The ECE Distinguished Lectures Series (DLS) brings world-class researchers to the University to share their research and discoveries. The lectures are free and everyone is welcome to attend.
Talk details, including links to the lectures, can be found on the DLS Speakers page. Or click the photo or name of the speaker you are interested in below.
Past Distinguished Lectures Series
Professor Irina Rish, Université de Montréal
September 23, 2021
Title: Making AI Robust and Versatile: a Path to AGI?
Abstract: Modern AI systems have achieved impressive results in many specific domains, from image and speech recognition to natural language processing and mastering complex games such as chess and Go. However, they often remain inflexible, fragile and narrow, unable to continually adapt to a wide range of changing environments and novel tasks without “catastrophically forgetting” what they have learned before, to infer higher-order abstractions allowing for systematic generalization to out-of-distribution data, and to achieve the level of robustness necessary to “survive” various perturbations in their environment – a natural property of most biological intelligent systems, and a necessary property for successfully deploying AI systems in real-life applications. In this talk, we will provide a brief overview of some modern approaches towards making AI more “broad” (versatile) and robust, including transfer learning, domain generalization, invariance principle in causality, adversarial robustness and continual learning. Furthermore, we briefly discuss the role of scale, and summarize recent advances in training large-scale unsupervised models, such as GPT-3, CLIP, DALL-e, which demonstrate remarkable improvements in transfer, both forward (few-shot generalization to novel tasks) and backward (alleviating catastrophic forgetting). We also emphasize the importance of developing an empirical science of AI behaviors, and focus on rapidly expanding field of neural scaling laws, which allow us to better compare and extrapolate behavior of various algorithms and models with increasing amounts of data, model size and computational resources.
Biography: Irina Rish is an Associate Professor in the Computer Science and Operations Research Department at the Université de Montréal (UdeM) and a core faculty member of MILA – Quebec AI Institute. She holds Canada Excellence Research Chair (CERC) in Autonomous AI and a Canadian Institute for Advanced Research (CIFAR) Canada AI Chair. She received her MSc and PhD in AI from University of California, Irvine and MSc in Applied Mathematics from Moscow Gubkin Institute. Dr. Rish’s research focus is on machine learning, neural data analysis and neuroscience-inspired AI. Before joining UdeM and MILA in 2019, Irina was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She received multiple IBM awards, including IBM Eminence & Excellence Award and IBM Outstanding Innovation Award in 2018, IBM Outstanding Technical Achievement Award in 2017, and IBM Research Accomplishment Award in 2009. Dr. Rish holds 64 patents, has published over 80 research papers in peer-reviewed conferences and journals, several book chapters, three edited books, and a monograph on Sparse Modeling.
Professor Zhenan Bao, Stanford University
September 30, 2021
Title: Skin-Inspired Organic Electronics
Abstract: Skin is the body’s largest organ, and is responsible for the transduction of a vast amount of information. This conformable, stretchable, self-healable and biodegradable material simultaneously collects signals from external stimuli that translate into information such as pressure, pain, and temperature. The development of electronic materials, inspired by the complexity of this organ is a tremendous, unrealized materials challenge. However, the advent of organic-based electronic materials may offer a potential solution to this longstanding problem. Over the past decade, we have developed materials design concepts to add skin-like functions to organic electronic materials without compromising their electronic properties. These new materials and new devices enabled arrange of new applications in medical devices, robotics and wearable electronics. In this talk, I will discuss basic material design concepts for realizing stretchable, self-healable and biodegradable conductive or semiconductive materials. I will show our methods for scalable fabrication of stretchable electronic circuit blocks. Finally, I will show a few examples of applications we are pursuing uniquely enabled by skin-like organic electronics when interfacing with biological systems, such as low-voltage electrical stimulation, high-resolution large area electrophysiology, “morphing electronics” that grows with biological system and genetically targeted chemical assembly – GTCA.
Bio: Zhenan Bao is Department Chair and K.K. Lee Professor of Chemical Engineering, and by courtesy, a Professor of Chemistry and a Professor of Material Science and Engineering at Stanford University. Bao founded the Stanford Wearable Electronics Initiate (eWEAR) in 2016 and serves as the faculty director.
Prior to joining Stanford in 2004, she was a Distinguished Member of Technical Staff in Bell Labs, Lucent Technologies from 1995-2004. She received her Ph.D in Chemistry from the University of Chicago in 1995. She has over 600 refereed publications and over 100 US patents with a Google Scholar H-Index >175.
Bao is a member of the National Academy of Engineering, the American Academy of Arts and Sciences and the National Academy of Inventors. She is a Fellow of MRS, ACS, AAAS, SPIE, ACS PMSE and ACS POLY.
Bao was selected as Nature’s Ten people in 2015 as a “Master of Materials” for her work on artificial electronic skin. She was awarded the MRS Mid-Career Award in 2021, the inaugural ACS Central Science Disruptor and Innovator Prize in 2020, the Gibbs Medal by the Chicago session of ACS in 2020, the Wilhelm Exner Medal by Austrian Federal Minister of Science 2018, ACS Award on Applied Polymer Science 2017, the L’Oréal-UNESCO For Women in Science Award in the Physical Sciences 2017, the AICHE Andreas Acrivos Award for Professional Progress in Chemical Engineering in 2014, ACS Carl Marvel Creative Polymer Chemistry Award in 2013, ACS Cope Scholar Award in 2011, the Royal Society of Chemistry Beilby Medal and Prize in 2009, the IUPAC Creativity in Applied Polymer Science Prize in 2008.
Bao is a co-founder and on the Board of Directors for C3 Nano and PyrAmes, both are silicon-valley venture funded start-ups. She serves as an advising Partner for Fusion Venture Capital.
Professor David Patterson, University of California, Berkeley
October 7, 2021
Title: Carbon Emissions and Large Neural Network Training
Abstract: The demand for computation by machine learning (ML) has grown rapidly over the past few years. Together with this growth come a number of costs, including energy. Estimating the energy cost is an important step towards measuring the environmental impact of ML and identifying strategies to be more sustainable. Yet, estimating energy costs is challenging without detailed information about how models are trained and used.
Here we take one step further than previous studies on this topic and calculate the energy use and carbon footprint of several recent large models (T5, Meena, GShard, Switch Transformer, and GPT-3). We also refine earlier estimates for the neural architecture search that found Evolved Transformer. We evaluate more precise data quantifying model types, datacenter efficiency, processor efficiency, and energy mix. We also discuss the training and inference life cycles of machine learning models.
We highlight significant opportunities to further improve our energy efficiency and want to share these observations with ML practitioners as well as others interested in the energy consumption and environmental footprint of ML training. The factors we found to be most impactful within Google include:
- Large but sparsely activated DNNs can consume <1/10th the energy of large, dense DNNs without sacrificing accuracy despite using the same number or even more parameters.
- Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2 emissions vary ~5X-10X, even within the same country and the same organization.
- Specific datacenter infrastructure matters, as Cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters and the ML-oriented accelerators inside them can be ~2-5X more effective than off-the-shelf systems.
Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to ~100-1000X; even given the same DNN, the choice of datacenter and processor can save up to ~10-100X. As a result of these insights, we are now optimizing where and when large models are trained.
Bio: David Patterson is a UC Berkeley professor, Google distinguished engineer, RISC-V International Vice-Chair, and RISC-V International Open Source Laboratory Director. His best known projects are RISC and RAID. He co-authored seven books, including Computer Architecture: A Quantitative Approach, and shared the 2017 ACM A.M. Turing Award with his co-author John Hennessy.
Professor Rama Chellappa, Johns Hopkins University
October 28, 2021
Title: Are Machines Learning?
Abstract: In this talk, I will briefly survey my group’s recent works on building operational systems for face recognition, vehicle re-identification, and action recognition using deep learning. While reasonable success can be claimed, many open problems still remain to be addressed. These include bias detection and mitigation, domain adaptation and generalization, and handling adversarial attacks. Some of our recent work addressing these challenges will be presented.
Bio: Prof. Rama Chellappa is a Bloomberg Distinguished Professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering at Johns Hopkins University (JHU). At JHU, he is also affiliated with CIS, CLSP, Malone Center and MINDS. Before coming to JHU in August 2020, he was a Distinguished University Professor, a Minta Martin Professor of Engineering, a Professor in the ECE department and the University of Maryland Institute Advanced Computer Studies at the University of Maryland (UMD). He holds a non-tenure position as a College Park Professor in the ECE department at UMD. During 1981-1991, he was an assistant and associate professor in the Department of EE-Systems at University of Southern California. He received the M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN. His current research interests span many areas in image and signal processing, computer vision, artificial intelligence, and machine learning. Prof. Chellappa is a recipient of many awards from IEEE, IAPR, UMD and USC. Some notable awards are the 2020 Jack S. Kilby Medal for Signal Processing from the IEEE, the K.S. Fu Prize from the International Association of Pattern Recognition (IAPR), the Society and Technical Achievement Awards from the IEEE Signal Processing Society, the Technical Achievement Award from the IEEE Computer Society and the Inaugural Leadership Award from the IEEE Biometrics Council. At UMD, he has received numerous college and university level recognitions for research, teaching, innovation and mentoring of undergraduate students. Prof. Chellappa served as the EIC of IEEE Transactions on Pattern Analysis and Machine Intelligence, as a Distinguished Lecturer of the IEEE Signal Processing Society, as the President of IEEE Biometrics Council and as General and Technical Program Chair/Co-Chair for several IEEE international and national conferences and workshops. He is a Fellow of AAAI, AAAS, ACM, IAPR, IEEE, NAI and OSA and holds eight patents.
Frank O’Mahony, Intel
January 27, 2022
Title: Scaling SerDes Beyond 100Gb/s in Advanced CMOS Technologies
Abstract: Over the past two decades, high-speed wireline data rates have doubled every three-to-four years to keep pace with aggregate system bandwidth requirements. Communication standards for networking and storage, like Ethernet and OIF-CEI, tend to be the first to shift to higher data rates in order to support bandwidth density requirements for datacenters, supercomputers, telecom and AI hardware. Today, SerDes IPs up to 116Gb/s are reaching maturity and pathfinding for SerDes transceivers capable of sending data over 200Gb/s is well underway.
Maintaining this exponential bandwidth trend while staying within acceptable die area and system thermal limits has clearly benefitted from continuous CMOS process technology scaling. However, the rate of bandwidth increase and required improvements in energy efficiency have exceeded the benefits of process technology scaling alone. SerDes system and circuit architecture have had to evolve and improve to fill this gap. In addition, the benefits of scaled CMOS process technology come with challenges for transistor and interconnect reliability and the parasitics for scaled geometries. Increasing data rates and relatively constant link distances have gradually required some longer-reach copper interconnects to be replaced by optical channels. But, for now, electrical signaling continues to be the primary way that data gets on and off of the chips, packages and boards at the heart of high-bandwidth systems.
This presentation will start by providing an introduction to SerDes, including standards, basic signal integrity and link equalization and clocking architecture. Next it will describe system and circuit design techniques that have extended per-lane bandwidth to 100Gb/s, including PAM-4 modulation and ADC/DSP-based receivers, along with the benefits and challenges of designing high-speed transceivers in scaled CMOS technologies. Finally it will show some recent design and measurement results for the next leap in SerDes data rates up to 224Gb/s.
Bio: Frank leads the I/O Circuit Technology group within Advanced Design at Intel in Hillsboro, Oregon, where he is a Senior Principal Engineer. His team coordinates circuit-process co-design for wireline I/O at Intel. They also design and test the first I/Os on each new CMOS process technology. From 2003 until 2011 he was a member of the Signaling Research group in Intel Labs. His work at Intel spans high-speed and low-power transceivers, clock generation and distribution, equalization, analog design in scaled CMOS and on-die measurement techniques.
Frank received the BS, MS, and PhD degrees in electrical engineering from Stanford University in 1997, 2000 and 2004, respectively. He has published over 45 papers in peer-reviewed conferences and journals. He has received the ISSCC Jack Kilby Award, IEEE Journal on Solid-State Circuits Best Paper Award and TCAS Darlington Best Paper Award. Frank has been on the ISSCC Technical Program Committee since 2012 including five years as the Wireline Subcommittee chair. He currently serves as the ISSCC 2022 Forums Chair. He is a Senior Member of the IEEE and served as an IEEE Distinguished Lecturer. Frank currently chairs the IEEE SSCS Distinguished Lecturer Program.
Professor Domitilla Del Vecchio, MIT
February 10, 2022
Title: Control Theory for Synthetic Biology
Abstract: Engineering biology has tremendous potential to impact applications ranging from energy, to environment, to health. As the sophistication of engineered biological networks increases, the ability to predict system behavior becomes more limited. In fact, while a system’s component may be well characterized in isolation, the salient properties of this component often change in rather surprising ways once it interacts with other components in the cell or when the intra-cellular environment changes. This context-dependence of biological circuits makes it difficult to perform rational design and often leads to lengthy, combinatorial, design procedures where each module needs to be re-designed ad hoc when other parts are added to a system. Rather than relying on such ad-hoc design procedures, control theoretic approaches may be used to engineer “insulation” of circuit components from context, thus enabling modular composition through specified input/output connections. In this talk, I will give an overview of modularity failures in genetic circuits, focusing on problems of loads, and introduce a control-theoretic framework, founded on the concept of retroactivity, to address the insulation question. Within this framework, insulation can be mathematically formulated as a disturbance rejection problem; however, classical solutions are not directly applicable due to bio-physical constraints. I will thus introduce solutions relying on time-scale separation, a key feature of biomolecular systems, which we used to build two classes of devices: load drivers and the resource decouplers. These devices aid modularity, facilitate predictable composition of genetic circuits, and show how control theoretic approaches can address pressing challenges in engineering biology.
