September 17, 2020
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
Title: Rethinking principles of modeling, simulations and control for the changing electric energy systems
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
Title: Energy Technology Innovation to Rebuild a More Sustainable and Circular Economy
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
Title: Harnessing signals within a low-D manifold in the million-dimensional brain to restore voluntary movement following spinal cord injury
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.
March 4, 2021
Title: Understanding the Role of Network Structure in Controlling Complex Networks
Abstract: Controllability of complex network systems is an active area of research at the intersection of network science, control theory, and multi-agent coordination, with multiple applications ranging from brain dynamics to the smart grid and cyber-physical systems. The basic question is to understand to what extent the dynamic behavior of the entire network can be shaped by changing the states of some of its subsystems, and decipher the role that network structure plays in achieving this. This talk examines this question in two specific instances: characterizing network controllability when control nodes can be scheduled over a time horizon and hierarchical selective recruitment in brain networks. Regarding controllability, we show how time-varying control schedules can significantly enhance network controllability over fixed ones, especially when applied to large networks. Through the analysis of a novel scale-dependent notion of nodal centrality, we show that optimal time-varying scheduling involves the actuation of the most central nodes at appropriate spatial scales. Regarding hierarchical selective recruitment, we examine network mechanisms for selective inhibition and top-down recruitment of subnetworks under linear-threshold dynamics. Motivated by the study of goal-driven selective attention in neuroscience, we build on the characterization of key network dynamical properties to enable, through either feedforward or feedback control, the targeted inhibition of task-irrelevant subnetworks and the top-down recruitment of task-relevant ones.
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
Title: How flexible are Integrated Circuits?
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
March 18, 2021
Title: Exotic Wave-Matter Interactions in Metamaterials Based on Broken Symmetries
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.