20, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: TBA,
Talk Title: TBA
Series: CS Colloquium
Abstract: TBA
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: TBA
Host: Muhammad Naveed / David Kempe
Location: Olin Hall of Engineering (OHE) - 100 D
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Epstein Institute Seminar, ISE 651
Tue, Mar 20, 2018 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Natashia Boland, Professor, Georgia Tech
Talk Title: Time Discretization in Integer Programming
Host: Dr. Phebe Vayanos/Prof. Suvrajeet Sen
More Information: March 20, 2018.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
-
CS Distinguished Lecture: Sham Kakade (University of Washington) – Sub-Linear Reinforcement Learning
Tue, Mar 20, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sham Kakade, University of Washington
Talk Title: Sub-Linear Reinforcement Learning
Series: Computer Science Distinguished Lecture Series
Abstract: Suppose an agent is an unknown environment and seeks to maximize his/her long term future reward. We consider the basic question: does the agent need to learn an accurate model of the environment before he/she can start executing a near-optimal long term course of actions?
Specifically, this talk will consider the problem of provably optimal reinforcement learning for (episodic) finite horizon MDPs, i.e., how an agent learns to maximize his/her (long term) reward in an uncertain environment. The talk will present a novel algorithm, the Variance-reduced Upper Confidence Q-learning (vUCQ), which is the first algorithm which enjoys a regret bound that is both sub-linear in the model size and that achieves optimal minimax regret. The algorithm is sub-linear in that the time to achieve epsilon average regret is a number of samples that is far less than that required to learn any (non-trivial) estimate of the underlying model of the environment. The importance of sub-linear algorithms is largely the motivation for algorithms such as "Q-learning" and other "model-free" approaches.
vUCQ is a successive refinement method in which the algorithm reduces the variance in the "Q-value" estimates and couples this estimation scheme with an upper confidence based algorithm. Technically, this coupling of these techniques is what leads to the algorithm's strong guarantees, showing that "model-free" approaches can be optimal.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Statistics and the Department of Computer Science at the University of Washington.
From 2011-2015, I was a principal research scientist at Microsoft Research, New England. From 2010-2012, I was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania. From 2005-2009, I was an assistant professor at the Toyota Technological Institute at Chicago.
I completed my PhD at the Gatsby Computational Neuroscience Unit under the supervision of Peter Dayan, and I was an undergraduate at Caltech where I obtained my BS in physics. I was a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
EE Seminar: IoT in the CMOS Era and Beyond: Leveraging Mixed-Signal Arrays for Ultra-Low-Power Sensing, Computation, and Communication
Wed, Mar 21, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Siddharth Joshi, University of California, San Diego
Talk Title: IoT in the CMOS Era and Beyond: Leveraging Mixed-Signal Arrays for Ultra-Low-Power Sensing, Computation, and Communication
Abstract: Energy efficiencies obtained by analog processing are critical for next-generation "smart" sensory systems that implement intelligence at the edge. Such systems are widely applicable in areas like biomedical data acquisition, continuous infrastructure monitoring, intelligent sensor networks, and data analytics. However, adaptive analog computing is sensitive to nonlinearities induced by mismatch and noise, which has limited the application of analog signal processing to signal conditioning prior to quantization. This has relegated the bulk of the processing to the digital domain, or a remote server, limiting the system efficiency and autonomy. This talk highlights principled techniques to algorithm-circuit co-design to overcome these obstacles, leading to energy-efficient high-fidelity mixed-signal computation and adaptation.
First, I will provide analytical bounds on the energetic advantages derived by alleviating the need for highly accurate and energy-consuming analog-to-digital conversion through high-resolution analog pre-processing. I will then present an embodiment of this principle in a micropower, multichannel, mixed-signal array processor developed in 65nm CMOS. Spatial filtering with the processor yields 84 dB in analog interference suppression at only 2 pJ energy per mixed-signal operation. At the algorithmic level, I will present work on a gradient-free variation of coordinate descent, Successive Stochastic Approximation (S2A). S2A is resilient to the adverse effects of analog mismatch encountered in compact low-power realizations of high-resolution, high-dimensional mixed-signal processing systems. Over-the-air experiments employing S2A in non-line-of-sight demonstrate adaptive beamforming achieving 65 dB of processing gain.
I will conclude with my vision about the impact of mixed-signal processing on the next generation of computing systems and share my recent work spanning across devices (RRAM), architectures (compute-in memory) and emerging applications(neuromorphic computing). Crossing these hierarchies is critical to leverage emerging technologies in realizing the next generation of sensing, computing, and communicating systems.
Biography: Siddharth Joshi is a Postdoctoral Fellow in the department of Bioengineering at UC San Diego, he completed his PhD in 2017 at the department of Electrical and Computer Engineering, UC San Diego where he also completed his M.S. in 2012. His research focuses on the co-design of custom, non-Boolean and non-von Neumann, hardware and algorithms to enable machine learning and adaptive signal processing in highly resource constrained environments. Before coming to UCSD, he completed a B. Tech from Dhirubhai Ambani Institute of Information and Communication Technology in India.
Host: Alice Parker, parker@usc.edu, x04476
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
CS Colloquium: Nithya Sambasivan (Google) - Design for Autonomy and Fairness of New Technology Users in the Global South
Wed, Mar 21, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nithya Sambasivan, Google
Talk Title: Design for Autonomy and Fairness of New Technology Users in the Global South
Series: CS Colloquium
Abstract: 2017 saw half the world online. As technology penetration and ecosystem maturity increase, there is a growing intent to use technology for socio-economic development for new technology users. However, complex long-standing challenges like affordability, safety, and socio-religious diktats affect people at the cusp of the internet. My work aims to empower new technology users with increased autonomy and fairness through technology. I present my prior work on design and evaluation of a cost transparency tool intended to help new mobile Internet users; design to tackle abuse and safety vectors for women in Internet technologies; and design and deployment of an information broadcasting system for urban sex workers in India. I show how prevailing HCI assumptions of privacy, trust, and user identities need to be challenged as Internet advances to reach all edges of human society. Through these projects, I show how large problems can be practically addressed through a combination of design, policy, and algorithms.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Nithya Sambasivan is a researcher focused on technology design for social, economic and political benefits in the Global South. Her research spans the areas of HCI and ICTD, and has won several recognitions at top conferences. She has been a researcher at Google since 2012, where she has co-founded a group to conduct future-facing research on under-represented topics, such as gender equity and new technology users. Her research has influenced several large-scale real-world projects for the next billion users, and has been directly translated to core libraries, metrics, and guidance for Android and Web developers at Build for Billions, design.google/nbu, and Google I/O talks. Nithya has a Ph.D. and MS in information and computer sciences for University of California, Irvine and and MS in Human Computer Interaction (HCI) from Georgia Tech. Her dissertation focused on technology design for the low-income communities of slums, urban sex workers and microentreprises in India. She is a recipient of Google's Anita Borg and UC Irvine Dean's fellowships. She has interned at Microsoft Research India, Nokia Research Center and IBM TJ Watson.
Host: Milind Tambe
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
INCOSE Webinar 110
Wed, Mar 21, 2018 @ 11:00 AM - 12:00 PM
Systems Architecting and Engineering, USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Dr, Swaminathan Natarajan, Dr. Anand Kumar, and Subhro Chaudhuri, Chief Scientist, Tata Consultancy, Senior Scientist, TCS Research, and Senior Scientist, TCS Research, respectively
Talk Title: A Conceptual Model of Systems Engineering
Series: INCOSE Speaker Series
Abstract: The Systems Science Working Group has started a new project to devise a conceptual model of systems that is based on key principles and concepts from systems science. This webinar presents an early draft of such a conceptual model. While systems engineering has strong empirical guidance in the form of practices, methods and standards, it is lacking theoretical foundation in comparison to other engineering disciplines. One approach to address this problem is based on the simple insight that solutions in typical engineering disciplines depend on knowledge in their own discipline, but creating a good systems engineering solution depends on bringing together knowledge from many disciplines. As a result, to create theoretical foundations for systems engineering, it is necessary to inquire into how knowledge domains come together in systems.