Bio: Domitilla Del Vecchio received the Ph. D. degree in Control and Dynamical Systems from the California Institute of Technology, Pasadena, and the Laurea degree in Electrical Engineering (Automation) from the University of Rome at Tor Vergata in 2005 and 1999, respectively. From 2006 to 2010, she was an Assistant Professor in the Department of Electrical Engineering and Computer Science and in the Center for Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. In 2010, she joined the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where she is currently Professor and member of the Synthetic Biology Center. She is a IEEE Fellow and a recipient of the Newton Award for Transformative Ideas during the COVID-19 Pandemic (2020), the 2016 Bose Research Award (MIT), the Donald P. Eckman Award from the American Automatic Control Council (2010), the NSF Career Award (2007), the American Control Conference Best Student Paper Award (2004), and the Bank of Italy Fellowship (2000). Her research focuses on developing techniques to make synthetic genetic circuits robust to context and on applying these to biosensing and cell fate control for regenerative medicine applications.
Professor W.C. Chew, Purdue University
March 17, 2022
4:00 p.m. EDT
Title: Classical and Quantum Electromagnetics: What is the Difference?
Abstract: Electromagnetics has influenced electrical engineering pervasively since the advent of Maxwell’s equations in 1865 . Classical electromagnetics has impacted electrical engineering technologies all the way from statics to optics. It has influenced technologies from nanometer length scales to planetary length scales. What was unbeknownst to Maxwell was that the equations he put together are also valid in the quantum world. This is because photons are electromagnetic in origin: the field associated with photons is electromagnetic field, which can be quantized to accommodate or carry photons.
In this talk, we will discuss the impact of classical electromagnetics in a wide swath of technologies especially related to electrical engineering. The recent advent of quantum technologies calls for new ways to solve the quantum Maxwell’s equations. In addition, a way to track the state of the quantum system is necessary, and this is obtained by solving the quantum state equation. This field of quantum electromagnetics is still in its infancy, but it is hoped that the knowledge base generated for classical electromagnetics can be reused for quantum electromagnetics. Since photons are used in quantum communications, quantum computers, quantum sensing, many new future technologies can be impacted by quantum electromagnetics.
Bio: W.C. Chew received all his degrees from MIT. His research interests are in wave physics, specializing in fast algorithms for multiple scattering imaging and computational electromagnetics in the last 30 years. His recent research interest is in combining quantum theory with electromagnetics, and differential geometry with computational electromagnetics. After MIT, he joined Schlumberger-Doll Research in 1981. In 1985, he joined U Illinois Urbana-Champaign, was then the director of the Electromagnetics Lab from 1995-2007. During 2000-2005, he was the Founder Professor, 2005-2009 the YT Lo Chair Professor, and 2013-2017 the Fisher Distinguished Professor. During 2007-2011, he was the Dean of Engineering at The University of Hong Kong. He joined Purdue U in August 2017 as a Distinguished Professor. He has co-authored three books, many lecture notes, over 450 journal papers, and over 600 conference papers. He is a fellow of various societies, and an ISI highly cited author. In 2000, he received the IEEE Graduate Teaching Award, in 2008, he received the IEEE AP-S CT Tai Distinguished Educator Award, in 2013, elected to the National Academy of Engineering, and in 2015 received the ACES Computational Electromagnetics Award. He received the 2017 IEEE Electromagnetics Award. In 2018, he served as the IEEE AP-S President. He is a distinguished visiting professor at Tsinghua U, China, Hong Kong U, and National Taiwan U.
Professor Kunle Olukotun, Stanford University
March 24, 2022
4:00 p.m. EDT
Title: Let the Data Flow!
Abstract: As the benefits from Moore’s Law diminish, future computing performance improvements will rely on specialized application accelerators. To justify the expense of designing an accelerator it should accelerate an important set of application areas. In my talk, I will explain how Reconfigurable Dataflow Accelerators (RDAs) can be used to accelerate a broad set of data intensive applications. RDAs can accelerate Machine Learning (ML) by efficiently executing the hierarchical dataflow that exists in many ML applications and models. I will explain how RDAs can also be used to accelerate irregular applications using a new programming model called Dataflow Threads. I will talk about future research directions for dataflow architectures including sparse ML applications, networking applications and dataflow architecture compilers
Bio: Kunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is a pioneer in multi-core processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project.
In 2017 Olukotun co-founded SambaNova Systems, a Machine Learning and Artificial Intelligence company, and continues to lead as their Chief Technologist. Prior to SambaNova Systems, Olukotun founded Afara Websystems to develop high- throughput, low-power multi-core processors for server systems. The Afara multi-core processor, called Niagara, was acquired by Sun Microsystems and now powers Oracle’s SPARC-based servers.
Olukotun is the Director of the Pervasive Parallel Lab and a member of the Data Analytics for What’s Next (DAWN) Lab, developing infrastructure for usable machine learning.
Olukotun is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design. Olukotun recently won the IEEE Computer Society’s Harry H. Goode Memorial Award and was also elected to the National Academy of Engineering.
Kunle received his Ph.D. in Computer Engineering from The University of Michigan.
11:00 a.m.Title: Guessing Random Additive Noise Decoding (GRAND)
Abstract: Claude Shannon’s 1948 “A Mathematical Theory of Communication” provided the basis for the digital communication revolution. As part of that ground-breaking work, he identified the greatest rate (capacity) at which data can be communicated over a noisy channel. He also provided an algorithm for achieving it, based on random codes and a code-centric maximum Maximum Likelihood (ML) decoding, where channel outputs are compared to all possible codewords to select the most likely candidate based on the observed output. Despite its mathematical elegance, his algorithm is impractical from a complexity perspective and much work in the intervening 70 years has focused on co-designing codes and decoders that enable reliable communication at high rates.
We introduce a new algorithm for a noise-centric, rather than code-centric, ML decoding. The algorithm is based on the principle that the receiver rank orders noise sequences from most likely to least likely, and guesses noises accordingly. Subtracting noise from the received signal in that order, the first instance that results in an element of the code-book is the ML decoding. For common additive noise channels, we establish that the algorithm is capacity-achieving for uniformly selected code-books, providing an intuitive alternate approach to the channel coding theorem. We illustrate the practical usefulness of our approach and the fact that it renders the decoding of random codes feasible. The complexity of the decoding is, for the sorts of channels generally used in commercial applications, quite low, unlike code-centric ML. This work is joint with Ken Duffy (Maynooth University).
Bio: Muriel Médard is the Cecil H. Green Professor in the Electrical Engineering and Computer Science (EECS) Department at MIT and leads the Network Coding and Reliable Communications Group at the Research Laboratory for Electronics at MIT. She has served as editor for many publications of the Institute of Electrical and Electronics Engineers (IEEE), of which she was elected Fellow, and she has served as Editor in Chief of the IEEE Journal on Selected Areas in Communications. She was President of the IEEE Information Theory Society in 2012, and served on its board of governors for eleven years. She has served as technical program committee co-chair of many of the major conferences in information theory, communications and networking. She received the 2019 Best Paper award for IEEE Transactions on Network Science and Engineering, 2009 IEEE Communication Society and Information Theory Society Joint Paper Award, the 2009 William R. Bennett Prize in the Field of Communications Networking, the 2002 IEEE Leon K. Kirchmayer Prize Paper Award, the 2018 ACM SIGCOMM Test of Time Paper Award and several conference paper awards. She was co-winner of the MIT 2004 Harold E. Egerton Faculty Achievement Award, received the 2013 EECS Graduate Student Association Mentor Award and served as undergraduate Faculty in Residence for seven years. In 2007 she was named a Gilbreth Lecturer by the U.S. National Academy of Engineering. She received the 2016 IEEE Vehicular Technology James Evans Avant Garde Award, the 2017 Aaron Wyner Distinguished Service Award from the IEEE Information Theory Society and the 2017 IEEE Communications Society Edwin Howard Armstrong Achievement Award. She is a member of the National Academy of Inventors. She was elected Member of the National Academy of Engineering for her contributions to the theory and practice of network coding in 2020. She received in 2020 a doctorate honors cause from the Technical University of Munich.
October 15, 2020
Title: Ultra-dense Data Storage and Extreme Parallelism with Electronic-Molecular Systems
Abstract: Sustaining Moore’s law is an increasingly challenging proposition. This talk will cover an alternative approach: going directly to the molecular level. Although we have yet to achieve scalable, general-purpose molecular computation, there are areas of IT in which a molecular approach shows growing promise. In this talk, I will explain how molecules, specifically synthetic DNA, can store digital data and perform certain types of special-purpose computation by leveraging tools already developed by the biotechnology industry. I will also discuss the architectural implications of molecular storage and processing systems and advocate for hybrid electronic-molecular systems as potential solutions to difficult computational problems, such as large-scale similarity search.
Bio: Karin Strauss is a Principal Research Manager at Microsoft Corporation and an Affiliate Professor at the University of Washington. She co-leads the Molecular Information System Laboratory with Luis Ceze, working on using molecules, currently DNA, to benefit the IT industry. Her background is in computer architecture, systems, and most recently molecular biology. Her research interests include emerging storage technologies, scaling of computation and storage, and special-purpose accelerators. Selected as one of the “100 Most Creative People in Business in 2016” by Fast Company Magazine, she got her PhD from the Department of Computer Science at the University of Illinois, Urbana-Champaign in 2007.
October 29, 2020
Abstract: In this talk we revisit the way we model electric power systems and assumptions made. This is done with an eye on fundamental challenges and missed opportunities for enhanced electricity services. Instead of focusing on specific technology, we view the problem of sustainable and resilient electricity service as the complex social-ecological system problem which can be greatly enabled by the distributed minimally coordinated grid operations. For this to work, several principles for operating protocols are needed. These principles are based on our recently-introduced multi-layered modeling framework for posing the problem of safe, robust and efficient design and control for rapidly changing electric energy systems. The proposed framework establishes dynamic relations between physical concepts such as stored energy, useful work, and wasted energy, on one hand; and modeling, simulation, and control of interactive modular complex dynamical systems, on the other. In particular, our recently introduced energy state-space modeling approach for electric energy systems is further interpreted using fundamental laws of physics in multi-physical systems, which are modeled as dynamically interacting modules. As such, it can be taught to students not specializing in traditional power systems engineering.
This approach is shown to be particularly well-suited for scalable optimization of large-scale complex systems. Instead of having to use simpler models, the proposed multi-layered modeling of system dynamics in energy space offers a promising basic method for modeling and controlling inter-dependencies across multi-physics subsystems for ensuring both feasible and near-optimal operation. It is illustrated how this approach can be used for understanding fundamental physical causes of inefficiencies and infeasibility, voltage collapse problem in particular, created either at the component level or resulting from poor matching of their interactions.
The ideas presented here evolved over the past two decades, in collaboration with several former students at both Carnegie Mellon University and Massachusetts Institute of Technology. They provide theoretical foundations for Dynamic Monitoring and Decision Systems (DyMonDS) framework envisioned as the next-generation SCADA and operating protocols for the changing electric energy systems. The control design based on joint work with Xia Miao and R. Jaddivada for microgrids and integration of renewable resources and demand response is used as an example to illustrate potential benefits of this approach.
Finally, many open modeling, estimation and optimization challenges/opportunities using this modeling approach are discussed.
Bio: Marija Ilić has retired as a Professor Emerita at Carnegie Mellon University. She is currently a Senior Staff in the Energy Systems Group 73 at the MIT Lincoln Laboratory, and a Senior research Scientist at MIT Institute for Data, Systems and Society (IDSS)/LIDS. She is an IEEE Life Fellow. She was the first recipient of the NSF Presidential Young Investigator Award for Power Systems signed by late President Ronald Regan. In addition to her academic work, she is the founder of New Electricity Transmission Software Solutions, Inc. (NETSS, Inc.). She has co-authored several books on the subject of large-scale electric power systems, and has co-organized an annual multidisciplinary Electricity Industry conference series at Carnegie Mellon (http://www.ece.cmu.edu/~electriconf) with participants from academia, government, and industry.
November 19, 2020
Title: Research Challenges in Large-Scale Machine Learning Systems at Facebook
Abstract: Machine learning powers nearly every major product in the Facebook family of apps. At this scale, ML data, training, and inference consume a significant (and growing) amount of resources in terms of compute, storage, and networking. Meanwhile, the design of the hardware and software systems needed to power ML workflows is instrumental in unlocking the experimentation process needed to innovate in the space and advance the state of the art. In this presentation, I will shine a spotlight on some of the interesting research challenges that we’ve encountered in this space, as well as some of the open problems that have seen under-investment from the broader research community.
Bio: Kim Hazelwood is the West Coast Head of Engineering for Facebook AI Research and well as the Technical Lead for Facebook Systems and Machine Learning (SysML) Research. Her expertise lies at the intersection between systems (compute and software platforms) and machine learning. Prior to joining Facebook in 2015, Kim held positions including Associate Professor with tenure at the University of Virginia, Software Engineer at Google, and Director of Systems Research at Yahoo Labs. She received a PhD in Computer Science from Harvard University, and is the recipient of an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award, the ACM SIGPLAN 10-Year Test of Time Award, the ACM SIGPLAN Programming Languages Award, and the CRA Distinguished Service Award. She currently serves on the Board of Directors of the Computing Research Association (CRA), MIT SystemsThatLearn, and EPFL EcoCloud. She has authored over 50 conference papers and one book.
