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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181015T160000
DTEND;TZID=America/New_York:20181015T170000
DTSTAMP:20260524T195750
CREATED:20180810T155930Z
LAST-MODIFIED:20190501T143758Z
UID:8162-1539619200-1539622800@idss-stage.mit.edu
SUMMARY:Augmented Lagrangians and Decomposition in Convex and Nonconvex Programming
DESCRIPTION:Multiplier methods based on augmented Lagrangians are attractive in convex and nonconvex programming for their stabilizing and even convexifying properties. They have widely been seen\, however\, as incompatible with taking advantage of a block-separable structure. \nIn fact\, when articulated in the right way\, they can produce decomposition algorithms in which low-dimensional subproblems can be solved in parallel. Convergence in the nonconvex case is\, of course\, just local\, but is available under a broad analog of the strong second-order sufficient condition for local optimality that dominates much of computational methodology outside of convex optimization. This carries over also to extended nonlinear programming with its greater flexibility to handle composite terms. \nBio: Ralph Tyrrell (Terry) Rockafellar has long been associated with the University of Washington\, Seattle\, where he is Professor Emeritus of Mathematics\, but has also contributed in recent years as Adjunct Research Professor of Systems and Industrial Engineering at the University of Florida\, Gainesville\, and as Honorary Professor of the Department of Applied Mathematics at Hong Kong Polytechnic University. \nHis interests span from convex and variational analysis to problems of optimization and equilibrium\, especially nowadays applications in finance\, engineering\, and economics involving risk and reliability\, along with schemes of problem decomposition on convex and nonconvex programming. \nIn addition to being a winner of the Dantzig Prize given jointly by SIAM and the Mathematical Programming Society (1983)\, Prof. Rockafellar has gained international recognition for his work through honorary doctorates bestowed by universities in a number of countries. INFORMS awarded him and Roger Wets the 1997 Lancaster Prize for their book Variational Analysis\, and in 1999 he was further honored by INFORMS with John von Neumann Theory Prize for his fundamental contributions to the methodology of optimization. He has authored over 240 publications\, including one of the all-time most highly cited books in mathematics\, Convex Analysis. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-series-2
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180924T160000
DTEND;TZID=America/New_York:20180924T170000
DTSTAMP:20260524T195750
CREATED:20180810T155456Z
LAST-MODIFIED:20190501T143856Z
UID:8160-1537804800-1537808400@idss-stage.mit.edu
SUMMARY:The Power of Multiple Samples in Generative Adversarial Networks
DESCRIPTION:We bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953\, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs\, where a trained neural network (called a critic) determines whether a given sample is from the real data or the generated (fake) data. By jointly training the generator and the critic\, the hope is that eventually the trained generator will generate realistic samples. One of the major challenges in GAN is known as “mode collapse”; the lack of diversity in the samples generated by thus trained generators. We propose a new training framework\, where the critic is fed with multiple samples jointly (which we call packing)\, as opposed to each sample separately as done in standard GAN training. With this simple but fundamental departure from standard GANs\, experimental results show that the diversity of the generated samples improve significantly. We analyze this practical gain by first providing a formal mathematical definition of mode collapse and making a fundamental connection between the idea of packing and the intensity of mode collapse. Precisely\, we show that the packed critic naturally penalizes mode collapse\, thus encouraging generators with less mode collapse. The analyses critically rely on operational interpretation of hypothesis testing and corresponding data processing inequalities\, which lead to sharp analyses with simple proofs. For this talk\, I will assume no prior background on GANs. \nThis is joint work with Zinan Lin (CMU)\, Ahsish Khetan (Amazon AI)\, and Giulia Fanti (CMU). \nBio: Sewoong Oh is an Associate Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD\, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. His research interest is in theoretical machine learning\, including spectral methods\, ranking\, crowdsourcing\, estimation of information measures\, differential privacy\, and generative adversarial networks. He was co-awarded the best paper award at the SIGMETRICS in 2015\, NSF CAREER award in 2016 and GOOGLE Faculty Research Award. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-series-1
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180917T160000
DTEND;TZID=America/New_York:20180917T170000
DTSTAMP:20260524T195750
CREATED:20180810T155258Z
LAST-MODIFIED:20190501T143950Z
UID:8158-1537200000-1537203600@idss-stage.mit.edu
SUMMARY:Regret of Queueing Bandits
DESCRIPTION:We consider a variant of the multiarmed bandit (MAB) problem where jobs or tasks queue for service\, and service rates of different servers (agents) may be unknown. Such (queueing+learning) problems are motivated by a vast range of service systems\, including supply and demand in online platforms (e.g.\, Uber\, Lyft\, Airbnb\, Upwork\, etc.)\, order flow in financial markets (e.g.