This inquiry led to a distinction between domains that carry knowledge about types of wholes vs. domains that carry knowledge about aspects. Theoretical knowledge is built up in aspect domains, while type domains carry knowledge about how various aspects come together in a whole, and how wholes relate to each other. In such a case, Systems engineering can be looked at in terms of four worlds: real, system models, types knowledge and aspects knowledge worlds. Creating an engineering solution involves using type world knowledge to synthesize various aspect solutions, using systems knowledge to ensure compositionality, implementing the solution in real world, and closing gaps between model and reality.
We are in the early stages of this exploration, but would appreciate anyone who would like to join us in this journey. Please contact us for more information.
Swaminathan Natarajan, swami.n@tcs.com
Anand Kumar, anand.ar@tcs.com
Subhrojyoti Roy Chaudhuri, subhrojyoti.c@tcs.com
Biography: Dr. Swaminathan Natarajan (Swami) is a Chief Scientist with Tata Consultancy Services Research and has more than 30 years of Industrial experience in Systems architecture, Software architecture and Engineering. He obtained his B.Tech from IIT Madras in 1983 and Ph.D. in computer science from the University of Illinois in 1989. His background includes teaching software engineering at Texas A&M University and Rochester Institute of Technology, as well as applied research positions with Xerox and Motorola India. His work with TCS has focused on systems research, engineering and architecture, including a role as control systems architect for the SKA radio telescope project. He is the editor of ISO 30103, a systems engineering standard on product quality achievement and co-chair of the INCOSE systems science working group.
Dr. Anand Kumar is a Senior Scientist with TCS Research and has more than 21 years of Industrial experience in Systems architecture, Software architecture and engineering. Anand is a member of the ISO JTC1 SC7 WG42 working group on architecture. He is the co-editor of ISO-IEC-IEEE 42020 standard on architecture processes and ISO-IEC-IEEE 42030 standard on architecture evaluation. Anand is the co-chair of INCOSE Architecture Working group, chair for INCOSE India Architecture working group and ISSS digital product-service systems working group. Anand has authored more than 40 papers in leading international journals and conferences. Anand has been granted 3 patents by US PTO and 2 patents by India PTO.
Subrojyoti Roy Chaudhuri (Subhro) is a Senior Scientist with TCS research and has around 20 years of experience. He has been instrumental in the research, design and development of many key capabilities produced by TCS such as MasterCraft and Ignio. Subhro represented the Indian team to participate and contribute in design and development of multiple international mega science projects such as the ITER and the Square Kilometer Array (SKA). He is a member of Telescope Manager (TM), an International Consortium and provider of one of the key capabilities of SKA. He currently leads the Telescope Management work package on behalf of the SKA TM consortium and is responsible for the overall design of the monitoring and control solution for SKA. His current area of research entails developing the architecture for the next generation of enabling platforms that would automate the realization of domain specific solutions utilizing robotics and IoT.
Host: International Council on Systems Engineering (INCOSE)
More Info: https://incoseevents.webex.com/mw3100/mywebex/default.do?nomenu=true&siteurl=incoseevents&service=6&rnd=0.5636237872535654&main_url=https%3A%2F%2Fincoseevents.webex.com%2Fec3100%2Feventcenter%2Fevent%2FeventAction.do%3FtheAction%3Ddetail%26%26%26EMK%3D483
Webcast: Event number 599 253 796, Event password INCOSE110Location: Online via WebEX
WebCast Link: Event number 599 253 796, Event password INCOSE110
Audiences: Everyone Is Invited
Contact: James Moore II
-
Automated Geometric Shape Deviation Modeling for Cyber-Physical Additive Manufacturing Systems via Bayesian Neural Networks
Wed, Mar 21, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Arman Sabbaghi, Purdue University
Talk Title: Automated Geometric Shape Deviation Modeling for Cyber-Physical Additive Manufacturing Systems via Bayesian Neural Networks
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: A significant challenge in dimensional accuracy control of a cyber-physical additive manufacturing (AM) system is the comprehensive specification of geometric shape deviation models for different computer-aided design (CAD) inputs on its constituent AM processes. Current deviation model building methods cannot satisfactorily address this challenge in practice because they are unable to leverage previously specified deviation models for different shapes and processes in an automated or rapid manner. We present a new model building methodology based on a class of Bayesian neural networks (NNs) that directly address the challenge of cyber-physical AM systems. Our framework enables automated and computationally efficient deviation modeling of different shapes and/or AM processes without sacrificing predictive accuracy, compared to existing modeling methods on the same samples of manufactured shapes. A fundamental innovation in our framework is the design of new and connectable NN structures that can leverage previously specified models for adaptive and principled model building. The power and broad scope of our method is demonstrated with several case studies on both in-plane and out-of-plane deviations for a wide variety of shapes manufactured under different stereolithography processes. Our Bayesian NN methodology for automated and comprehensive deviation modeling can ultimately be applied to advance fast, flexible, and high-quality manufacturing in a cyber-physical AM system. This talk is based on a paper written by Raquel De Souza Borges Ferreira, Dr. Arman Sabbaghi, and Dr. Qiang Huang.
Biography: Arman Sabbaghi is an Assistant Professor in the Department of Statistics at Purdue University. His research interests include model building for improved control of complex engineering systems, Bayesian data analysis, experimental design, causal inference, and statistical analysis with missing data.
Host: Prof. Paul Bogdan
More Information: sabbaghi-t.jpg
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
-
Aerospace and Mechanical Engineering Seminar
Wed, Mar 21, 2018 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Peter Hagedorn, Professor, Mechanical Engineering, Technische Universität Darmstadt, Germany
Talk Title: New Results on Self-Excitation in Circulatory and Parametrically Excited Systems
Abstract: In mechanical engineering systems, self-excited vibrations are in general unwanted and sometimes dangerous. There are many systems exhibiting self-excited vibrations which up to this day cannot be completely avoided, such as brake squeal, the galloping vibrations of overhead transmission lines, the ground resonance in helicopters and others. Most of these systems have in common that in the linearized equations of motion the self-excitation terms are given by non-conservative, circulatory forces and/or parametric excitation. The presentation will discuss some recent results in linear and nonlinear systems of this type.
Self-excited vibrations have of course been mathematically modelled and studied at least since the times of van der Pol. The van der Pol oscillator is a one degree of freedom system; its linearized equations of motion correspond to an oscillator with negative damping. Sometimes also other self-excited systems present negative damping, which can be made responsible for self-excited vibrations. In all the engineering systems mentioned above however, the self-excitation mechanism is mainly related to the interaction between different degrees of freedom (modes), and the linearized equations of motions contain circulatory terms. This together with parametric resonance is the main excitation mechanism discussed in this paper. Destabilization by 'negative damping' will not be considered. Also stick-slip phenomena are not in the focus of this presentation; they also do not seem to play an important role in all the examples given above.
The systems analyzed in this presentation therefore are characterized by the M, D, G, K, N matrices (mass, damping, gyroscopic, stiffness and circulatory matrices, respectively) which may all be time-dependent. In the unstable case, additional nonlinear terms do of course limit the vibration amplitudes. Different types of bifurcations relevant for these systems have recently been studied in the literature.
In the first part, MDGKN-systems with constant coefficients will be discussed. For a long time it has been well known, that the stability of such systems can be very sensitive to damping, and also to the symmetry properties of the mechanical structure. Recently, several new theorems were proved concerning the effect of damping on the stability and on the self-excited vibrations of the linearized systems. The importance of these results for practical mechanical engineering systems will be discussed. It turns out that the structure of the damping matrix is of utmost importance, and the common assumption, namely representing the damping matrix as a linear combination of the mass and the damping matrices, may give completely misleading results for the problem of instability and the onset of self-excited vibrations.