January 14, 2021
Abstract: There is an enormous need for clean energy technology research and development to enable a global transition to sustainable energy systems. There are economic incentives for nations to invest in research and development to enable lower cost and more efficient systems: from 2015 to 2019, worldwide annual investments in renewable energy infrastructure have averaged about $600B. The build out of renewables over the next decades will need to be vastly larger in order to meet the 2015 Paris goal of reducing the worldwide emissions of greenhouse gases by 30% by 2030. Beyond 2030, new and vastly cost-reduced technologies for energy efficiency and sustainability in all sectors of the global economy will be needed, as well as enhanced renewable technologies, flexible and smart electric grids combined with efficient and low cost energy storage. In order to sustain the Paris goal of an earth temperature rise by 2 or even 1.5 degrees, humanity’s greenhouse gas emissions must be reduced drastically, to below 100% of current levels by 2050. We will need to deploy technologies that are at all stages of the readiness levels now, as well as move to a circular economy. There are encouraging signs that nations are investing stimulus funds to rebuild economies after COVID-19 with aim of accomplishing these goals for sustainability. I will suggest areas of research, development, demonstration and deployment in which advances are needed in order to meet this grand challenge.
Bio: Cherry Murray obtained both a B.S. and a Ph.D, in physics from the Massachusetts Institute of Technology. Her research interests have varied from experimental condensed matter and surface physics to nanotechnology, innovation, research and development of telecommunications networks, national security and science and technology policy. Her current interests include policy, research, development, and innovation to sustain human civilization on future Earth and beyond.
From 1978 to 2004, Murray held a number of research and management positions, which culminated in the Senior Vice Presidency of Physical and Wireless Research, at Bell Laboratories, Lucent Technologies, formerly AT&T Bell Laboratories and previously Bell Telephone Laboratories, Inc. She retired from Lucent Technologies and then served at Lawrence Livermore National Laboratory as Deputy Director for Science and Technology from 2004 to 2007, and as Principal Associate Director for Science and Technology from 2007 to 2009. She was dean of Harvard University’s School of Engineering and Applied Sciences from 2009 until 2014.
On leave from Harvard, Murray served as the Director of the United States Department of Energy’s Office of Science, from 2015 until 2017, overseeing $6 billion in competitive scientific research in the areas of advanced scientific computing, basic energy sciences, biological and environmental sciences, fusion energy sciences, high energy physics, and nuclear physics, as well as the management of 10 national laboratories.
She then returned to Harvard as the Benjamin Peirce Professor of Technology and Public Policy and Professor of Physics, retiring from this position in 2019, and joined the University of Arizona as Professor of Physics and Deputy Director for Research, Biosphere2, with a focus on the nexus of environment, water, food, and energy, ecology research and developing solutions for global sustainability.
In April 2019 she was elected to be one of the six co-chairs of the InterAcademy Partnership (IAP), a collaboration of over 145 academies of science, engineering and medicine. The IAP mission is to strengthen the independent voice of science at global, regional and national levels, by convening and empowering the world’s academies of science, engineering and medicine to work together on issues of global importance.
A member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences, Murray has received the National Medal of Technology and Innovation as well as the American Physical Society Maria Goeppert-Mayer Award and George E. Pake Prize. She currently serves as chair of the Board of Governors of the Okinawa Institute of Science and Technology Graduate University, on the Council of the Science and Technology for Society Forum in Japan, and as a board member of the American Academy of Arts and Sciences.
February 11, 2021
Title: Extreme Field Control with Electromagnetic Metasurfaces
Abstract: The research area of metamaterials has captured the imagination of scientists and engineers over the past two decades by allowing unprecedented control of electromagnetic fields. The extreme manipulation of fields has been made possible by the fine spatial control and wide range of material properties that can be attained through subwavelength structuring. Research in this area has resulted in devices which overcome the diffraction limit, render objects invisible, and even break time reversal symmetry. It has also led to flattened and conformal optical systems and ultra-thin antennas. This seminar will identify recent advances in the growing area of metamaterials, with a focus on metasurfaces: two dimensional metamaterials. The talk will explain what they are, the promise they hold, and how these field-transforming surfaces are forcing the rethinking of electromagnetic/optical design.
Electromagnetic metasurfaces are finely patterned surfaces whose intricate patterns/textures dictate their electromagnetic properties. Conventional field-shaping devices, such as lenses in prescription eye glasses or a magnifying glass, require thickness (propagation length) to manipulate electromagnetic waves through interference. In contrast, metasurfaces manipulate electromagnetic waves across negligible thicknesses through surface interactions, by impressing abrupt phase and amplitude discontinuities onto a wavefront. The role of the visible (propagating) and invisible (evanescent) spectrum in establishing these discontinuities will be explained. In addition, it will be shown how metasurfaces allow the complete transformation of fields across a boundary, and how this unique property is driving a new generation of ultra-compact electromagnetic and optical devices with unparalleled field control. Metasurfaces will be described that exhibit various field tailoring capabilities including multiwavelength and multifunctional performances, extreme field shaping and multi-input to multi-output capabilities. Recent results will be shown on metasurfaces with tunable properties and those that vary with time, and both space and time, opening new opportunities in adaptive and trainable designs.
Bio: Anthony Grbic is a Professor of Electrical Engineering and Computer Science at the University of Michigan. He received his B.A.Sc., M.A.Sc., and Ph.D. degrees in Electrical Engineering from the University of Toronto, in 1998, 2000, and 2005. Dr. Grbic’s research interests include engineered electromagnetic structures (metamaterials, metasurfaces, electromagnetic band-gap materials, frequency-selective surfaces), antennas, microwave circuits, wireless power transmission, and analytical electromagnetics/optics. Anthony Grbic is a Fellow of IEEE, and has received several recognitions for his research work including the Presidential Early Career Award for Scientists and Engineers, an AFOSR Young Investigator Award, and an NSF Faculty Early Career Development Award. He also received an Outstanding Young Engineer Award from the IEEE Microwave Theory and Techniques Society and a Booker Fellowship from the United States National Committee of the International Union of Radio Science.
February 25, 2021
Abstract: Brain-computer interfaces capable of transforming monitored brain activity into movement offer visions of a remarkable new clinical tool for restoring voluntary movement to paralyzed patients, but they also represent a powerful new tool for observing the brain in action. My lab’s work is at the interface of these two opportunities and seeks to address them both. To be viable clinically, BCIs must generalize better across motor behaviors, they must be more resilient in the face of constantly changing recorded neurons, and they must assist the user’s effort to become more proficient in their use. I will describe experiments in which we record signals directly from a monkey’s brain, transform them, then send them to an electrical stimulator that causes muscles to contract in precise spatiotemporal patterns under the monkey’s voluntary control. We have used this Functional Electrical Stimulation BCI to restore voluntary hand movement to monkeys during temporary paralysis, and are developing it further to allow its continuous use for a range of motor behaviors meant to represent those of a person’s daily activities. The heart of this approach is the identification of “latent signals” expressed within a low-dimensional manifold computed from the multiple neuron recordings. These signals appear to provide a stable prediction of the monkey’s behavior over many months-long periods. We are examining these latent signals in motor cortex, using single-unit recordings made wirelessly while the monkey is in its home cage. We are developing nonlinear decoders to predict muscle activity from the latent signals, across a range of motor behaviors that has not previously been possible. We are also studying the interaction between the premotor and motor cortices during motor adaptation, to understand the neural changes that accompany this adaptation. By learning more about the representation of diverse behaviors within the motor areas of the brain, we will be able to move our FES BCI more effectively from the lab to the clinic.
Bio: Lee E. Miller is a Distinguished Professor of Neuroscience in the Departments of Physiology, Physical Medicine and Rehabilitation, and Biomedical Engineering at Northwestern University. He was inducted into the American Institute for Medical and Biological Engineering in 2016 and is the current president of the Society for the Neural Control of Movement. Dr. Miller has had a career-long interest in the signals generated by neurons during arm movement. In the past 10 years, his lab has increasingly focused on translational research, including the use of brain machine interfaces to restore movement and sensation to spinal cord injured patients.
Professor Jorge Cortés, University of California, San Diego
March 4, 2021
Bio: Jorge Cortes is a Professor with the Department of Mechanical and Aerospace Engineering at the University of California, San Diego. He received the Licenciatura degree in mathematics from the Universidad de Zaragoza, Spain, in 1997, and the Ph.D. degree in engineering mathematics from the Universidad Carlos III de Madrid, Spain, in 2001. He held postdoctoral positions at the University of Twente, The Netherlands, and at the University of Illinois at Urbana-Champaign, USA. He was an Assistant Professor with the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz from 2004 to 2007. He is the author of “Geometric, Control and Numerical Aspects of Nonholonomic Systems” (New York: Springer-Verlag, 2002) and co-author of “Distributed Control of Robotic Networks” (Princeton: Princeton University Press, 2009). He received a NSF CAREER award in 2006 and was the recipient of the 2006 Spanish Society of Applied Mathematics Young Researcher Prize. He has co-authored papers that have won the 2008 IEEE Control Systems Outstanding Paper Award, the 2009 SIAM Review SIGEST selection from the SIAM Journal on Control and Optimization, the 2012 O. Hugo Schuck Best Paper Award in the Theory category, and the 2019 IEEE Transactions on Control of Network Systems Outstanding Paper Award. He is a Fellow of IEEE and SIAM. At the IEEE Control Systems Society, he has been a Distinguished Lecturer (2010-2014), and is currently its Director of Operations and an elected member (2018-2020) of its Board of Governors. His current research interests include distributed control and optimization, network science and complex systems, resource-aware control and coordination, distributed decision making and autonomy, and multi-agent coordination in robotics, transportation, power systems, and neuroscience. http://carmenere.ucsd.edu/jorge
March 11, 2020
Abstract: Integrated electronics is one of the most pervasive technologies in our world. It is at the heart of smart personal devices, computers, communication systems, domestic appliances, vehicles and of the Internet of Everything (IoE): all technologies that have become indispensable for our lifestyle.
When you consider Integrated Circuits, you will typically think to “chips”: miniature pieces of crystalline silicon where increasingly smaller field-effect transistors can be packed together. This integration trend, called Moore’s law, has characterized 50 years of amazing progress in electronics and its ubiquitous applications.
Innovative technologies for circuit integration, however, make possible entirely new, large form factors: from the displays we use every day to novel flexible electronics manufactured on foil.
In this lecture I will focus on the fascinating field of flexible electronics, discussing its innovative applications, the most recent developments, the scientific challenges that make it exciting, and the advancements that will inspire future research.
Bio: Eugenio Cantatore is Full Professor in the Integrated Circuits group at Eindhoven University of Technology (TU/e), where he leads the Emerging Technologies Lab. His research focusses on the design and characterization of electronic circuits fabricated with emerging technologies, as well as the design of ultra-low power micro-systems for biomedical applications. One of his main interests is the design of flexible electronics fabricated on plastic foil, including sensors interfaces, analog-digital converters and transceivers. To provide a few application examples, these elements enable integrating sensors in wearables for improved well-being and seamless medical diagnostics. They can even be embedded in food packaging, making it possible to measure the quality of the groceries, and allowing people to keep food as long as it is effectively good to eat, avoiding waste. Cantatore was chair of the Technology Directions subcommittee of ISSCC from 2013 to 2016, and ISSCC program chain in 2019. He is presently member of the ISSCC Executive Committee, member at large of the SSCS AdCom and Associate Editor of TCAS1. He is also active in the Technical Program Committees of IWASI and ESSCIRC. He was nominated IEEE Fellow in 2016, for contributions to the design of circuits with organic thin film transistors.
Cantatore authored or co-authored more than 200 papers in journals and conference proceedings, and 13 patents or patent applications. He is the recipient of several best paper awards and other professional recognitions. https://www.tue.nl/en/research/researchers/eugenio-cantatore
Professor Andrea Alù, The City University of New York (CUNY)
March 18, 2021
Abstract: In this talk, I discuss our recent research activity in electromagnetics, nano-optics, acoustics and mechanics, showing how suitably tailored meta-atoms and suitable arrangements of them open exciting venues to realize new phenomena and devices for light, radio-waves and sound. I discuss venues to largely break Lorentz reciprocity and realize isolation without the need of magnetic bias, based on broken time-reversal symmetry induced by mechanical motion, spatio-temporal modulation and/or nonlinearities. I also discuss how broken symmetries in space and space-time can open the opportunity to induce topological order in metamaterials. Another class of interesting metamaterials based on broken symmetries are parity-time symmetric metamaterials, which are asymmetric in space, but symmetric upon parity and time inversion. In the talk, I will also discuss the impact of these concepts from basic science to practical technology, from classical waves to quantum phenomena.
Bio: Andrea Alù is the Founding Director and Einstein Professor at the Photonics Initiative, CUNY Advanced Science Research Center. He received his Laurea (2001) and PhD (2007) from the University of Roma Tre, Italy, and, after a postdoc at the University of Pennsylvania, he joined the faculty of the University of Texas at Austin in 2009, where he was the Temple Foundation Endowed Professor until Jan. 2018. Dr. Alù is a Fellow of NAI, AAAS, IEEE, AAAS, OSA, SPIE and APS, and has received several scientific awards, including the IEEE Kiyo Tomiyasu Award, the Vannevar Bush Faculty Fellowship from DoD, the ICO Prize in Optics, the NSF Alan T. Waterman award, the OSA Adolph Lomb Medal, and the URSI Issac Koga Gold Medal.