The second case considered deals with MDGKN-systems with time-periodic coefficients. The stability of these systems can be studied via Floquet theory. A typical property of parametric instability behavior is the existence of combination resonances. However, if parametric excitation in the system is simultaneously present in the K and the N matrices and/or there are excitation terms which are not all in phase, an atypical behavior may occur: The linear system may then for example be unstable for all frequencies of the parametric excitation, and not only in the neighborhood of certain discrete frequencies. Such atypical parametric instability happens even for M, D, G constant and zero mean values for the matrices K(t) and N(t). This was recently observed at the linearized equations of motion for a minimal model of a squealing disk brake. It turns out, that an even much simpler example of such a situation was given about 70 years ago by Lamberto Cesari, but seems to have fallen into oblivion. Until recently it was thought that such out of phase terms in the parametric excitation would not occur in engineering systems. In the presentation it is shown that they may indeed occur for example in the model of a squealing brake and probably in many other mechanical engineering systems, as long as there is slip with friction between solid bodies.
In the unstable case, additional nonlinear terms do of course limit the vibration amplitudes. Different types of bifurcations relevant for these systems are studied using normal form theory, in particular for the 'Cesari equations' with additional nonlinearities.
Host: Department of Aerospace and Mechanical Engineering
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
-
CAIS Seminar: Dr. Ian Holloway (UCLA) - Social Networking Site Data Mining to Understand Substance Use and HIV Risk Among Gay, Bisexual and Other Men Who Have Sex With Men
Wed, Mar 21, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Ian Holloway, UCLA
Talk Title: Social Networking Site Data Mining to Understand Substance Use and HIV Risk Among Gay, Bisexual and Other Men Who Have Sex With Men
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Dr. Holloway's presentation will outline the development of a culturally congruent data collection and mining module (DCMM) to study the social networking site (SNS) use patterns, substance use and HIV risk and protective behaviors of gay, bisexual and other men who have sex with men (MSM). Data gathered through the DCMM will be used to inform just-in-time adaptive interventions to prevent incidence of new HIV cases among this population disproportionately impacted by HIV/AIDS.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Holloway is an Assistant Professor in the Department of Welfare at the UCLA Luskin School of Public Affairs and the Director of the Southern California HIV/AIDS Policy Research Center. His applied behavioral health research examines the contextual factors that contribute to heath disparities among sexual and gender minority populations. Dr. Holloway is particularly interested in how social media and new technologies can be harnessed for health promotion and disease prevention. He holds dual master's degrees in social work and public health from Columbia University and a doctorate in social work from the University of Southern California.
Host: Milind Tambe
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Mark Bun (Princeton University) - Finding Structure in the Landscape of Differential Privacy
Wed, Mar 21, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mark Bun, Princeton University
Talk Title: Finding Structure in the Landscape of Differential Privacy
Series: CS Colloquium
Abstract: Differential privacy offers a mathematical framework for balancing two goals: obtaining useful information about sensitive data, and protecting individual-level privacy. Discovering the limitations of differential privacy yields insights as to what analyses are incompatible with privacy and why. These insights further aid the quest to discover optimal privacy-preserving algorithms. In this talk, I will give examples of how both follow from new understandings of the structure of differential privacy.
I will first describe negative results for private data analysis via a connection to cryptographic objects called fingerprinting codes. These results show that an (asymptotically) optimal way to solve natural high-dimensional tasks is to decompose them into many simpler tasks. In the second part of the talk, I will discuss concentrated differential privacy, a framework which enables more accurate analyses by precisely capturing how simpler tasks compose
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Mark Bun is a postdoctoral researcher in the Computer Science Department at Princeton University. He is broadly interested in theoretical computer science, and his research focuses on understanding foundational problems in data privacy through the lens of computational complexity theory. He completed his Ph.D. at Harvard in 2016, where he was advised by Salil Vadhan and supported by an NDSEG Research Fellowship.
Host: Aleksandra Korolova
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Biomedical Engineering Department Guest Speaker
Thu, Mar 22, 2018 @ 01:00 PM - 02:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Michael Economo, PhD,
Talk Title: TBA
Host: Ellis Meng, PhD
Location: Corwin D. Denney Research Center (DRB) - DRB 145/145A
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
EE Seminar: Programming Dynamic Behaviors in Molecular Systems and Materials
Thu, Mar 22, 2018 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Elisa Franco, Assistant Professor, University of California, Riverside
Talk Title: Programming Dynamic Behaviors in Molecular Systems and Materials
Abstract: Biological cells can adapt, replicate, and repair in ways that are unmatched by man-made devices. At the core of these complex behaviors are many dynamic processes that are difficult to deconstruct, and lack the modularity of electrical and mechanical systems. For example, shape adaptation in cells arises from the interplay of receptors, gene networks, and self-assembling cytoskeletal scaffolds. While the interplay of elements performing sensing, control, and actuation is apparent, it is not clear how to program similar behaviors in biological or synthetic matter using a minimal number of components and reactions. To address this general challenge, we follow a reductionist approach and we combine a systems-engineering theoretical analysis with experiments on nucleic acid systems. Nucleic acids are versatile molecules whose interactions and kinetic behaviors can be rationally designed from their sequence content; further, they are relevant in a number of native and engineered cellular pathways, as well as in biomedical and nanotechnology applications. I will illustrate our approach with two examples. The first is the construction of self-assembling DNA scaffolds that can be programmed to respond to environmental inputs and to canonical molecular signal generators such as pulse generators and oscillators. The second is the design of molecular feedback controllers to achieve homeostatic behavior and reference tracking. I will stress how mathematical modeling and control theory are essential to help identify design principles, to guide experiments, and to explain observed phenomena.
Biography: Elisa Franco is an Assistant Professor in Mechanical Engineering at UC Riverside. She received a Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2011. She also received a Ph.D. in Automation and a Laurea degree (cum laude) in Power Systems Engineering from the University of Trieste, Italy. Prof. Franco's main interests are in the areas of biological feedback and DNA nanotechnology: her research focuses on design, modeling, and synthesis of controllers and responsive materials using nucleic acids and proteins. She is the recipient of an NSF CAREER award and a Hellman Fellowship.
Host: Mihailo Jovanovic, mihailo@usc.edu and Alice Parker, parker@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
CS Colloquium: Holly Yanco (University of Massachusetts Lowell) - Designing for Human-Robot Interaction
Thu, Mar 22, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Holly Yanco, University of Massachusetts Lowell
Talk Title: Designing for Human-Robot Interaction
Series: Computer Science Colloquium
Abstract: Robots navigating in difficult and dynamic environments often need assistance from human operators or supervisors, either in the form of teleoperation or interventions when the robot's autonomy is not able to handle the current situation. Even in more controlled environments, such as office buildings and manufacturing floors, robots may need help from people. This talk will discuss methods for controlling both individual robots and groups of robots, in applications ranging from assistive technology to telepresence to search and rescue. A variety of modalities for human-robot interaction with robot systems, including multi-touch devices, software-based operator control units (softOCUs), game controllers, virtual reality headsets, and Google Glass, will be presented.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Holly Yanco is a Distinguished University Professor, Professor of Computer Science, and Director of the New England Robotics Validation and Experimentation (NERVE) Center at the University of Massachusetts Lowell. Her research interests include human-robot interaction, multi-touch computing, robot autonomy, fostering trust of autonomous systems, evaluation methods for robot systems, and the use of robots in K-12 education to broaden participation in computer science. Yanco's research has been funded by NSF, including a CAREER Award, ARO, DARPA, DOE-EM, NASA, NIST, Microsoft, and Google. Yanco is Co-Chair of the Massachusetts Technology Leadership Council's Robotics Cluster,served as Co-Chair of the Steering Committee for the ACM/IEEE Conference on Human-Robot Interaction and Journal of Human-Robot Interaction from 2013-2016, and was a member of the Executive Council of the Association for the Advancement of Artificial Intelligence (AAAI) from 2006-2009. Yanco has a PhD in Computer Science from the Massachusetts Institute of Technology.
Host: Maja Mataric
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
Astani Civil and Environmental Engineering Seminar
Fri, Mar 23, 2018 @ 03:00 AM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Omid Davtalab and Measrainey Meng , Astani CEE Ph.D. Students
Talk Title: High-resolution integration of water, energy, and climate models to assess electricity grid vulnerabilities to climate change
Abstract: See attached abstracts
More Information: Seminar Announcement 3_23_18.pdf
Location: Ray R. Irani Hall (RRI) - 101
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
-
W.V.T. RUSCH ENGINEERING HONORS COLLOQUIUM
Fri, Mar 23, 2018 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Martin Meisler, Sustainability Consultant, Senior Environmental Specialist for Metropolitan Water District, and Founding Member of BiomimicryLA, Metropolitan Water District
Talk Title: How Would Nature Solve That Problem?
Host: Dr. Prata & EHP
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Su Stevens
-
EE-EP Faculty Candidate - Limei Tian, Friday, March 23rd @ 2pm in EEB 132
Fri, Mar 23, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Limei Tian, University of Illinois at Urbana-Champaign
Talk Title: Epidermal Electronics and Bioplasmonics for Advanced Health Care
Abstract: Remarkable advances in the design and fabrication of soft, flexible electronics over the past decade form the basis of novel classes of skin-interfaced wearable medical devices capable of continuously measuring and wirelessly transmitting biophysical and biochemical information. These new systems are expected to revolutionize healthcare by improving outcomes and reducing costs, as they become integral parts of modern, connected medical infrastructure. In this talk, I will discuss the recent advances in materials, mechanics and manufacturing approaches of such systems designed for electrophysiology and thermophysiology. I will show that large-area, skin-like electrical interfaces enable, via advanced pattern recognition algorithms, control of robotic prosthesis with sensory feedback provided by electrical stimulation. These platforms are also magnetic resonance imaging (MRI)-compatible, thereby allowing for the simultaneous measurements of electroencephalography (EEG) and functional MRI.
In the second part of the talk, I will discuss design and implementation of plasmonic biosensors for simple, portable, sensitive, on-chip biodiagnostics in point-of-care and resource-limited settings. While there has been a tremendous progress in the rational design of plasmonic nanotransducers with high sensitivity and the development of hand-held read-out devices, the translation of these biosensors to resource-limited settings is hindered by the poor thermal, chemical, and environmental stability of the biorecognition elements. Degradation of the sensitive reagents and biodiagnostic chips compromises analytical validity, preventing accurate and timely diagnosis. I will present a novel class of plasmonic biosensors that rely artificial antibodies as recognition elements with excellent thermal and chemical stability. Finally, I will discuss my future research efforts in wearable and implantable electronics to facilitate accurate disease diagnosis and personalized medicine.
Biography: Limei Tian is currently a Beckman Institute Postdoctoral Fellow at the University of Illinois at Urbana-Champaign. She earned her Ph.D. from the Department of Mechanical Engineering and Materials Science at Washington University in St. Louis in 2014. Her research interests include the design, synthesis and fabrication of novel materials and devices, which can expand the fundamental understanding of biotic-abiotic interactions at various length scales and foster technologies that enable advanced health care, renewable energy, environmental monitoring and homeland security. She is the recipient of National Science Foundation summer institute fellowship (2011), Materials Research Society graduate student award (2013), Chinese Government Award for outstanding students abroad (2014) and Beckman Institute Postdoctoral Fellowship (2015).
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
CS Colloquium: Himabindu Lakkaraju (Stanford University) Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making
Mon, Mar 26, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Himabindu Lakkaraju, Stanford University
Talk Title: Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making
Series: CS Colloquium
Abstract: Domains such as law, healthcare, and public policy often involve highly consequential decisions which are predominantly made by human decision-makers. The growing availability of data pertaining to such decisions offers an unprecedented opportunity to develop machine learning models which can aid human decision-makers in making better decisions. However, the applicability of machine learning to the aforementioned domains is limited by certain fundamental challenges:
1) The data is selectively labeled i.e., we only observe the outcomes of the decisions made by human decision-makers and not the counterfactuals.
2) The data is prone to a variety of selection biases and confounding effects.
3) The successful adoption of the models that we develop depends on how well decision-makers can understand and trust their functionality, however, most of the existing machine learning models are primarily optimized for predictive accuracy and are not very interpretable.
In this talk, I will describe novel computational frameworks which address the aforementioned challenges, thus, paving the way for large-scale deployment of machine learning models to address problems of significant societal impact. First, I will discuss how to build interpretable predictive models and explanations of complex black box models which can be readily understood and consequently trusted by human decision-makers. I will then outline efficient and provably near-optimal approximation algorithms to solve these problems. Next, I will present a novel evaluation framework which allows us to reliably compare the quality of decisions made by human decision-makers and machine learning models amidst challenges such as missing counterfactuals and presence of unmeasured confounders (unobservables). Lastly, I will provide a brief overview of my research on diagnosing and characterizing biases (systematic errors) in human decisions and predictions of machine learning models.
I will conclude the talk by sketching future directions which enable effective and efficient collaboration between humans and machine learning models to address problems of societal impact.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Hima Lakkaraju is a Ph.D. candidate in Computer Science at Stanford University. Her research focuses on enabling machine learning models to complement human decision making in high-stakes settings such as law, healthcare, public policy, and education. At the core of her research lie rigorous computational techniques leading to algorithmic contributions in machine learning, data mining, and econometrics. Hima has received several fellowships and awards including the Robert Bosch Stanford graduate fellowship, Microsoft research dissertation grant, Google Anita Borg scholarship, IBM eminence and excellence award, and best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. Her research has been covered by various media outlets such as the New York Times, MIT Tech Review, Harvard Business Review, TIME, Forbes, Business Insider, and Bloomberg.
Host: Aleksandra Korolova
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
EE-EP Faculty Candidate, Wei Bao, Monday, March 26th @12pm in EEB 132
Mon, Mar 26, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Wei Bao, University of California, Berkeley
Talk Title: Interacting Light with Semiconductor at the Nanoscale
Abstract: The ability to probe and control light-matter interaction at the nanometer scale not only advances frontiers of fundamental science, but also is a critical prerequisite to device applications in electronics, sensing, catalysis, energy harvesting, and more. Exploiting and enhancing the originally weak light-matter interactions via nanofabricated photonic structures; we will be able to sense chemical species at single molecule levels, to devise better imaging and manufacturing tools, to transfer data more efficiently at higher speed.
In this talk, I will first describe a simple and general nano-optical device developed during my Ph.D., called campanile probe, which lay groundwork for generally-applicable nano-optical studies. Two examples will be discussed, where we cross the boundary from insufficient to sufficient resolution beyond optical diffraction limit and perform optical hyperspectral imaging of luminescence heterogeneity along InP nanowires and synthetic monolayer MoS2, providing spectral information distinct from diffraction limited micro-PL spectral imaging. Following this, I will discuss the recent works using cavities to further enhance the strength of light-matter interaction into the strong coupling regime. The formation of coherently coupled cavity exciton-polariton in two-dimensional monolayer WS2 and the inorganic perovskite CsPbBr3 as well as the ultralow threshold optically pumped polariton lasing in perovskite cavities will be shown. Finally, I will conclude by presenting my vision of how these devices can enable a wide range of capabilities with relevance to multidimensional spectroscopy imaging, efficient solid-state lighting and even beyond.