4:00 p.m., room SF1105Title: Wireless Bioelectronics
Abstract: Miniaturized electronics, when placed inside the body, can wirelessly monitor and modulate internal activity and thus hold promise as a new class of treatments for disorders. The development of such bioelectronic medicines requires wireless interfaces that are tiny and operate deep in a complex electromagnetic environment. In this talk, I will describe a new method for electromagnetic energy transfer that exploits near-field interactions with biological tissue to wirelessly power tiny devices anywhere in the body, including the heart and the brain. I will discuss engineering and experimental challenges to realizing such interfaces, including a pacemaker that is smaller than a grain of rice and a fully internalized neuromodulation platform. These devices can act as bioelectronic medicines, capable of precisely modulating local activity, that may be more effective treatments than drugs, which act globally throughout the body.
Bio: Ada received her Ph.D. degree from the University of California at Berkeley. Upon graduation, she spent some time in industries, and worked at Intel and SiBeam. Then, she returned to academic. Now, she is a faculty at Stanford University. She is a Chan Zuckerberg Biohub senior investigator.
Friday, October 11, 2019
4:00 p.m., room BA1160
Title: The Role of Cryo-CMOS in Quantum Computers
Abstract: Quantum computing holds the promise to solve intractable problems using processors that exploit quantum physics concepts, such as superposition and entanglement. The core of a quantum processor, generally an array of qubits, needs to be controlled and read out by a classical processor operating on the qubits with nanosecond latency, several millions of times per second. Due to the extremely weak signals involved in the process, ultra-low-noise, highly sensitive circuits and systems are needed, along with very precise timing capability. We advocate the use of CMOS technologies to achieve these goals, whereas the circuits will be operated at deep-cryogenic temperatures. We believe that these circuits, collectively known as cryo-CMOS control, will make future qubit arrays scalable, enabling a faster growth of the qubit count. In the talk, the challenges of designing and operating complex circuits and systems at 4K and below will be outlined, along with preliminary results achieved in the control and read-out of qubits by ad hoc integrated circuits that were optimized to operate at low power in these conditions. The talk will conclude with a perspective on the field and its trends.
Bio: Edoardo Charbon (SM’00 F’17) received the Diploma from ETH Zurich, the M.S. from the University of California at San Diego, and the Ph.D. from the University of California at Berkeley in 1988, 1991, and 1995, respectively, all in electrical engineering and EECS. He has consulted with numerous organizations, including Bosch, X-Fab, Texas Instruments, Maxim, Sony, Agilent, and the Carlyle Group. He was with Cadence Design Systems from 1995 to 2000, where he was the Architect of the company’s initiative on information hiding for intellectual property protection. In 2000, he joined Canesta Inc., as the Chief Architect, where he led the development of wireless 3-D CMOS image sensors. Since 2002 he has been a member of the faculty of EPFL, where is a full professor since 2015. From 2008 to 2016 he was with Delft University of Technology’s as Chair of VLSI design. He has been the driving force behind the creation of deep-submicron CMOS SPAD technology, which is mass-produced since 2015 and is present in telemeters, proximity sensors, and medical diagnostics tools. His interests span from 3-D vision, LiDAR, FLIM, FCS, NIROT to super-resolution microscopy, time-resolved Raman spectroscopy, and cryo-CMOS circuits and systems for quantum computing. He has authored or co-authored over 350 papers and two books, and he holds 21 patents. Dr. Charbon is a distinguished visiting scholar of the W. M. Keck Institute for Space at Caltech, a fellow of the Kavli Institute of Nanoscience Delft, a distinguished lecturer of the IEEE Photonics Society, and a fellow of the IEEE.
Thursday, October 24, 2019
4:00 p.m., room SF1105
Title: Flexible Microimplants for Bioelectronics Medicine and Neuroprosthetics
Abstract: Neural implants need to establish stable and reliable interfaces to the target structure for chronic application in neurosciences as well as in clinical applications. They have to record electrical neural signals, excite neural cells or fibers by means of electrical stimulation. In case of optogenetic experiments, optical stimulation by integrated light sources or waveguides must be integrated on implants. Metabolic monitoring and detection of neurotransmitter concentrations is also part of the research agenda but not yet mature enough for translation in chronic clinical applications. Proper selection of substrate, insulation and electrode materials is of utmost importance to bring the interface in close contact with the neural target structures, minimize foreign body reaction after implantation and maintain functionality over the complete implantation period. Our work has focused on polymer substrates with integrated thin-film metallization as core of our flexible neural interfaces approach and silicone rubber with metal sheets. Micromachining and laser structuring are the main technologies for electrode array manufacturing. Designing applications for implants in the peripheral and central nervous system needs integration of components, the connection of cables and connectors to both, electrode arrays and hermetic packages containing electronic circuitry for recording, stimulation and signal processing. Failure of one of the components or connections stops the function of the whole system. We present an exemplary implant system and discuss state of the art materials and manufacturing techniques as well as prominent failure modes. Thin-film substrates and hybrid combinations with silicone rubber substrates serve as neural interfaces. Adhesion layers have been integrated to obtain long term stability of polyimide-platinum sandwiches. Hermetic packages with dozens of electrical feed-throughs need novel approaches to meet the desire of implants with hundreds of electrode channels. Reliability data from long-term ageing studies and chronic experiments show the applicability of thin-film implants for stimulation and recording and ceramic packages for electronics protection. Examples of sensory feedback after amputation trauma, vagal nerve stimulation to treat hypertension and chronic recordings from the brain surface display opportunities and challenges of these miniaturized implants. System assembly and interfacing microsystems to robust cables and connectors still is a major challenge in translational research and transition of research results into medical products.
Bio: Prof. Dr. Stieglitz is a full professor for Biomedical Microtechnology in the Institute for Microsystem Technology (IMTEK) at the University of Freiburg (Germany) since 2004. His work focuses on the development of biocompatible assembling and packaging techniques and the application of microsystems for neural prostheses and neuromodulation. His research interests include biomedical microdevices, functional electrical stimulation and neural implants.
Dr. Stieglitz studied electrical engineering at the University of Technology Braunschweig (1987-89) and Karlsruhe (1989-93) where he received the Dipl.-Ing. degree in electrical engineering with the special subject biomedical engineering in 1993. In 1998 he received the Dr.-Ing. degree (summa cum laude) in electrical engineering from the University of Saarland (Germany). This work was honored with the ‘Stiftung-Familie-Klee’ award for young scientists from the German Society for Biomedical Engineering (DGBMT) in 1999.
In 2000 he received the science award of the Saarland State for his work on flexible, neural prostheses. Dr. Stieglitz qualified as a university lecturer (habilitation) in 2002 at the Saarland University in biomedical microsystem technology. He worked with the Fraunhofer-Institute for Biomedical Engineering (IBMT) from 1993 to 2004, where he established the research work on biomedical microsystems for neural prostheses, which finally led to the IBMT Neural Prostheses Group.
Dr. Stieglitz is a Senior member of the IEEE Engineering in Medicine and Biology Society (EMBS) where he is member of the Neural Engineering Technical Committee, the German Engineering Society (VDI) and the German Society for Biomedical Engineering (DGBMT) within the German Electrotechnical Society (VDE) where he is chair of the Neural Prostheses and Intelligent Implants Section. He is also member of the Society for Neurosciences, the Materials Research Society and founding member of the International Society for Functional Electrical Stimulation (IFESS).
He is co-founder and of the companies Cortec (www.cortec-neuro.com) and neuroloop (www.neuroloop.de) that were spun off the University of Freiburg. Dr. Stieglitz is member of the Bernstein Center Freiburg and deputy speaker of the Cluster of Excellence BrainLinks-BrainTools (German Research Foundation ExC 1086).
Thursday, November 7, 2019
4:00 p.m., room SF1105
Title: Deep Generative Models, Compressed Sensing and General Inverse Problems
Abstract: We will introduce deep generative models and show how they can be used to solve inverse problems of various types. Linear inverse problems involve the reconstruction of an unknown signal (e.g. a tomography image) from an underdetermined system of noisy linear measurements. Most results in the literature require that the reconstructed signal has some known structure, e.g. it is sparse in some known basis (usually Fourier or Wavelet). In this work we show how to remove such prior assumptions and rely instead on deep generative models (e.g. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)). We show how the problems of image inpainting (completing missing pixels) and super-resolution are special cases of this general framework. We generalize theoretical results on compressive sensing for deep generative models and discuss several open problems.
Bio: Alex Dimakis is an Associate Professor at the ECE department at UT Austin. He received his Ph.D. in 2008 from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech. He received the NSF Career, a Google faculty award and the Eli Jury dissertation award. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. In 2018 he received the James L. Massey Award awarded by the Information Theory society. His research interests include information theory, coding theory and machine learning.
Thursday, February 13, 2020
4:00 p.m., room SF1105
Title: How Many Pixels Does Your Camera Have? Ours Has Only One!
Abstract: Cameras are often marketed in terms of the number of pixels they have – the more pixels the “better” the camera. Rather than increasing the number of pixels we ask the question “how can a camera work when it only has a single pixel?” This talk will link the field of computational ghost imaging to that of single-pixel cameras, explaining how components found within a standard data projector, more commonly used for projecting films and the like, can be used to create both still and video cameras using a single photodiode.
These single pixel approaches are particularly useful for imaging at wavelengths where detector arrays are either very expensive or even unobtainable. The ability to image at unusual wavelengths means that one can make cameras that can see through fog or smoke or even image invisible gases as they leak from pipes.
Beyond imaging at these unusual wavelengths, by using pulsed illumination and adding time resolution to the camera it is possible to see in 3D, perhaps useful for autonomous vehicles and other robotic applications.
Bio: Miles Padgett holds the Kelvin Chair of Natural Philosophy at the University of Glasgow. He is interested in all things optical both classical and quantum. In 2001 he was elected a Fellow of the Royal Society of Edinburgh (RSE) and in 2014 a Fellow of the Royal Society, the UK’s National Academy. In 2009, with Les Allen, he won the Institute of Physics Young Medal, in 2014 the RSE Kelvin Medal, in 2015 the Science of Light Prize from the European Physical Society and in 2017 the Max Born Award of the Optical Society (OSA).
He is lead scientist in QuantIC, a £30M investment and one of the UKs four Quantum Technology Hubs. QuantIC links over 100 world-leading quantum scientists/technologists from six UK Universities with global industry leaders to transform imaging across instrumentation, security and industrial sectors.
Thursday, February 27, 2020
4:00 p.m., room SF1105
Title: Research on the Computation of Large Scale Power System Transients
Abstract: Numerical simulation methods are used in modern power systems for design, operation and analysis stages. Numerical models are more and more important and play dominant roles in technological advancements in modern power systems in Canada and in the world. The needs for simulation capabilities increase significantly faster than the capability of researchers to deliver new methods for solving increasingly complex problems.
The development of faster circuit based simulation tools allows to expand their application range from the fast electromagnetic transients (EMTs) into the slower electromechanical transients. The computational kernel used for circuit based simulation of power systems, can be applied to create a unified environment for studying a wide range of power system phenomena. There are several important challenges.
This presentation is on research activities related to numerical methods for the simulation of power system transients based on the EMT approach. The generality of this approach allows to adapt it according to various accuracy needs.
It is possible to start from a steady-state solution and perform transient analysis by accurately initializing component models. The initialization process encounters several difficulties with the increasing presence of power electronics converters used in modern renewable energy sources. It becomes necessary to integrate converter equations and control strategies into the initialization process.
Computational speed is an important handicap of the EMT approach, but it can be dramatically improved through parallelisation in modern computer architectures. Automatic parallelization can be based on sparse matrix solvers with appropriate adjustments. Such adjustments include modifications for nonlinear models and switching devices due to repetitive refactorization needs. Splitting networks through delay based transmission lines is realized using matrix block-diagonal factorization. Important difficulties arise for splitting networks through other components. Ideal wires can be used for splitting with bordered block diagonal matrices, but the solution of such matrices is challenging in the presence of varying topologies and nonlinear functions. Moreover, automatic methods must be found for formulating bordered block diagonal matrices.
Other acceleration techniques are based on multi-step solution, variable time-step integration and futuristic methods, such as parallel-in-time solution of network equations. It is also demonstrated that several circuit formulation methods share common grounds and can be related to each other. The most generic formulation methods can be used to derive specific methods for various needs.
Highly accurate models are developed for EMTs, but it should be possible to relax accuracy according to numerical integration time-step and study needs. The concept of accuracy navigator orients research towards adaptive solution methods and models. Such methods should maintain efficiency and stability while remaining sufficiently accurate. The recent developments in modeling accuracy for various components, including renewable energy sources, are also presented.
Other important challenges and solutions are presented. Among those are data portability issues between applications and standardizations for models. The graphical user interface aspect is predominant and can be used to manage very large scale grids with automatic drawing techniques and conversion of data from the Common Information Model.
Bio: Jean Mahseredjian, Professor Polytechnique Montréal, Ph. D., IEEE Fellow. Jean Mahseredjian graduated from Polytechnique Montréal with a Ph.D. in 1991. From 1987 to 2004 he worked at IREQ (Hydro-Québec) on research and development activities related to the simulation and analysis of electromagnetic transients. In December 2004 he joined the faculty of electrical engineering at Polytechnique Montréal. Jean Mahseredjian is the creator and lead developer of a software for the simulation of electromagnetic transients (EMTP). His current research activities are related to the computation of power system transients for large-scale power systems.
2:00 p.m., room SF1101Title: From Modeling Speech and Language to Modeling Financial Markets
Abstract: This presentation will address whether modern artificial intelligence (AI) has the potential to transform the financial markets by examining the successful applications of AI in other industries including speech, vision, natural language, and robotics. The presentation will also discuss the technical challenges that are unique to financial modeling and investment management.