Biography: Dr. Wei Bao is a postdoctoral researcher in Prof. Xiang Zhang's lab at the University of California, Berkeley. Previously he earned his B.A. in Physics (minor in Chemistry) at Peking University in 2009, and his M.S. in Mechanical Engineering (minor in Electrical Engineering) at UCLA in 2010. Wei then received his Ph.D. in Materials Science and Engineering (minor in Electrical Engineering) at University of California, Berkeley under the supervision of Prof. Miquel Salmeron and Prof. P. James Schuck in 2015. His Ph.D. work in nanoscale spectroscopic investigations of optoelectronic has led to several awards including: MRS Graduate Student Gold Award, Dorothy M. and Earl S. Hoffman Scholarships, Ross N. Tucker Memorial Award, as well as a R&D 100 Award 2013. His postdoc research currently focuses on polaritonics lasing devices, a scientific direction at the interface between low-dimensional semiconductor nanophotonics and quantum physics.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Biomedical Engineering Seminars
Mon, Mar 26, 2018 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Talk Title: TBA
Host: Professor Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
-
Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Mar 26, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: B. Ross Barmish, University of Wisconsin, Madison
Talk Title: From the Kelly-Shannon Collaboration to Stock Trading Based on Feedback Control
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: This talk begins with a description of some ideas related to gambling which originated at Bell Labs in the 1950s by John Kelly and Claude Shannon. With their work serving as motivation for this talk, I will provide an overview of my research on the development of new stock-trading algorithms. The most salient feature of my approach is that no model of any sort is used for the underlying stock-price dynamics. Instead, in the spirit of technical analysis, the size of the time-varying stock position is determined using some simple ideas involving the adaptive power of feedback control loops. This approach is said to be "reactive" rather than predictive and amounts to assigning high priority to sound money management. After the key ideas driving this research are explained, the back-testing of the trading algorithms using historical data will be addressed with attention paid to practical considerations such as transaction costs, leverage and margin. It is interesting to note that sometimes the simulations lead to unexpected results which were not contemplated during the course of the research.
Biography: B. Ross Barmish is Professor of Electrical and Computer Engineering at the University of Wisconsin, Madison. Prior to joining UW in 1984, he held faculty positions at Yale University and the University of Rochester. From 2001-2003, he served as Chair of the EECS Department at Case Western Reserve while holding the Nord endowed professorship. He received his Bachelor's degree in EE from McGill University and the M.S. and Ph.D. degrees, also in EE, from Cornell University.
Throughout his career, he has served the IEEE Control Systems Society in many capacities and has been a consultant for a number of companies. Professor Barmish is the author of the textbook ``New Tools for Robustness of Linear Systems'' and is a Fellow of both the IEEE and IFAC for his contributions to robust control. He received two Best Journal Publication awards, each covering a three-year period, from the International Federation of Automatic Control and has given a number of keynotes and plenary lectures at major conferences. In~2013, he received the IEEE Control Systems Society Bode Prize.
While his earlier work concentrated on robustness of dynamical systems, his current university research involves building a bridge between feedback control theory and trading in complex financial markets. In addition to this academic pursuit, in his capacity as CEO of Robust Trading Solutions, his work involves transition of stock-trading algorithms from theory to practice and government sponsored research on the NASDAQ Limit Order Book.
Host: Petros Ioannou, ioannou@usc.edu
More Information: barmish.jpg (JPEG Image, 411 × 568 pixels).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
-
EE Seminar: Enabling Optical Methods for Next-Generation Neural Prostheses
Mon, Mar 26, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Andrea Giovannucci, Research Scientist, Flatiron Institute, Simons Foundation
Talk Title: Enabling Optical Methods for Next-Generation Neural Prostheses
Abstract: Optical methods present interesting new opportunities for brain computer interfaces (BCIs) and closed-loop experiments because of their capability to densely monitor and stimulate in-vivo large neural populations across weeks with single cell resolution. For instance, combining optical methods for recording (two-photon imaging of calcium indicators) and perturbing (optogenetics) neural ensembles opens the door to exciting closed-loop experiments, where the stimulation pattern can be determined based on the recorded activity and/or the behavioral state. However, the adoption of such tools for BCIs is currently hindered by the lack of algorithms that track neural activity in real-time. In a typical closed-loop experiment, the monitored/perturbed regions of interest (ROIs) have been preselected by analyzing offline a previous dataset from the same field of view. Monitoring the activity of a ROI, which usually corresponds to a soma, typically entails averaging the fluorescence over the corresponding ROI, resulting in a signal that is only a proxy for the actual neural activity and which can be sensitive to motion artifacts and drifts, as well as spatially overlapping sources, background/neuropil contamination, and noise. Furthermore, by preselecting the ROIs, the experimenter is unable to detect and incorporate new sources that become active later during the experiment or track changes in neuronal morphology, which prevents the execution of truly closed-loop experiments.
In the first portion of this talk I will present an Online, single-pass, algorithmic framework for the Analysis of Calcium Imaging Data (OnACID). The framework is highly scalable with minimal memory requirements, as it processes the data in a streaming fashion one frame at a time, while keeping in memory a set of low dimensional sufficient statistics and a small minibatch of the last data frames. Every frame is processed in four sequential steps: i) The frame is registered against the previous denoised (and registered) frame to correct for motion artifacts. ii) The fluorescence activity of the already detected sources is tracked. iii) Newly appearing neurons are detected and incorporated to the set of existing sources. iv) The fluorescence trace of each source is denoised and deconvolved to provide an estimate of the underlying spiking activity. I will present the results of applying OnACID to several large-scale (90-350GB) mouse and zebrafish larvae in-vivo datasets. OnAcid can find and track tens of thousands of neurons faster than real-time, and outperforms state of the art algorithms benchmarked on multiple manual annotations using a precision-recall framework.
In the second portion of the talk, I will present an application of brain optical imaging to unveil coding properties and feedback mechanisms implemented by neurons in the cerebellum, a brain area implied in motor control and in the production of agile movement sequences. By monitoring across days the same neuronal populations of mice undergoing associative learning I will show that a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.
In the last section of the talk, I will discuss my plans to develop all-optical neural prostheses interfacing with the cerebellum to recover lost motor function in the central nervous system because of injury or disease.
Biography: Andrea Giovannucci has a Ph.D. in computer science from Universitat Autònoma de Barcelona in Spain and a B.S. in electrical engineering from Politecnico di Milano in Italy. From 2008 to 2010 he was a postdoctoral fellow at Pompeu Fabra University (Barcelona), where he developed signal processing algorithms and circuit models for neuroprosthetic applications. From 2010 to 2015 he completed a postdoctoral fellowship at the Princeton Neuroscience Institute (PNI), Princeton University. At PNI, he pioneered the use of genetically encoded calcium indicators to image neurons in the cerebellum of awake learning mice, and applied them to investigate coding properties of cerebellar neurons during motor learning. Since 2015 Andrea Giovannucci is a research scientist at the Flatiron Institute, Simons Foundation, where he develops algorithms for the analysis of calcium imaging data, general-purpose neural networks and data-intensive computing projects. Dr. Giovannucci was the recipient of the First Prize for the Best Agent Service or Application in the Agent Technology Competition (IST Agentcities.net) in 2003, was shortlisted for the best Ph.D. thesis in artificial intelligence (ECCAI), and was the recipient of the prestigious Juan de La Cierva (Spain) and New Jersey Commission on Brain Injury Research (USA) fellowships. Andrea Giovannucci is the leader developer of the CaImAn open source software platform for calcium imaging analysis, currently used by hundreds of research laboratories worldwide.
Host: Maryam Shanechi, shanechi@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
EE Seminar: Statistical and Formal Methods in Hardware Security
Tue, Mar 27, 2018 @ 10:30 AM - 11:45 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yiorgos Makris, Professor, ECE Department, The University of Texas at Dallas
Talk Title: Statistical and Formal Methods in Hardware Security
Abstract: Partly because of design outsourcing and migration of fabrication to low-cost areas around the globe, and partly because of increased reliance on third-party intellectual property, the integrated circuit (IC) supply chain is now considered far more vulnerable than ever before. With electronics ubiquitously deployed in sensitive domains and critical infrastructure, such as wireless communications, industrial environments, as well as health, financial and military applications, understanding the corresponding risks and developing appropriate remedies have become paramount. To this end, in this presentation I will discuss the role that statistical and formal methods can play in ensuring security and trustworthiness of ICs and the systems wherein they are deployed, and I will introduce two solutions that my research group has contributed to the area of hardware security.
The first contribution, known as Statistical Side-Channel Fingerprinting, is a statistical method for assessing whether an integrated circuit originates from a known distribution or not, based on parametric measurements such as delay, power, electromagnetic emanations, temperature, etc. Effectiveness of this method in detecting ICs which have been subjected to malicious modifications (a.k.a. hardware Trojans) will be demonstrated using silicon measurements from a custom-designed wireless cryptographic IC. Solutions to the main challenges of statistical side-channel fingerprinting, namely the availability of a statistically significant trusted population and the detection of hardware Trojans which are activated after deployment, will also be discussed and demonstrated in silicon.
The second contribution, known as Proof-Carrying Hardware Intellectual Property, is a formal method for proving compliance of an electronic design acquired from a third-party vendor with a set of security properties. These properties, which are expressed as theorems with corresponding proofs in a formal proof management system (i.e., Coq) and which can be automatically checked by the consumer, outline the boundaries of trusted operation without necessarily specifying the exact functionality of the design. Effectiveness of this method in certifying secure instruction execution will be demonstrated on a popular microcontroller and its utility for data secrecy protection through fully-automated information flow tracking will be demonstrated on a cryptographic core.
I will conclude by revisiting the modus operandi of the hardware security research area as it enters its second decade of activity and I will emphasize the need for (i) intensified efforts towards statistical and formal methods which can offer risk bounds and provable security, and (ii) synergy platforms whereby hardware security can be seamlessly integrated with software security, network security and cryptography, towards developing holistic system-level solutions for both contemporary and emerging applications. In this context, I will also briefly review our recent efforts in mixed-signal and system-level proof-carrying hardware, covert wireless communications, machine learning-based malware detection and workload forensics, as well as in establishing an NSF Industry/University Cooperative Research Center on Hardware and Embedded System Security and Trust (CHEST).
Biography: Yiorgos is a professor of Electrical and Computer Engineering at The University of Texas at Dallas, where he leads the Trusted and RELiable Architectures (TRELA) Research Laboratory. Prior to joining UT Dallas in 2011, he spent a decade as a faculty of Electrical Engineering and of Computer Science at Yale University. He holds a Ph.D. (2001) and an M.S. (1997) in Computer Engineering from the University of California, San Diego, and a Diploma of Computer Engineering and Informatics (1995) from the University of Patras, Greece. His main research interests are in the application of formal and machine learning-based methods in the design of trusted and reliable integrated circuits and systems, with particular emphasis in the analog/RF domain. He is also investigating hardware-based malware detection, forensics and reliability methods in modern microprocessors, as well as on-die learning and novel computational modalities using emerging technologies. His research activities have been supported by NSF, SRC, ARO, AFRL, DARPA, Boeing, IBM, LSI, Intel, Advantest, AMS and TI. Yiorgos is as an associate editor of the IEEE Transactions on Information Forensics and Security, the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the IEEE Design & Test periodical and the Springer Journal of Electronic Testing: Theory and Applications. He served as the 2016-2017 general chair and the 2013-2014 program chair of the IEEE VLSI Test Symposium, and as a topic coordinator and/or program committee member for several IEEE and ACM conferences. He is a Senior Member of the IEEE, a recipient of the 2006 Sheffield Distinguished Teaching Award and a recipient of the Best Paper Award from the 2013 Design Automation and Test in Europe (DATE'13) conference and the 2015 VLSI Test Symposium (VTS'15).
Host: Peter Beerel, beerel@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
CS Colloquium: Stefanos Nikolaidis (Carnegie Mellon University) - Mathematical Models of Adaptation in Human-Robot Collaboration
Tue, Mar 27, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Stefanos Nikolaidis, Carnegie Mellon University
Talk Title: Mathematical Models of Adaptation in Human-Robot Collaboration
Series: CS Colloquium
Abstract: The goal of my research is to improve human-robot collaboration by integrating mathematical models of human behavior into robot decision making. I develop game-theoretic algorithms and probabilistic planning techniques that reason over the uncertainty in the human internal state and its dynamics, enabling autonomous systems to act optimally in a variety of real-world collaborative settings.
While much work in human-robot interaction has focused on leader-assistant teamwork models, the recent advancement of robotic systems that have access to vast amounts of information suggests the need for robots that take into account the quality of the human decision making and actively guide people towards better ways of doing their task. In this talk, I propose an equal partners model, where human and robot engage in a dance of inference and action, and I focus on one particular instance of this dance: the robot adapts its own actions via estimating the probability of the human adapting to the robot. I start with a bounded memory model of human adaptation parameterized by the human adaptability - the probability of the human switching towards a strategy newly demonstrated by the robot. I then propose data-driven models that capture subtler forms of adaptation, where the human teammate updates their expectations of the robot's capabilities through interaction. Integrating these models into robot decision making allows for human-robot mutual adaptation, where coordination strategies, informative actions and trustworthy behavior are not explicitly modeled, but naturally emerge out of optimization processes. Human subjects experiments in a variety of collaboration and shared autonomy settings show that mutual adaptation significantly improves human-robot team performance, compared to one-way robot adaptation to the human.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Stefanos Nikolaidis completed his PhD at Carnegie Mellon's Robotics Institute in December 2017 and he is currently a research associate at the University of Washington, Computer Science & Engineering. His research lies at the intersection of human-robot interaction, algorithmic game-theory and planning under uncertainty. Stefanos develops decision making algorithms that leverage mathematical models of human behavior to support deployed robotic systems in real-world collaborative settings. He has a MS from MIT, a MEng from the University of Tokyo and a BS from the National Technical University of Athens. He has additionally worked as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction, has a best paper nomination from the same conference this year and was a best paper award finalist in the International Symposium on Robotics.
Host: Joseph Lim
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Epstein Institute Seminar, ISE 651
Tue, Mar 27, 2018 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Fernando Ordonez, Professor, University of Chile
Talk Title: Solving Stackelberg Equilibrium in Stochastic Games
Host: Prof. Maged Dessouky
More Information: March 27, 2018.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
-
EE Seminar: Statistical Interference of Properties of Distribution: Theory, Algorithms, and Applications
Wed, Mar 28, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jiantao Jiao, Stanford University
Talk Title: Statistical Interference of Properties of Distribution: Theory, Algorithms, and Applications
Abstract: Modern data science applications - ranging from graphical model learning to image registration to inference of gene regulatory networks - frequently involve pipelines of exploratory analysis requiring accurate inference of a property of the distribution governing the data rather than the distribution itself. Notable examples of properties include Shannon entropy, mutual information, Kullback-Leibler divergence, and total variation distance, among others.
This talk will focus on recent progress in the performance, structure, and deployment of near-minimax-optimal estimators for a large variety of properties in high-dimensional and nonparametric settings. We present general methods for constructing information theoretically near-optimal estimators, and identify the corresponding limits in terms of the parameter dimension, the mixing rate (for processes with memory), and smoothness of the underlying density (in the nonparametric setting). We employ our schemes on the Google 1 Billion Word Dataset to estimate the fundamental limit of perplexity in language modeling, and to improve graphical model and classification tree learning. The estimators are efficiently computable and exhibit a "sample size boosting" phenomenon, i.e., they attain with n samples what prior methods would have needed n log(n) samples to achieve.
Biography: Jiantao Jiao is a Ph.D. student in the Department of Electrical Engineering at Stanford University. He received the B.Eng. degree in Electronic Engineering from Tsinghua University, Beijing, China in 2012, and the M.Eng. degree in Electrical Engineering from Stanford University in 2014. He is a recipient of the Presidential Award of Tsinghua University and the Stanford Graduate Fellowship. He was a semi-plenary speaker at ISIT 2015 and a co-recipient of the ISITA 2016 Student Paper Award. He co-designed and co-taught the graduate course EE378A (Statistical Signal Processing) at Stanford University in 2016 and 2017, with his advisor Tsachy Weissman. His research interests are in statistical machine learning, high-dimensional and nonparametric statistics, information theory, and their applications in medical imaging, genomics, and natural language processing. He is a co-founder of Qingfan (www.qingfan.com), an online platform that democratizes technical training and job opportunities for anyone with access to the internet.