Bio: Li Deng joined Citadel, one of the global and most successful investment firms, as its Chief AI Officer in May 2017. Previously, he was Chief Scientist of AI and partner research manager at Microsoft; and prior to Microsoft, he was a professor at the University of Waterloo in Ontario and held teaching/research positions at MIT (Cambridge), ATR (Kyoto, Japan), and HKUST (Hong Kong). He is a Fellow of the IEEE, of the Acoustical Society of America, and of the ISCA. He has also been an affiliate professor at University of Washington in Seattle since 2000. In recognition of the pioneering work on disrupting speech recognition industry using large-scale deep learning, he received the 2015 IEEE SPS Technical Achievement Award for Outstanding Contributions to Automatic Speech Recognition and Deep Learning. He also received numerous best paper and patent awards for contributions to artificial intelligence, machine learning, information retrieval, multimedia signal processing, speech processing, and human language technology. Deng is an author or coauthor of six technical books on deep learning, speech processing, discriminative machine learning, and natural-language processing.
Thursday, October 18, 2018
4:00 p.m., room SF1105
Title: Extreme Optics with Zero-Index
Abstract: Nanotechnology has enabled the development of nanostructured composite materials (metamaterials) with exotic optical properties not found in nature. In the most extreme case, we can create materials which support light waves that propagate with infinite phase velocity, corresponding to a refractive index of zero. This zero index can only be achieved by simultaneously controlling the electric and magnetic resonances of the nanostructure. We present an in-plane metamaterial design consisting of silicon pillar arrays, embedded within a polymer matrix and sandwiched between gold layers. Using an integrated nano-scale prism constructed of the proposed material, we demonstrate unambiguously a refractive index of zero in the optical regime. This design serves as a novel on-chip platform to explore the exotic physics of zero-index metamaterials, with applications to super-coupling, integrated quantum optics, and phase matching.
Bio: Eric Mazur is the Balkanski Professor of Physics and Applied Physics and Area Chair of Applied Physics at Harvard University, Member of the Faculty of Education at the Harvard Graduate School of Education, and Past President of the Optical Society.
Mazur is a prominent physicist known for his contributions in nanophotonics, an internationally recognized educational innovator, and a sought after speaker. In education he is widely known for his work on Peer Instruction, an interactive teaching method aimed at engaging students in the classroom and beyond. In 2014 Mazur became the inaugural recipient of the Minerva Prize for Advancements in Higher Education. He has received many awards for his work in physics and in education and has founded several successful companies. Mazur is Chief Academic Advisor for Turning Technologies, a company developing interactive response systems for the education market. Mazur has widely published in peer-reviewed journals and holds numerous patents. He has also written extensively on education and is the author of Peer Instruction: A User’s Manual (Prentice Hall, 1997), a book that explains how to teach large lecture classes interactively, and of the Principles and Practice of Physics (Pearson, 2015), a book that presents a groundbreaking new approach to teaching introductory calculus-based physics.
Mazur is a leading speaker on optics and on education. His motivational lectures on interactive teaching, educational technology, and assessment have inspired people around the world to change their approach to teaching.
Thursday, November 15, 2018
4:00 p.m., room SF1105
Title: Combining EEG and fMRI: a powerful tool to study epilepsy
Abstract: The EEG is commonly used to help diagnose epilepsy and to localize the region of the brain from which seizures are likely to originate. The spatial resolution of EEG is however not very high and the EEG cannot see deep in the brain because of the attenuation of the electrical field with distance. If the EEG is recorded during functional MRI scanning (fMRI), it is possible to study the metabolic changes caused by these epileptic discharges (increases or decreased in the fMRI signal compared to baseline). The most intense signal changes reflect the regions with the most metabolic change, thus the regions with the most intense neuronal discharges, which are presumably the source of the epileptic discharge. These can be located anywhere in the brain.
There are many technical challenges in performing EEG-fMRI studies, mainly as a result of recording the very low amplitude EEG signal in the large (3 Tesla) and changing magnetic field of the MR scanner. It is nevertheless possible to obtain an interpretable EEG signal.
In patients who have an epilepsy that is refractory to drug treatment, it is possible to consider the surgical removal of the source of epileptic seizures. There are different methods to find this region, and EEG-fMRI has a particular place in this set of methods because it is non-invasive and it can see deep in the brain. EEG-fMRI studies have revealed regions of activation that help in the planning of surgery or in determining where in the brain one can place electrodes to find the source of seizures.
Bio: Jean Gotman received an engineering degree from the University of Paris and a PhD in Neuroscience from McGill University in Montreal. He pioneered the automatic detection of spikes and seizures during long-term EEG monitoring and made his methods widely available through Stellate, a company he created in 1986, which developed and sold all over the world equipment and software for EEG, epilepsy monitoring and polysomnography. He published over 300 peer-reviewed papers and 40 chapters. His research interests include analysis of the EEG, mechanisms of epileptogenesis, seizure generation and spread in humans, High Frequency Oscillations and functional imaging in the diagnosis and study of epilepsy. He received the Research Recognition Award from the American Epilepsy Society, the Pierre Gloor Award of the American Clinical Neurophysiology Society, the Penfield Award of the Canadian League against Epilepsy, was named Ambassador for Epilepsy by the International League against Epilepsy, and gave the Lennox-Lombroso lecture at the American Epilepsy Society.
Thursday, November 22, 2018
4:00 p.m., room SF1105
Title: Energy-Efficient Edge Computing for AI-driven Applications
Abstract: Edge computing near the sensor is preferred over the cloud due to privacy and/or latency concerns for a wide range of applications including robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. In this talk, we will describe how joint algorithm and hardware design can be used to reduce energy consumption while delivering real-time and robust performance for applications including deep learning, computer vision, autonomous navigation and video/image processing. We will show how energy-efficient techniques that exploit correlation and sparsity to reduce compute, data movement and storage costs can be applied to various AI tasks including object detection, image classification, depth estimation, super-resolution, localization and mapping.
Bio: Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for portable multimedia applications, including computer vision, deep learning, autonomous navigation, and video process/coding. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee of ITU-T and ISO/IEC standards body during the development of High Efficiency Video Coding (HEVC), which received a Primetime Emmy Engineering Award. She is a co-editor of the book entitled “High Efficiency Video Coding (HEVC): Algorithms and Architectures” (Springer, 2014).
Prof. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she received the Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT. She is a recipient of the 2018 Facebook Hardware & Software Systems Research Award, the 2017 Qualcomm Faculty Award, the 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Research Program (YIP) Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2007 DAC/ISSCC Student Design Contest Award, and a co-recipient of the 2017 CICC Outstanding Invited Paper Award, the 2016 IEEE Micro Top Picks Award and the 2008 A-SSCC Outstanding Design Award.
For more information about research in the Energy-Efficient Multimedia Systems Group at MIT visit: http://www.rle.mit.edu/eems/
Thursday, February 14, 2019
4:00 p.m., room SF1105
Title: Distributed protocols for cooperative multi-robot systems
Abstract: In cooperative multi-robot systems, there is a group of robots that seek to achieve a collective task as a team. Each individual robot makes decisions based on available local information as well as limited communications with neighboring robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized control paradigm, both measurements and decisions are distributed among multiple actors. This talk surveys various results for cooperative robotics based on methods drawn from game theory and distributed optimization, with applications to area coverage, cooperative pursuit, and self-assembly.
Bio: Jeff S. Shamma is a Professor of Electrical Engineering at the King Abdullah University of Science and Technology (KAUST) and the Director of the Center of Excellence for NEOM Research at KAUST. Shamma received a Ph.D. in systems science and engineering from MIT in 1988. He has held faculty positions at the University of Minnesota, The University of Texas at Austin, and the University of California, Los Angeles, and was the Julian T. Hightower Chair in Systems & Control in the School of Electrical and Computer Engineering at Georgia Tech. Shamma is a Fellow of the IEEE and the IFAC (International Federation of Automatic Control), and a recipient of the NSF Young Investigator Award, American Automatic Control Council Donald P. Eckman Award, and Mohammed Dahleh Award. Shamma is currently the deputy editor-in-chief for the IEEE Transactions on Control of Network Systems and a Distinguished Lecturer of the IEEE Control Systems Society.
Thursday, March 14, 2019
4:00 p.m., room SF1105
Title: Decentralization in Energy Systems: Absorbing Solar and Storage into Grids
Abstract: As prices for solar photovoltaics and battery energy storage plummet, grids around the globe are undergoing tremendous changes. How should we design and operate grids in the future in the presence of these technologies? This talk will cover some of my group’s recent efforts to answer this question. First, I will focus on a new approach to decentralized network optimization – a variant of the primal-dual subgradient method – that can be used to enable grid-integration of distributed energy resources such as solar photovoltaics, batteries and electric vehicles. I will then discuss how grids should be built in the future when distributed energy resource costs are so low. Using a simple concept called an iso-reliability curve, I will explain a method to identify cost-optimal fully decentralized systems – i.e. standalone solar home systems. After applying this method to a large solar resource dataset, I will present results indicating that in many unelectrified parts of the world, future decentralized systems will be able to deliver electricity at costs and reliabilities better than existing centralized grids.
Bio: Duncan Callaway is an Associate Professor of Energy and Resources at the University of California, Berkeley. He is also a faculty affiliate in Electrical Engineering and Computer Science, and a faculty scientist at Lawrence Berkeley Laboratory. He received his PhD from Cornell University. He has held engineering positions at Davis Energy Group and PowerLight Corporation, and academic positions at UC Davis, the University of Michigan and UC Berkeley. Duncan teaches courses on electric power systems and at the intersection of statistical learning and energy. His research focuses on grid integration of renewable electricity and models and control strategies for demand response, electric vehicles and electricity storage.
4:00 p.m., room SF1105Title: Consumer-focused High Performance Computing Architectures
Abstract: The technology landscape is incredibly exciting today, with high-performance computation transforming many aspects of society and daily life. New innovations appear seemingly daily in areas of entertainment, transportation, communication, and health care, just to name a few. Emerging practical applications of virtual and augmented reality, autonomous vehicles, and automated reasoning will place new demands on our computing architectures. While the computational appetite of emerging applications in these spaces appear to be growing without bound, the historical technology scaling trends which have provided the fundamental horsepower for computing over the last 50 years, are slowing substantially. This talk will discuss some of the cataclysmic trends in consumer applications of high-performance computing, and focus on opportunities for computer designers. It will also present challenges associated with emerging deep neural networks (DNNs) and describe recent works that (1) enable larger and more complex networks to be trained on compute devices with limited memory capacity; and (2) reduce the memory and computation footprints of DNNs at inference time, enabling them to run with vastly improved energy efficiency.
Bio: Dr. Stephen W. Keckler is the Vice President of Architecture Research at NVIDIA and an Adjunct Professor of Computer Science at the University of Texas at Austin, where he served on the faculty from 1998-2012. His research interests include parallel computer architectures, high-performance computing, energy-efficient architectures, and embedded computing. Dr. Keckler is a Fellow of the ACM, a Fellow of the IEEE, an Alfred P. Sloan Research Fellow, and a recipient of the NSF CAREER award, the ACM Grace Murray Hopper award, the President’s Associates Teaching Excellence Award at UT-Austin, and the Edith and Peter O’Donnell award for Engineering. He earned a B.S. in Electrical Engineering from Stanford University and M.S. and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology.
Thursday, Oct. 5, 2017
4:00 p.m., room SF1105
Title: Broadband Electromagnetic Modelling and Stochastic Signal Analysis of Multiconductor Interconnections
Abstract: This lecture addresses the statistical modelling and simulation of high-speed interconnections with uncertain physical properties and terminations. Typical quantities of interest are S-parameters, crosstalk, Bit Error Rates and eye-diagrams. Focus is on on-board and on-chip multiconductor interconnections modelled by the telegrapher equations. First, the meaning of the classical resistance, inductance, conductance and capacitance (RLGC) per unit-of-length parameters in the presence of good conductors and semiconductors, is revisited. Next, attention is devoted to an efficient numerical approach to obtain broadband RLGC-data for arbitrary cross-sections, while accurately taking skin-effect and current crowding into account. This efficient numerical technique lays the foundation for the subsequent statistical signal analysis where stochastic variations in the physical link properties and in the load conditions are included. It is shown how Polynomial Chaos based approaches can be advantageously harnessed to beat the traditional Monte Carlo technique and how these techniques can be integrated in SPICE-compatible simulators. Finally, we consider the case in which only a limited set of measurements or simulations of a passive interconnection, exhibiting variability, are available, without any further physical or geometrical insight. It is investigated how machine learning techniques allow to create a realistic and physically consistent stochastic model of that interconnection and its signal transfer properties.
Bio: Daniël De Zutter was born in 1953. He received his M. Sc. Degree in Electrical Engineering from the University of Gent in 1976. In 1981 he obtained a Ph. D. degree and in 1984 he completed a thesis leading to a degree equivalent to the French Aggrégation or the German Habilitation. He is a full professor of electromagnetics. His research focuses on all aspects of circuit and electromagnetic modelling of high-speed and high-frequency interconnections and packaging, on Electromagnetic Compatibility (EMC) and numerical solutions of Maxwell’s equations. As author or co-author he has contributed to more than 250 international journal papers and 270 papers in conference proceedings. In 2000 he was elected to the grade of Fellow of the IEEE. Between 2004 and 2008 he served as the Dean of the Faculty of Engineering and Architecture of Ghent University and was the head of the Department of Information Technology and of its Electromagnetic Research group until February 2017.
Thursday, Nov. 2, 2017
4:00 p.m., room SF1105
Title: From Biomedical Imaging to Online Blogs: Graph Signal Processing
Abstract: I will present a path from classification in biomedical imaging to online blogs, where a common thread is graph signal processing, a theoretical framework that generalizes fundamental concepts of classical signal processing from regular domains, such as lines and rectangular lattices, to general graphs. It is particularly applicable to domains such as physical, engineering, and social, where signals are characterized by irregular structure. Signal processing on graphs has found multiple applications, including approximation, sampling, classification, inpainting and clustering, and I will describe some of these.
Bio: Jelena Kovačević received a Ph.D. degree from Columbia University. She then joined Bell Labs, followed by Carnegie Mellon University in 2003, where she is currently the Hamerschlag University Professor and Head of the Department of ECE, and Professor of BME. She received the Dowd Fellowship at CMU, Belgrade October Prize, and the E.I. Jury Award at Columbia University. She is a coauthor on an SP Society award-winning paper and is a coauthor of the textbooks Wavelets and Subband Coding and Foundations of Signal Processing. Dr. Kovacevic is the Fellow of the IEEE and was the Editor-in-Chief of the IEEE Transactions on Image Processing. She was a keynote speaker at a number of meetings and has been involved in organizing numerous conferences. Her research interests include multiresolution techniques, graphs, biomedical imaging, and smart infrastructure.
Thursday, Nov. 30, 2017
4:00 p.m., room SF1105
Title: On the Dynamics of Influence and Appraisal Networks
Abstract: This talk will present models for the evolution of interpersonal influences, interpersonal appraisals, and social power in a group of individuals. Specifically, we will propose learning models in two scenarios: groups who discuss and form opinions along a sequence of issues, and groups who execute a sequence of decomposable tasks. In both scenarios we establish the emergence of rational optimal behavior, or lack thereof, as a result of the natural dynamical evolution of interpersonal appraisals and influence structures. Our multiagent models and analysis results are grounded in influence networks from mathematical sociology, replicator dynamics from evolutionary games, and transactive memory systems from organization science.
Bio: Francesco Bullo is a Professor with the Mechanical Engineering Department and the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. His research interests focus on network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of “Geometric Control of Mechanical Systems” (Springer, 2004) and “Distributed Control of Robotic Networks” (Princeton, 2009); his forthcoming “Lectures on Network Systems” is available on his website. He received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a Fellow of IEEE and IFAC. He has served on the editorial boards of IEEE, SIAM, and ESAIM journals, and will serve as IEEE CSS President in 2018.
Thursday, Jan. 18, 2018
4:00 p.m., room SF1105
Title: Breaking the Speed Barrier of Today’s Line Protective Relays
Abstract: Today’s electric power grids interconnect an increasing number of inverter-based sources fed from renewable energy resources: wind turbines and solar panels. DC links are being planned to transport vast amounts of energy across continents linking multiple AC grids. As a result of these changes, today’s grids are more difficult to protect against short-circuit currents and other abnormal events than in the past.
Traditional protection devices, such as transmission line relays, respond to narrow-band-filtered voltages and currents, and thus—for detecting short-circuits—they count on the power sources to supply fault current toward the short-circuit location. In its 100-year history, the engineering field of power system protection has developed sophisticated protection principles. These principles, however, take advantage of the many properties of a traditional source—a large synchronous generator. When applied near inverter-based sources, these principles are tested and may underperform or fail. The industry is going through an adjustment phase, working on protection solutions that do not depend on the fixed properties of a synchronous generator, but work with inverter-based sources that use arbitrary and proprietary control schemes when generating output currents.
This lecture focuses on novel protection principles based on traveling waves and incremental signals. These protection principles respond to transients fed with the energy stored in the grid prior to a short-circuit, and therefore they are much less dependent on the sources’ outputs during the short circuit.
These protection principles offer dramatic improvements in the operating times and sensitivity. They are good examples of a disruptive technology. This lecture introduces several of the new principles and explains why only now these principles can be fully developed and can make their way into practice. These enablers include high-speed analog-to-digital converters and vast processing power afforded at industrial-grade component ratings, high-speed communications, precise wide-area timing, and modern simulation tools. The lecture will use actual field cases and actual devices to demonstrate the principles.
Bio: Dr. Bogdan Kasztenny is a Senior Engineering Director in R&D at Schweitzer Engineering Laboratories, Inc. He has over 27 years of expertise in power system protection and control, including 10 years of academic career and 17 years of industrial experience, developing, promoting, and supporting many protection and control products. Bogdan is an IEEE Fellow, Senior Fulbright Fellow, Canadian representative of CIGRE Study Committee B5, and a registered professional engineer in the province of Ontario. Bogdan serves on the Western Protective Relay Conference Program Committee and on the Developments in Power System Protection Conference Program Committee. Bogdan has authored over 200 technical papers and holds over 30 patents. Since 2014, Bogdan has been heavily involved in research, development, and support of protective relays and fault locators based on traveling waves and superimposed components.
Thursday, Feb. 1, 2018
4:00 p.m., room SF1105
Title: Computation and uncertainty: The Past, Present and Future of Control
Abstract: Reflecting on our work over the last 40 years I found that it was dominated by two themes: computation and uncertainty. I will describe how the rapidly increasing computational resources have affected our approaches to deal with uncertainty in feedback control. The talk will be illustrated by examples from process control and other application areas like automotive and power systems.
Bio: Manfred Morari was head of the Department of Information Technology and Electrical Engineering at ETH Zurich from 2009 to 2012 and head of the Automatic Control Laboratory from 1994 to 2008. Before that he was the McCollum-Corcoran Professor of Chemical Engineering and Executive Officer for Control and Dynamical Systems at the California Institute of Technology. From 1977 to 1983 he was on the faculty of the University of Wisconsin. He obtained the diploma from ETH Zurich and the Ph.D. from the University of Minnesota, both in chemical engineering. His interests are in constrained and robust control. Morari’s research is internationally recognized. The analysis techniques and software developed in his group are used in universities and industry throughout the world. He has received numerous awards, including the Eckman Award, Ragazzini Award and Bellman Control Heritage Award from the American Automatic Control Council; the Colburn Award, Professional Progress Award and CAST Division Award from the American Institute of Chemical Engineers; the Control Systems Technical Field Award and the Bode Lecture Prize from IEEE. He is a Fellow of IEEE, AIChE and IFAC. In 1993 he was elected to the U.S. National Academy of Engineering, in 2015 to the UK Royal Academy of Engineering. Manfred Morari served on the technical advisory boards of several major corporations.
Thursday, Feb. 8, 2018
4:00 p.m., room SF1105
Title: Wireless Transceivers: Architectural and Circuit Evolution toward a Complete Integrated Solution
Abstract: As we move toward 5G, improvements by 1,000x in system capacity and 10x in data rate, energy and spectral efficiency are required to satisfy the projected demands. To this end, new features will be introduced (e.g. un-licensed bands re-use, channel bonding, Full Duplex) and existing ones (e.g. MIMO, Carrier Aggregation) will be enhanced. This will drastically increase terminal complexity while at the same time lower cost, size and power consumption will be required. Within this scenario, the only way to keep meeting the expectations of the market is by increasing the level of integration of the terminal. This talk analyzes the progresses already achieved and those expected with respect to the RF transceiver. First the evolution of the architecture of a multi-standard transceiver up to the present time associated with the CMOS technology scaling. After, a discussion on the architecture of future transceiver capable to satisfy the requirements of the new standard like Saw/Duplexer-less implementation, full-duplex architecture, very broadband channels etc. will be provided.
Bio: Rinaldo Castello (S’78–M’78–SM’92–F’99) graduated from the University of Genova (summa cum laude) in 1977 and received the M.S. and the Ph. D. from the University of California, Berkeley, in ‘81 and ‘84. From ‘83 to ‘85 he was Visiting Assistant Professor at the University of California, Berkeley. In 1987 he joined the University of Pavia where he is now a Full Professor. He consulted for ST-Microelectronics, Milan, Italy up to 2005 in ‘98 he started a joint research centre between the University of Pavia and ST and was its Scientific Director up to ‘05. He promoted the establishing of several design centre from multinational IC companies around Pavia, among them Marvell for which he was a consultant from 2005 to 2016. He is now consulting for InvenSense. Rinaldo Castello has been a member of the TPC of the European Solid State Circuit Conference (ESSCIRC) since 1987 and of the International Solid State Circuit Conference (ISSCC) from ‘92 to ‘04. He was Technical Chairman of ESSCIRC ’91 and General Chairman of ESSCIRC ‘02, Associate Editor for Europe of the IEEE J. of Solid-State Circ. from ’94 to ’96 and Guest Editor of its July ’92 special issue. From 2000 to 2007 he has been Distinguished Lecturer of the IEEE Solid State Circuit Society. Prof Castello was named one of the outstanding contributors for the first 50 and 60 years of ISSCC and a co-recipient of the Best Paper Award at the 2005 Symposium on VLSI of the Best Invited Paper Award at the 2011 CICC and of the Best Evening Panel Award at ISSCC 2012 and 2015. He was one of the two European representatives at the Plenary Distinguished Panel of ISSCC 2013 and the Summer 2014 Issue of the IEEE Solid State Circuit Magazine was devoted to him. Rinaldo Castello is a Fellow of the IEEE.
Thursday, April 5, 2018
4:00 p.m., room SF1105
Title: Black Holes Last Tango: LIGO and the Dawn of Gravitational-wave Astronomy
Abstract: The first direct detections of gravitational waves in late 2015 were made possible by a dedicated forty-year quest to design, build, and operate LIGO, the Laser Interferometer Gravitational-wave Observatory. In this talk, I’ll give an introduction to gravitational waves and what makes them so difficult to detect and at the same time such powerful and unique probes of the universe. I’ll also present how we detect gravitational waves using fantastically sensitive interferometers, and talk about the first detections of colliding black holes and the potentially profound implications. Time permitting, I’ll give a preview of where LIGO intends to go in the next decade and beyond.
Bio: David Reitze holds joint positions as the Executive Director of the LIGO Laboratory at the Caltech and a Professor of Physics at the University of Florida. He has authored more than 250 publications, and is a Fellow of both the American Physical Society and the Optical Society. He is a member of the LIGO Scientific Collaboration (LSC) that was awarded the 2016 Special Breakthrough Prize in Fundamental Physics, the 2016 Gruber Foundation Cosmology Prize, and the 2017 Rossi Prize of the High Energy Astrophysics Division of the American Astronomical Society. He served as the elected Spokesperson of the LSC from 2007-2011, and was recently awarded the US National Academy of Sciences Award for Scientific Discovery.
Thursday, Oct. 6, 2016
3:00 p.m., room SF1105
Title: Randomness, Everlasting Security, and Undetectability
Abstract: Security and privacy are fundamental concerns in today’s world. These concerns have become particularly prominent with Snowden’s revelations of the presence of the NSA in our daily lives. These revelations have shown that traditional cryptographic techniques do not provide the security as was expected calling into question how security and privacy can be provided. In this talk we investigate how randomness in the environment can be used to provide everlasting security and undetectability (privacy) in wireless networks. In the first part of the talk we describe a practical way to harness this randomness to provide and improve the security of wireless communications. We introduce the notion of “dynamic secrets”, information shared by two parties, Alice and Bob, engaged in communication and not available to an adversary, Eve. The basic idea is to dynamically generate a series of secrets from randomness present in the in wireless environment. These dynamic secrets exhibit interesting security properties and offer a robust alternative to cryptographic security protocols. We present a simple algorithm for generating these secrets and using them to ensure secrecy. In some situations, Alice and Bob may want not only to secure their communications but to keep it private. In the second part of our talk we focus on the use of randomness to conceal any possible communications between Alice and Bob. Here the challenge is for Alice to communicate with Bob without an adversary, Willie the warden, ever realizing that the communication is taking place. Specifically, we establish that Alice can send (√??) bits (and no more) to Bob in time ?? over a variety of wireless and optical channels. Moreover, we report experimental results that corroborate these theoretical results and report on more recent developments.
Bio: Don Towsley holds a B.A. in Physics (1971) and a Ph.D. in Computer Science (1975) from the University of Texas. He is currently a Distinguished Professor at the University of Massachusetts in the College of Information & Computer Sciences. He has held visiting positions at numerous universities and research labs. His research interests include networks and performance evaluation. He currently serves as a Co-Editor-in-Chief of ACM Transactions on Modeling and Performance Evaluation of Computer Systems (TOMPECS) and previously as Editor-in-Chief of IEEE/ACM Transactions on Networking, and on numerous editorial boards. He has served as Program Co-chair of several conferences including INFOCOM 2009. He has received numerous awards including the 2007 IEEE Koji Kobayashi Award, 2007 ACM SIGMETRICS Achievement Award, and 2008 ACM SIGCOMM Achievement Award, and numerous paper awards including a 2008 ACM SIGCOMM Test-of-Time Paper Award and the 2012 ACM SIGMETRICS Test-of-Time Award. Last, he has been elected Fellow of both the ACM and IEEE.
Thursday, Nov. 10, 2016
3:00 p.m., room SF1105
Title: Novel High Capacity Fibers for Optical Communication
Abstract: Single mode fibers have served as the backbone of the communication infrastructure for over 3 decades, during which the capacity of the single mode fiber was continuously scaled by 3 or more orders of magnitude, most notably by the introduction of wavelength multiplexing and more recently by moving to digital-coherent transmission techniques. The capacity provided by the single mode fiber is now approaching the theoretical limit imposed by the fiber nonlinearity and Shannon’s capacity formula, and therefore the use of new fibers supporting multiple spatial modes have been proposed. In this contribution we will review the major technological breakthroughs in multimode and multicore fiber-optic long distance communication based on multiple-input multiple-output (MIMO) digital signal processing.