Host: Salman Avestimehr, avestimehr@gmail.com
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
Aerospace and Mechanical Engineering Seminar
Wed, Mar 28, 2018 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: David Lentink, Assistant Professor, Department of Mechanical Engineering, Stanford University
Talk Title: Avian Inspired Design
Abstract: Many organisms fly in order to survive and reproduce. My lab focusses on understanding bird flight to improve flying robots - because birds fly further, longer, and more reliable in complex visual and wind environments. I use this multidisciplinary lens that integrates biomechanics, aerodynamics, and robotics to advance our understanding of the evolution of flight more generally across birds, bats, insects, and autorotating seeds. The development of flying organisms as an individual and their evolution as a species are shaped by the physical interaction between organism and surrounding air. The organism's architecture is tuned for propelling itself and controlling its motion. Flying animals and plants maximize performance by generating and manipulating vortices. These vortices are created close to the body as it is driven by the action of muscles or gravity, then are 'shed' to form a wake (a trackway left behind in the fluid). I study how the organism's architecture is tuned to utilize these and other aeromechanical principles to compare the function of bird wings to that of bat, insect, and maple seed wings. The experimental approaches range from making robotic models to training birds to fly in a custom-designed wind tunnel as well as in visual flight arenas - and inventing methods to 3D scan birds and measure the aerodynamic force they generate - nonintrusively - with a novel aerodynamic force platform. The studies reveal that animals and plants have converged upon the same solution for generating high lift: A strong vortex that runs parallel to the leading edge of the wing, which it sucks upward. Why this vortex remains stably attached to flapping animal and spinning plant wings is elucidated and linked to kinematics and wing morphology. While wing morphology is quite rigid in insects and maple seeds, it is extremely fluid in birds. I will show how such 'wing morphing' significantly expands the performance envelope of birds during flight, and will dissect the mechanisms that enable birds to morph better than any aircraft can. Finally, I will show how these findings have inspired my students to design new flapping and morphing aerial robots.
Biography: Professor Lentink's multidisciplinary lab studies biological flight, in particular bird flight, as an inspiration for engineering design. http://lentinklab.stanford.edu He has a BS and MS in Aerospace Engineering (Aerodynamics, Delft University of Technology) and a PhD in Experimental Zoology cum laude (Wageningen University). During his PhD he visited the California institute of Technology for 9 months to study insect flight. His postdoctoral training at Harvard was focused on studying birds. Publications range from technical journals to cover publications in Nature and Science. He is an alumnus of the Young Academy of the Royal Netherlands Academy of Arts and Sciences, recipient of the Dutch Academic Year Prize, the NSF CAREER award and he has been recognized in 2013 as one of 40 scientists under 40 by the World Economic Forum.
Host: Department of Aerospace and Mechanical Engineering
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Ashleen Knutsen
-
CAIS Seminar: Dr. Mayank Kejriwal (USC Information Sciences Institute) - Building Knowledge Graphs for Social Good
Wed, Mar 28, 2018 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Mayank Kejriwal, USC Information Sciences Institute
Talk Title: Building Knowledge Graphs for Social Good
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Illicit activities like human trafficking and narcotics have a significant Web footprint. In this talk, I will introduce and talk about building knowledge graphs (KG), a powerful means of representing and reasoning over knowledge using intelligent algorithms, to combat such problems for social good. I will also introduce a KG-centric system called DIG, developed in our group, that is currently being used by more than 100 US law enforcement agencies to combat human trafficking.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Mayank Kejriwal is a researcher at the USC Information Sciences Institute. His research on knowledge graphs, currently funded under both DARPA and IARPA, has been published in multiple interdisciplinary ACM, IEEE, Springer and Elsevier venues. He is authoring a textbook on knowledge graphs (MIT Press) with Pedro Szekely and Craig Knoblock.
Host: Milind Tambe
Location: Mark Taper Hall Of Humanities (THH) - 102
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Junier Oliva (Carnegie Mellon University) Scalable Learning Over Distributions
Thu, Mar 29, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Junier Oliva, Carnegie Mellon University
Talk Title: Scalable Learning Over Distributions
Series: CS Colloquium
Abstract: A great deal of attention has been applied to studying new and better ways to perform learning tasks involving static finite vectors. Indeed, over the past century the fields of statistics and machine learning have amassed a vast understanding of various learning tasks like clustering, classification, and regression using simple real valued vectors. However, we do not live in a world of simple objects. From the contact lists we keep, the sound waves we hear, and the distribution of cells we have, complex objects such as sets, distributions, sequences, and functions are all around us. Furthermore, with ever-increasing data collection capacities at our disposal, not only are we collecting more data, but richer and more bountiful complex data are becoming the norm.
In this presentation we analyze regression problems where input covariates, and possibly output responses, are probability distribution functions from a nonparametric function class. Such problems cover a large range of interesting applications including learning the dynamics of cosmological particles and general tasks like parameter estimation.
However, previous nonparametric estimators for functional regression problems scale badly computationally with the number of input/output pairs in a data-set. Yet, given the complexity of distributional data it may be necessary to consider large data-sets in order to achieve a low estimation risk.
To address this issue, we present two novel scalable nonparametric estimators: the Double-Basis Estimator (2BE) for distribution-to-real regression problems; and the Triple-Basis Estimator (3BE) for distribution-to-distribution regression problems. Both the 2BE and 3BE can scale to massive data-sets. We show an improvement of several orders of magnitude in terms of prediction speed and a reduction in error over previous estimators in various synthetic and real-world data-sets.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Junier Oliva is a Ph.D. candidate in the Machine Learning Department at the School of Computer Science, Carnegie Mellon University. His main research interest is to build algorithms that understand data at an aggregate, holistic level. Currently, he is working to push machine learning past the realm of operating over static finite vectors, and start reasoning ubiquitously with complex, dynamic collections like sets and sequences. Moreover, he is interested in exporting concepts from learning on distributional and functional inputs to modern techniques in deep learning, and vice-versa. He is also developing methods for analyzing massive datasets, both in terms of instances and covariates. Prior to beginning his Ph.D. program, he received his B.S. and M.S. in Computer Science from Carnegie Mellon University. He also spent a year as a software engineer for Yahoo!, and a summer as a machine learning intern at Uber ATG.
Host: Fei Sha
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
EE Seminar: Innovating Secure IoT Solutions for Extreme Environments
Thu, Mar 29, 2018 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rabia Yazicigil, Massachusetts Institute of Technology
Talk Title: Innovating Secure IoT Solutions for Extreme Environments
Abstract: The Internet of Things (IoT) is redefining how we interact with the world by supplying a global view based not only on human-provided data but also human-device connected data. For example, in Health Care, IoT will bring decreased costs, improved treatment results, and better disease management. However, the connectivity-in-everything model brings heightened security concerns. Additionally, the projected growth of connected nodes not only increases security concerns, it also leads to a 1000-fold increase in wireless data traffic in the near future. This data storm results in a spectrum scarcity thereby driving the urgent need for shared spectrum access technologies. These security deficiencies and the wireless spectrum crunch require innovative system-level secure and scalable solutions.
This talk will introduce energy-efficient and application-driven system-level solutions for secure and spectrum-aware wireless communications. I will present a novel ultra-fast bit-level frequency-hopping scheme for physical-layer security. This scheme utilizes the frequency agility of devices in combination with novel radio frequency architectures and protocols to achieve secure wireless communications. To address the wireless spectrum crunch, future smart radio systems will evaluate the spectrum usage dynamically and opportunistically use the underutilized spectrum; this will require spectrum sensing for interferer avoidance. I will discuss a system-level approach using band-pass sparse signal processing for rapid interferer detection in a wideband spectrum to convert the abstract improvements promised by sparse signal processing theory, e.g., fewer measurements, to concrete improvements in time and energy efficiency.