Bio: Dr. Roland Ryf is a Distinguished Member of Technical Staff at Nokia Bell Labs, Holmdel, NJ. He received a diploma and Ph.D. in physics from the Swiss Federal Institute of Technology (ETH) Zürich, Switzerland, working on nonlinear optics and optical parallel processing. After joining Bell Labs in May 2000 he has been working on MEMS based large port-count optical cross-connect switches, high resolution optical wavelength filters, multimode wavelength-selective switches and amplifiers, and numerous first experimental demonstrations of long distance high capacity space-division multiplexed transmission over multimode fibers and coupled-core multicore fibers. Dr. Ryf is an OSA fellow and authored/coauthored over 200 journal and conference publications and holds 40 patents.
Thursday, Nov. 24, 2016
3:00 p.m., room SF1105
Title: From Deep Learning to AI
Abstract: Research in artificial intelligence has known surprising breakthroughs in recent years, thanks in great part to progress in deep learning. So much so that some people now express fears about the potential consequences, whereas just a few years ago the hope for reaching human-level intelligence was gone from most radar screens. Deep learning methods are approaches to machine learning, which allow computers to obtain the knowledge required for intelligent behaviour through learning from examples. More specifically, deep learning algorithms are based on learning multiple levels of representation. Deep learning has already been extremely successful in speech recognition, computer vision and is quickly rising as a major tool for natural language processing. We motivate deep learning and review recent theoretical results on their expressive power and their optimization landscape. We close on some of the exciting challenges ahead on fronts such as unsupervised learning, especially via deep generative models, the role of attention mechanisms and natural language understanding.
Bio: Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research at the Université de Montréal, head of the Montréal Institute for Learning Algorithms (MILA),CIFAR Program co-director of the CIFAR Neural Computation and Adaptive Perception program and Canada Research Chair in Statistical Learning Algorithms. His main research ambition is to understand principles of learning that yield intelligence. He teaches a graduate course in Machine Learning (IFT6266) and supervises a large group of graduate students and post-docs. His research is widely cited (over 40,000 citations found by Google Scholar in mid-2016, with an H-index of 84). He is currently action editor for the Journal of Machine Learning Research, associate editor for the Neural Computation journal, editor for Foundations and Trends in Machine Learning, and has been associate editor for the Machine Learning Journal and the IEEE Transactions on Neural Networks. Yoshua Bengio was Program Chair for NIPS’2008 and General Chair for NIPS’2009 (NIPS is the flagship conference in the areas of learning algorithms and neural computation). Since 1999, he has been co-organizing the Learning Workshop with Yann Le Cun, with whom he has also created the International Conference on Representation Learning (ICLR).He has also organized or co-organized numerous other events, principally the deep learning workshops and symposia at NIPS and ICML since 2007.
Thursday, Jan. 19, 2017
4:00 pm, room SF1105
Title: Nanotechnology for Massively-Parallel, Multi-Physical Interrogation of Brain Activity
Abstract: Although our understanding of the properties of individual neurons and their role in brain computations has advanced significantly over the past several decades, we are still far from elucidating how complex assemblies of neurons – that is, brain circuits – interact to process information. In 2011, six U.S. scientists from different disciplines banded together, outlined a vision , and managed to convince the Obama administration of the unprecedented opportunity that now exists to launch a coordinated, large-scale international effort to map brain activity. This culminated in the U.S. BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies), which was launched in 2013. Our perspective was predicated, in part, on the current level of maturity of diverse fields of nanotechnology and silicon very-large-scale integration (VLSI) that can now be coalesced to create unprecedented tools for massively-parallel, multi-physical interrogation of brain activity. I will outline the immense complexity of such pursuits, the hopes we articulated, survey the existing technological landscape for assembling the requisite instrumentation, and then focus upon our own collaborative efforts toward these goals. I will highlight opportunities in the new field we’ve termed integrated neurophotonics for realizing this vision; it leverages advances in integrated nanophotonics, optogenetic reporters and effectors for neural recording and stimulation, and recent developments in implantable, multi-site neural nanoprobes based on silicon VLSI.
 Alivisatos A.P., Chun M., Church G.M., Greenspan R.J., Roukes M.L., Yuste R., The Brain Activity Map project and the challenge of functional connectomics. Neuron 74, 970-4 (2012).
Bio: Michael Roukes is the Robert M. Abbey Professor of Physics, Applied Physics, and Bioengineering at the California Institute of Technology. His scientific interests range from quantum measurement to applied biotechnology with a unifying theme of the development, very-large-scale integration, and application of complex nanosystems to precision measurements in physics, the life sciences, and medicine. Roukes was the founding Director of Caltech’s Kavli Nanoscience Institute (KNI) from 2003-2006. In 2007, he co-founded the Alliance for Nanosystems VLSI (very-large-scale integration) with scientists and engineers at CEA/LETI in Grenoble, which maintains a $B-scale microelectronics research foundry. He then continued as co-director of Caltech’s KNI from 2008 until 2013. Since then he has returned to full-time pursuit of research efforts with his group and collaborators worldwide. Concurrent with his Caltech appointment, he has held a Chaire d’Excellence in nanoscience in Grenoble, France since 2008. Among his honors, Roukes is a Fellow of the American Physical Society, a recipient of the NIH Director’s Pioneer Award, and has been awarded Chevalier (Knight) dans l’Ordre des Palmes Academiques by the Republic of France.
Thursday, Jan. 26, 2017
4:00 p.m., room SF1105
Title: Modelling and Control of Load Ensembles
Abstract: Growth in non-dispatchable generation, particularly renewable sources, is placing greater reliance on demand response (non-disruptive load control) for maintaining generation-load balance. Participation in fast-acting demand response can be maximized through aggregation of many small electrical loads. Numerous control strategies have been proposed for coordinating the behaviour of load ensembles to assist in power system operations. Modelling the natural and controlled response of such distributed systems is, however, quite challenging. Various modelling formalisms will be presented and their characteristics discussed. The dynamic behavior of load ensembles is strongly dependent on stochasticity and disorder within the load population. These effects will be illustrated and methods of capturing uncertainty within aggregate models will be considered. Load ensembles are inherently nonlinear and may display quite complicated dynamics, for example bifurcations associated with synchronization. Accordingly, controls must be carefully designed to avoid such complex behaviour. Case studies will illustrate the capability of load ensembles to provide fast-acting frequency regulation and to track the output of renewable generation.
Bio: Ian A. Hiskens holds the Vennema Professor of Engineering endowed chair in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He has held prior appointments in the Queensland electricity supply industry (for ten years), and various universities in Australia and the United States. Dr Hiskens’ research interests lie at the intersection of power system analysis and systems theory. His recent activities have focused on systems issues arising from large-scale integration of new forms of generation, and on the development of non-disruptive load control strategies. Other research interests include nonlinear and hybrid dynamical systems. He is actively involved in various IEEE societies, and recently completed his term as Vice-President for Finance of the IEEE Systems Council. He has served as an Associate Editor of IEEE Transactions on Power Systems, IEEE Transaction on Control Systems Technology and IEEE Transactions on Circuits and Systems. He is a Fellow of IEEE, a Fellow of Engineers Australia, and a Chartered Professional Engineer in Australia.
Title: Smart Power Devices and ICs with Wide and Extreme Bandgap Semiconductors
Thursday, March 16, 2017
4:00 p.m., room SF1105
Abstract: Silicon has long been the dominant, often exclusive, semiconductor of choice for high voltage power devices and ICs. Over the last decade, power switching devices made of two wide bandgap (WBG) semiconductors, SiC and GaN, are impacting power electronics systems with their commercial availability and performance improvement, and hence less power loss and more energy efficient, over conventional silicon counterparts. In this talk, the present status of the research, development and/or commercialization, as well as cost-effectiveness of smart power devices and ICs using wide (SiC, GaN) and extreme (Ga2O3, diamond, AlN) bandgap semiconductors in advanced energy efficient electronics systems is presented. The technology obstacles and needs faced in these semiconductors as well as future trend in these power devices and ICs are also discussed.
Bio: Prof. T. Paul Chow received his Ph.D. in Electrical Engineering from RPI in 1982. He was a member of the technical staff at GE Corporate Research and Development from 1977 to 1989. Since 1989, he has been with RPI, where he is now professor of the Electrical, Computer and Systems Engineering Department. He has been working in the power semiconductor device area since 1982. His present research activities include high-voltage silicon, GaAs and wide bandgap (particularly SiC and GaN) semiconductor power devices and ICs. He has published over 150 papers in scientific journals, has contributed eight chapters in technical textbooks, and has procured over fifteen patents. He is a Fellow of the IEEE.
Thursday, March 30, 2017
4:00 p.m., room SF1105
Title: Nanophotonics in material-systems of large sizes
Abstract: Recent nano-fabrication developments enabled implementation of many nanophotonic techniques to macroscopic scales, which is crucial for many applications of interest (e.g. energy conversion, displays, lighting). Some of our recent work in this area will be presented.
Bio: Marin Soljacic completed his undergraduate studies at MIT (1996), and PhD studies at Princeton (2000). He became a Professor in physics at MIT in 2005. He is also a founder of WiTricity Corporation (2007), which aims to commercialize wireless power transfer. His main research interests are in electromagnetic phenomena, focusing on nanophotonics, non-linear optics, and wireless power transfer. He is a co-author of 190 scientific articles, and more than 90 issued US patents. He is the recipient of the Adolph Lomb medal from the Optical Society of America (2005), and the TR35 award of the Technology Review magazine (2006). In 2008, he was awarded a MacArthur fellowship “genius” grant. He is a correspondent member of the Croatian Academy of Engineering since 2009. In 2011 he became a Young Global Leader (YGL) of the World Economic Forum. In 2014, he was awarded Blavatnik National Award.
Thursday, April 13, 2017
4:00 p.m., room SF1105
Title: Incremental Methods for Additive Convex Cost Optimization
Abstract: Motivated by machine learning problems over large data sets and distributed optimization over networks, we consider the problem of minimizing the sum of a large number of convex component functions. We study incremental gradient methods for solving such problems, which process component functions sequentially one at a time. We first consider deterministic cyclic incremental gradient methods (that process the component functions in a cycle) and provide new convergence rate results under some assumptions. We then consider a randomized incremental gradient method, called the random reshuffling (RR) algorithm, which picks a uniformly random order/permutation and processes the component functions one at a time according to this order (i.e., samples functions without replacement in each cycle). We provide the first convergence rate guarantees for this method that outperform its popular with-replacement counterpart stochastic gradient descent (SGD). We finally consider incremental aggregated gradient methods, which compute a single component function gradient at each iteration while using outdated gradients of all component functions to approximate the global cost function gradient, and provide new linear rate results. This is joint work with Mert Gurbuzbalaban and Pablo Parrilo.
Bio: Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and the Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. She is the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering and Computer Science (EECS) Department at the Massachusetts Institute of Technology. She is also the associate head of EECS. Her research expertise includes optimization theory, with emphasis on nonlinear programming and convex analysis, game theory, with applications in communication, social, and economic networks, distributed optimization and control, and network analysis with special emphasis on contagious processes, systemic risk and dynamic control. Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the Class of 1943 Career Development Chair, the inaugural Steven and Renee Innovation Fellowship, and the 2014 Spira teaching award. She served on the Board of Governors of the Control System Society in 2010 and was an associate editor for IEEE Transactions on Automatic Control. She is currently the area co-editor for a new area for the journal Operations Research, entitled “Games, Information and Networks. She is the co-author of the book entitled “Convex Analysis and Optimization” (Athena Scientific, 2003).
3:00 p.m., room SF1105
Title: Fundamental Limits on Information Security and Privacy
Abstract: As has become quite clear from recent headlines, the ubiquity of technologies such as wireless communications and on-line data repositories has created new challenges in information security and privacy. Information theory provides fundamental limits that can guide the development of methods for addressing these challenges. After a brief historical account of the use of information theory to characterize secrecy, this talk will review two areas to which these ideas have been applied successfully: wireless physical layer security, which examines the ability of the physical properties of the radio channel to provide confidentiality in data transmission; and utility-privacy tradeoffs of data sources, which quantify the balance between the protection of private information contained in such sources and the provision of measurable benefits to legitimate users of them. Several potential applications of these ideas will also be discussed.
Bio: H. Vincent Poor is Dean of the School of Engineering and Applied Science at Princeton University, where he is also the Michael Henry Strater University Professor. His research interests are primarily in the areas of information theory and signal processing, with applications in wireless networks and related fields. Among his publications is the recent book Principles of Cognitive Radio (Cambridge University Press, 2013). An IEEE Fellow, Dr. Poor is a member of the National Academy of Engineering and the National Academy of Sciences, and is a foreign member of the Royal Society. Recent recognition of his work includes the 2014 URSI Booker Gold Medal, and honorary doctorates from several universities.
Thursday, Oct. 22, 2015
3:00 p.m., room SF1105
Title: Snake Robots: A robotic solution for firefighting, search and rescue, and subsea IMR operations
Abstract: Snake robots are motivated by the long, slender and flexible body of biological snakes, which allows them to move in virtually any environment on land and in water. Since the snake robot is essentially a manipulator arm that can move by itself, it has a number of interesting applications including firefighting applications and search and rescue operations. In water, the robot is a highly flexible and dexterous manipulator arm that can swim by itself like a snake. This highly flexible snake-like mechanism has excellent accessibility properties; it can for instance access virtually any location on a subsea oil & gas installation, move into the confined areas of ship wrecks, or be used for observation of biological systems. Furthermore, not only can the swimming manipulator access narrow openings and confined areas, but it can also carry out highly complex manipulation tasks at this location since manipulation is an inherent capability of the system. By incorporating the propulsion system and the manipulation capabilities in the same mechanical structure, this vehicle becomes highly compact and is able to bring inspection and intervention capabilities to locations where ROVs today cannot operate. In the longer term, this may enable reduced size and cost of subsea production systems.
In this talk, I will present recent research results on modelling and control of snake robots, including both theoretical and experimental results.
Bio: Kristin Y. Pettersen is a Professor in the Department of Engineering Cybernetics, NTNU where she has been a faculty member since 1996. She was Head of Department 2011-2013, Vice-Head of Department 2009-2011, and Director of the NTNU ICT Programme of Robotics 2010-2013. In the period 2013 – 2022 she is also Key Scientist at the CoE Centre for Autonomous Marine Operations and Systems (AMOS). She is CEO of the spin-off company SwimM AS. She received the MSc and PhD degrees in Engineering Cybernetics at NTNU, Trondheim, Norway, in 1992 and 1996. She has published 200 international papers for conferences and journals, and her research interests focus on nonlinear control of mechanical systems with applications to robotics, with a special emphasis on marine robotics and snake robotics. She has edited a Springer Verlag book on Group Coordination and Cooperative control, and is co-author of one Springer Verlag book on Snake Robots, and another on Modeling and Control of Vehicle-Manipulator Systems. In 2008, she was a Visiting Professor at the Section for Automation and Control, University of Aalborg, Denmark, and in 1999 she was a Visiting Fellow at the Department of Mechanical and Aerospace Engineering, Princeton University. In 2006, she and her co-authors were awarded the IEEE Transactions on Control Systems Technology Outstanding Paper Award for: Global Uniform Asymptotic Stabilization of an Underactuated Surface Vessel: Experimental Results (K.Y. Pettersen, F. Mazenc and H. Nijmeijer). She has served as Associate Editor for several conferences, including, the IEEE Conference on Decision and Control, the IEEE Conference on Robotics and Automation, and the IEEE/RSJ International Conference on Intelligent Robots and Systems. She has served as a member of the Editorial Board of Simulation Modeling Practice and Theory, and is an Associate Editor of the IEEE Transactions on Control Systems Technology and the IEEE Control Systems Magazine. She was a member of the Board of Governors of IEEE Control Systems Society 2012 – 2014, and she has also held several board positions in industrial and research companies.
Thursday, Nov. 12, 2015
3:00 p.m., room SF1105
Title: Control and Optimization of Smart Grid
Abstract: We are at the cusp of a historic transformation of our energy system to a more sustainable form. It is driven by accelerating penetration of renewables, electric vehicles, smart buildings, smart appliances, distributed energy storage, and enabled by advances in power electronics and more extensive integration of information technologies with the power infrastructure. It will transform our power network into the largest and most complex Internet of Things. This will be a huge risk as these distributed energy resources (DER) can introduce random, frequent and large fluctuations in supply, demand, voltage and frequency. It will also be a tremendous opportunity as these DERs are intelligent, and hence capable of implementing active control that can improve system robustness, security, and efficiency. In this talk, I will describe some of the challenges that will arise in future smart grid and summarize some of our research to address them. In particular, I will discuss a recent approach to overcome nonconvexity in optimal power flow problems through semidefinite relaxation and how to design ubiquitous load-side frequency regulation.
Bio: Steven Low is a Professor of the Department of Electrical Engineering and the Department of Computing & Mathematical Sciences at Caltech. Before that, he was with AT&T Bell Laboratories, Murray Hill, NJ, and the University of Melbourne, Australia. He was on the Technical Advisory Board of Southern California Edison and a member of the Networking and Information Technology Technical Advisory Group for the US President’s Council of Advisors on Science and Technology (PCAST) in 2006. He is a Senior Editor of the IEEE Transactions on Control of Network Systems and the IEEE Transactions on Network Science & Engineering, is on the editorial boards of NOW Foundations and Trends in Electric Energy Systems, and in Networking. He is an IEEE Fellow and received his B.S. from Cornell and PhD from Berkeley.
Thursday, Nov. 19, 2015
3:00 p.m., room SF1105
Title: Silicon Photonics: The Optical Spice Rack
Abstract: Silicon is evolving as a versatile photonic platform with multiple functionalities that can be seamlessly integrated. The tool box is rich starting from the ability to guide and amplify multiple wavelength sources at GHz bandwidths, to optomechanical MEMS and opto-fluidics devices. As an example of novel device capabilities, I will discuss the generation of strong optical forces in these ultra small light confining structures. We have recently shown that optical forces can enable controllable, static manipulation of photonic structures, an important step towards enabling recently proposed functionalities for optomechanical devices, such as self-aligning and optical corralling behaviour. These advances should enable future micro-optomechanical systems (MOMS) with novel and distinct functionalities.
Bio: Michal Lipson joined the faculty of Electrical Engineering Department as the Higgins Professor in July 2015. Prof. Michal Lipson completed her B.S., MS and Ph.D. degrees in Physics in the Technion in 1998, and then took a Postdoctoral position at MIT in the Material Science department. Prior to joining Columbia, Lipson was a member the faculty of Cornell University since 2001, and was named the Given Foundation Professor of Engineering at the School of Electrical and Computer Engineering in 2013. Lipson is one of the pioneers of the field of Silicon Photonics, which today is recognized as the path for solving several of the major bottlenecks in microelectronics. She holds over 20 patents , is the author of over 200 technical papers and since 2001 has graduated over 25 Ph.D. students. She is a co-founder of PicoLuz a company specializing in nonlinear silicon photonic components. Professor Lipson’s honors and awards include Macarthur Fellow, Blavatnik Award, IBM Faculty Award, and NSF Early Career Award. She is a Fellow of the OSA and of IEEE. She was recently named by Thomson Reuters as a top 1% highly cited researcher in the field of Physics.
Thursday, January 28, 2016
3:00 p.m., room SF1105
Title: Imaging Tissue and Treating Cancer with Microwaves
Abstract: The endogenous (and possibly exogenously influenced) dielectric properties of tissue at microwave frequencies vary across different tissue types and physiological states. These properties may be exploited to differentiate tissues via low-power microwave imaging and to selectively heat diseased tissue at higher power levels. This presentation will highlight recent theoretical and experimental advances in low-cost microwave theranostics – that is, diagnostic and therapeutic microwave-based technologies – with an emphasis on breast imaging and targeted cancer treatment. On the diagnostic side, 3-D quantitative microwave imaging technology has the potential to address several important clinical needs in breast imaging, including evaluating breast density as part of a patient’s individualized risk assessment, screening women who are at higher risk for cancer, and monitoring changes in breast tissue in response to prevention and treatment protocols. On the therapeutic side, minimally invasive microwave ablation using miniaturized antennas as interstitial heating probes is emerging as a less invasive alternative to surgical resection and more effective and versatile alternative to conventional thermoablative techniques for the treatment of primary tumors.
Bio: Susan C. Hagness received the B.S. degree with highest honors and the Ph.D. degree in electrical engineering from Northwestern University in 1993 and 1998, respectively. Since 1998, she has been with the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison, where she currently holds the title of Philip D. Reed Professor and serves as the Associate Dean for Research and Graduate Affairs in the College of Engineering. She is also a Faculty Affiliate of the Department of Biomedical Engineering and a member of the UW Carbone Comprehensive Cancer Center. Dr. Hagness was the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) presented by the U.S. White House in 2000. In 2002, she was named one of the 100 top young innovators in science and engineering in the world by the MIT Technology Review magazine. She is also the recipient of the UW-Madison Emil Steiger Distinguished Teaching Award (2003), the IEEE Engineering in Medicine and Biology Society Early Career Achievement Award (2004), the URSI Isaac Koga Gold Medal (2005), the IEEE Transactions on Biomedical Engineering Outstanding Paper Award (2007), the IEEE Education Society Mac E. Van Valkenburg Early Career Teaching Award (2007), the UW System Alliant Energy Underkofler Excellence in Teaching Award (2009), the Physics in Medicine and Biology Citations Prize (2011), the UW-Madison Kellett Mid- Career Award (2011), and the UW-Madison College of Engineering Benjamin Smith Reynolds Award for Excellence in Teaching Engineers (2014). She was elected Fellow of the IEEE in 2009. She has held numerous leadership positions within the IEEE Antennas and Propagation Society (AP-S) and the United States National Committee (USNC) of the International Union of Radio Science (URSI). She was the Technical Program Chair of the 2012 IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting in Chicago, IL, and most recently completed a term as Chair of the IEEE AP-S Fellows Evaluation Committee.
Thursday, Feb. 25, 2016
3:00 p.m., room SF1105
Title: Mixed-Signal Circuits for the Data-Driven World
Abstract: Our insatiable demand for sensing, communication and computing has been a key driver in the relentless scaling of CMOS feature sizes. However, as the benefits of “business as usual” scaling are coming to an end, an increasing amount of out of the box thinking will be required to maintain the progress slopes that we have become accustomed to. In this spirit, my talk will focus on a number of opportunities in the design of mixed-signal circuits for next-generation systems. Specific examples include: Analog equalization for high-speed links, always-on image sensing, analog-to-information conversion for ultrasound imaging and nonlinear system ID, as well as mixed-signal accelerators for approximate computing applications.
Bio: Boris Murmann joined Stanford University in 2004, where he currently serves as an Associate Professor of Electrical Engineering. He received the Ph.D. degree in electrical engineering from the University of California at Berkeley in 2003. From 1994 to 1997, he was with Neutron Microelectronics, Germany, where he developed low-power and smart-power ASICs in automotive CMOS technology. Dr. Murmann’s research interests are in the area of mixed-signal integrated circuit design, with special emphasis on data converters and sensor interfaces. In 2008, he was a co-recipient of the Best Student Paper Award at the VLSI Circuits Symposium in 2008 and a recipient of the Best Invited Paper Award at the IEEE Custom Integrated Circuits Conference (CICC). He received the Agilent Early Career Professor Award in 2009 and the Friedrich Wilhelm Bessel Research Award in 2012. He has served as an Associate Editor of the IEEE Journal of Solid-State Circuits and as the Data Converter Subcommittee Chair of the IEEE International Solid-State Circuits Conference (ISSCC). He currently serves as the program vice-chair for the ISSCC 2016. He is a Fellow of the IEEE.
Thursday, March 17, 2016
3:00 p.m., room SF1105
Title: Closing the Loop: Nerves, Machines and Interfaces
Abstract: The integration of technology with biology makes us more productive in the workplace, makes medical devices more effective, and makes our entertainment systems more engaging. Real-time communication between a nervous system and a device is now possible, but full and reliable integration in biohybrid systems is still far from reality. Biohybrid systems of the future are likely to utilize biomimetic machines with multi-channel, high throughput interfaces not only to integrate with the biological system, but also to close the loop in a manner that promotes adaptation in the nervous system. This talk will present some of our work that uses tools and techniques from computational neuroscience and neural engineering to develop biohybrid systems. In an ongoing study, a neuromorphic adaptive control system to provide ventilatory assistance after spinal cord injury is being developed. In other work, we are developing and deploying neural-enabled prosthetic systems to provide amputees with technology that is highly functional and easy to use. These efforts are directed at developing effective and reliable peripheral neural interfaces that can record motor intent of upper limb amputees and use a fully implantable electrical stimulation system to restore the sense of touch and proprioception thereby closing the loop for seamless integration.
Bio: Ranu Jung holds the Wallace H. Coulter Eminent Scholars Chair in Biomedical Engineering at Florida International University (FIU) where she is Interim Dean of the College of Engineering and Computing and Professor of Biomedical Engineering. She is also ex-officio member of the Council of Chairs of the Herbert Wertheim College of Medicine, representative on the Board of Directors of the FIU Research Foundation Inc., and Chair of the Research Advisory Committee, FIU Office of Research and Economic Development. Previously she held faculty appointments at University of Kentucky and at Arizona State University where she was founding co-director of the Center for Adaptive Neural Systems- a multidisciplinary Arizona Board of Regents recognized research center. Jung received her B.Tech with Distinction in Electronics & Communication Engineering from National Institute of Technology, Warangal, India and her MS and PhD degrees in Biomedical Engineering from Case Western Reserve University. She was a N.E. Ohio American Heart Association Research Fellow and a National Institutes of Health National Research Service Award recipient. She is also a Senior Member of IEEE and the Society of Women Engineers and past-President of the international “Organization for Computational Neurosciences, Inc.”
Friday, April 22, 2016
10:00 a.m., room SF1105
Title: Programming the World of Uncertain Things
implement or deeply understand statistics. We demonstrate substantial programmability, correctness, and efficiency benefits of Uncertain for GPS sensor navigation, approximate computing, machine learning, and xBox.
Bio: Kathryn S. McKinley is a Principal Researcher at Microsoft. She was previously an Endowed Professor of Computer Science at The University of Texas at Austin. She received her BA, MS, and PhD from Rice University. Her research interests span programming languages, compilers, runtime systems, architecture, performance, and energy with a recent focus on programming models for estimates. She and her collaborators have produced several widely used tools: DaCapo Java Benchmarks, TRIPS Compiler, Hoard memory manager, MMTk memory management toolkit, and Immix garbage collector. She has graduated 21 PhD students. Her awards include the ACM SIGPLAN Programming Languages Software Award; ACM SIGPLAN Distinguished Service Award; and Best & Test of time awards from ASPLOS, OOPSLA, ICS, SIGMETRICS, IEEE Top Picks, SIGPLAN Research Highlights, and CACM Research Highlights. She served as program chair for ASPLOS, PACT, PLDI, ISMM, and CGO. She is currently a CRA and CRA-W Board member. Dr. McKinley was honored to testify to the House Science Committee (Feb. 14, 2013). She and her husband have three sons. She is an IEEE and ACM Fellow.