The tightly-coupled system solutions derived at the intersection of electronics, security, signal processing, and communications extend in applications beyond the examples provided here, enabling innovative IoT solutions for extreme environments.
Biography: Rabia Yazicigil is currently a Postdoctoral Associate at MIT. She received her PhD degree in Electrical Engineering from Columbia University in 2016. She received the B.S. degree in Electronics Engineering from Sabanci University, Istanbul, Turkey in 2009, and the M.S. degree in Electrical and Electronics Engineering from Ãcole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland in 2011.
Her research interest lies at the interface of electronics, security, signal processing and communication to innovate system-level solutions for future energy-constrained Internet of Things applications. She has been a recipient of a number of awards, including the "Electrical Engineering Collaborative Research Award" for her PhD research on Compressive Sampling Applications in Rapid RF Spectrum Sensing (2016), the second place at the Bell Labs Future X Days Student Research Competition (2015), Analog Devices Inc. outstanding student designer award (2015) and 2014 Millman Teaching Assistant Award of Columbia University. She was selected among the top 61 female graduate students and postdoctoral scholars invited to participate and present her research work in the 2015 MIT Rising Stars in Electrical Engineering Computer Science.
Host: Peter Beerel, pabeerel@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
-
CS Distinguished Lecture: Satinder Singh (University of Michigan) – Reinforcement Learning: From Vision to Action and Back
Thu, Mar 29, 2018 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Satinder Singh, University of Michigan
Talk Title: Reinforcement Learning: From Vision to Action and Back
Series: Computer Science Distinguished Lecture Series
Abstract: Stemming in part from the great successes of other areas of Machine Learning, in particular the recent success of Deep Learning, there is renewed hope and interest in Reinforcement Learning (RL) from the wider applications communities. Indeed, there is a recent burst of new and exciting progress in both theory and practice of RL. I will describe some theoretical results from my own group on a simple new connection between planning horizon and overfitting in RL, as well as some results on combining RL with Deep Learning in Minecraft, and Zero-Shot Generalization across compositional tasks. I will conclude with some lookahead at what we can do, both as theoreticians and those that collect data, to accelerate the impact of RL.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Satinder Singh is a Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He has been the Chief Scientist at Syntek Capital, a venture capital company, a Principal Research Scientist at AT&T Labs, an Assistant Professor of Computer Science at the University of Colorado, Boulder, and a Postdoctoral Fellow at MIT's Brain and Cognitive Science department. His research focus is on developing the theory, algorithms and practice of building artificial agents that can learn from interaction in complex, dynamic, and uncertain environments, including environments with other agents in them. His main contributions have been to the areas of reinforcement learning, multi-agent learning, and more recently to applications in cognitive science and healthcare. He is a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence) and has coauthored more than 150 refereed papers in journals and conferences and has served on many program committee's. He was Program-CoChair of AAAI 2017, and in 2013 helped cofound RLDM (Reinforcement Learning and Decision Making), a biennial multidisciplinary meeting that brings together computer scientists, psychologists, neuroscientists, roboticists, control theorists, and others interested in animal and artificial decision making.
Host: Haipeng Luo
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
W.V.T. RUSCH ENGINEERING HONORS COLLOQUIUM
Fri, Mar 30, 2018 @ 01:00 PM - 01:50 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Lily Lai, Associate Clinical Professor of Surgery, City of Hope
Talk Title: Utilization of Engineering in Cancer Care
Host: Dr. Prata & EHP
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Su Stevens
-
EE-EP Faculty Candidate - Mercedeh Khajavikhan, Friday, March 30th @ 2pm in EEB 132
Fri, Mar 30, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mercedeh Khajavikhan, University of Central Florida
Talk Title: Non-Hermitian Photonics: Optics at an Exceptional Point
Abstract: In recent years, non-Hermitian degeneracies, also known as exceptional points (EPs), have emerged as a new paradigm for engineering the response of optical systems. At such points, an N-dimensional space can be represented by a single eigenvalue and an eigenvector. As a result, these points are associated with abrupt phase transition in parameter space. Among many different non-conservative photonic configurations, parity-time (PT) symmetric systems are of particular interest since they provide a powerful platform to explore and consequently utilize the physics of exceptional points in a systematic manner. In this talk, I will review some of our recent works in the area of non-Hermitian (mainly PT-symmetric) active photonics. For example, in a series of works, we have demonstrated how the generation and judicial utilization of these points in laser systems can result in unexpected dynamics, unusual linewidth behavior, and improved modal response. On the other hand, biasing a photonic system at an exceptional point can lead to orders of magnitude enhancement in sensitivity- an effect that may enable a new generation of ultrasensitive optical sensors on chip. Non-Hermiticity can also be used as a means to promote or single out an edge mode in photonic topological insulator lattices. This effect has been recently utilized to demonstrate the first magnetic free topological insulator laser. In this talk, I will also discuss other topological behaviors in non-Hermitian systems, especially those associated with encircling an exceptional point in parameter space.
Biography: Mercedeh Khajavikhan received her Ph.D. in Electrical Engineering from the University of Minnesota in 2009. Her dissertation was on coherent beam combining for high power laser applications. In 2009, she joined the University of California in San Diego as a postdoctoral researcher where she worked on the design and development of nanolasers, plasmonic devices, and silicon photonics components. Since August 2012, she is an assistant professor in the College of Optics and Photonics (CREOL) at the University of Central Florida (UCF), working primarily on novel phenomena in active photonic systems. She received the NSF Early CAREER Award in 2015, the ONR Young Investigator Award in 2016, and the University of central Florida Reach for the Stars Award in 2017.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Astani Civil and Environmental Engineering Seminar
Fri, Mar 30, 2018 @ 03:00 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Farimah Shirmohammadi, Astani CEE Ph.D. Student
Talk Title: TBA
Abstract: TBA
Location: Ray R. Irani Hall (RRI) - 101
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
-
NL Seminar-Generating Adversarial Examples with Syntactically Controlled Paraphrase Networks
Fri, Mar 30, 2018 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mohit Iyyer , AI2, UMass Amherst)
Talk Title: Generating Adversarial Examples with Syntactically Controlled Paraphrase Networks
Series: Natural Language Seminar
Abstract: Many datasets for natural language processing problems lack linguistic variation, which hurts generalization of models trained on them. Recent research has shown that it is possible to break many learned models by evaluating them on adversarial examples, which are generated by manually introducing lexical, pragmatic, and syntactic variation to existing held-out examples from the data. Automating this process is challenging, as input semantics must be preserved in the face of potentially large sentence modifications. In this talk, I will focus specifically on syntactic variation in discussing our recent work on syntactically controlled paraphrase networks SCPN for adversarial example generation.
Given a sentence and a target syntactic form e.g., a constituency parse, an SCPN is trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing back translation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that almost always follow their target specifications without decreasing paraphrase quality when compared to baseline uncontrolled paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both 1. Fool pretrained models and 2. improve the robustness of these models to syntactic variation when used for data augmentation.
Biography: Mohit Iyyer will be joining UMass Amherst as an assistant professor in Fall 2018. Currently, he is a Young Investigator at the Allen Institute of Artificial Intelligence; prior to that, he received a Ph.D. from the Department of Computer Science at the University of Maryland, College Park, advised by Jordan Boyd Graber and Hal Daume III. His research interests lie at the intersection of natural language processing and machine learning. More specifically, he focuses on designing deep neural networks for both traditional NLP tasks e.g., question answering, language generation and new problems that involve creative language e.g., understanding narratives in novels). He has interned at MetaMind and Microsoft Research, and his research has won a best paper award at NAACL 2016 and a best demonstration award at NIPS 2015.
Host: Nanyun Peng
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/
.jpg)