\, limit order books)\, communication systems\, and supply chains. \nWe study algorithms that minimize queue-regret: the expected difference between the queue-lengths (backlogs) obtained by the algorithm\, and those obtained by a genie-aided matching algorithm that knows exact service rates. A naive view of this problem would suggest that queue-regret could grow logarithmically: since queue-regret cannot be larger than classical regret\, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time. Our work shows surprisingly more complex behavior — specifically\, the optimal queue-regret decreases with time and scales as O(1/t). We next consider holding-cost regret in multi-class (multiple types of tasks) multi-server (servers/agents have task-type dependent service rate) systems. Holding costs correspond to a system where a linear cost (with respect to time spent in the queue) is incurred for each incomplete task. We consider learning-based variants of the c-mu rule – a classic and well-studied scheduling policy that is used when server/agent service rates are known. We develop algorithms that result in constant expected holding-cost regret (independent of time). The key insight that allows such a regret bound is that service systems we consider exhibit explore-free learning\, where no penalty is (eventually) incurred for exploring and learning server/agent rates. We finally discuss the implications of our results on building platforms for matching tasks to servers/agents. Base on joint work with Subhashini Krishnasamy\, Rajat Sen\, Ari Arapostathis and Ramesh Johari. \nBio: Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. He is with The University of Texas at Austin\, where he is currently the Ashley H. Priddy Centennial Professor in Engineering\, the Director of the Wireless Networking and Communications Group (WNCG)\, and a Professor in the Department of Electrical and Computer Engineering. He received the NSF CAREER award in 2004 and was elected as an IEEE Fellow in 2014. His research interests lie at the intersection of algorithms for resource allocation\, statistical learning and networks\, with applications to wireless communication networks and online platforms. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-series-0
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180910T160000
DTEND;TZID=America/New_York:20180910T170000
DTSTAMP:20260524T195750
CREATED:20180905T161016Z
LAST-MODIFIED:20190501T144029Z
UID:8230-1536595200-1536598800@idss-stage.mit.edu
SUMMARY:Streaming Analytics for the Smart Grid
DESCRIPTION:How to conduct real-time analytics of streaming measurement data in the power grid? This talk offers a dynamic systems approach to utilizing data of different time scale for improved monitoring of the grid cyber and physical security. The first example of the talk presents how to leverage synchrophasor data dimensionality reduction and Robust Principal Component Analysis for early anomaly detection\, visualization\, and localization. The second example presents an online framework to detect cyber-attacks on automatic generation control (AGC). A cyber-attack detection algorithm is designed based on the approach of Dynamic Watermarking. The detection algorithm provides a theoretical guarantee of detection of cyber-attacks launched by sophisticated attackers possessing extensive knowledge of the physical and statistical models of targeted power systems. The underlying theme of the work suggests the importance of integrating data with dynamic context-aware models in the smart grid. \nBio: Dr. Le Xie is a Professor and Eugene Webb Faculty Fellow in the Department of Electrical and Computer Engineering at Texas A&M University. He received B.E. in Electrical Engineering from Tsinghua University\, S.M. in Engineering Sciences from Harvard\, and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon in 2009. His industry experience includes ISO-New England and Edison Mission Energy Marketing and Trading. His research interest includes modeling and control in data-rich large-scale systems\, grid integration of clean energy resources\, and electricity markets. \nDr. Xie received the U.S. National Science Foundation CAREER Award\, and DOE Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award. He was awarded the 2017 IEEE PES Outstanding Young Engineer Award. He was the recipient of Texas A&M Dean of Engineering Excellence Award\, ECE Outstanding Professor Award\, and TEES Select Young Fellow. He is an Editor of IEEE Transactions on Smart Grid\, and the founding chair of IEEE PES Subcommittee on Big Data & Analytics for Grid Operations. He and his students received the Best Paper awards at North American Power Symposium and IEEE SmartGridComm. He recently chaired the 2018 NSF Workshop on Real-time Learning and Decision Making in Dynamical Systems. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/streaming-analytics-smart-grid
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180515T150000
DTEND;TZID=America/New_York:20180515T160000
DTSTAMP:20260524T195750
CREATED:20180223T173133Z
LAST-MODIFIED:20180515T135702Z
UID:7445-1526396400-1526400000@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series - Vivek Borkar
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/https-lids-mit-edu-news-and-events-events-distributed-algorithms-tsitsiklis-and-beyond/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180508T150000
DTEND;TZID=America/New_York:20180508T160000
DTSTAMP:20260524T195750
CREATED:20180223T173042Z
LAST-MODIFIED:20180223T173042Z
UID:7443-1525791600-1525795200@idss-stage.mit.edu
SUMMARY:A Rationally Designed Biomolecular Integral Feedback Control System for Robust Gene Regulation
DESCRIPTION:Abstract \nHumans have been influencing the DNA of plants and animals for thousands of years through selective breeding. Yet it is only over the last 3 decades or so that we have gained the ability to manipulate the DNA itself and directly alter its sequences through the modern tools of genetic engineering. This has revolutionized biotechnology and ushered in the era of synthetic biology. Among the possible applications enabled by synthetic biology is the design and engineering of feedback control systems that act at the molecular scale in real-time to steer the dynamic behavior of living cells. Here I will present our theoretical framework for the design and synthesis of such control systems\, and will discuss the main challenges in their practical implementation. I will then present the first designer gene network that attains integral feedback in a living cell and demonstrate its tenability and disturbance rejection properties. A growth control application shows the inherent capacity of this integral feedback control system to deliver robustness\, and highlights its potential use as a universal controller for regulation of biological variables in arbitrary networks. Finally\, I will discuss the potential impact of biomolecular control systems in industrial biotechnology and medical therapy and bring attention to the opportunities that exist for control theorists to advance this young area of research. \nBiography \nMustafa Khammash is the Professor of Control Theory and Systems Biology at the Department of Biosystems Science and Engineering at ETH Zurich\, Switzerland. He works in the areas of control theory\, systems biology\, and synthetic biology. His lab develops theoretical\, computational\, and experimental methods aimed at understanding the role of dynamics\, feedback\, and randomness in biology. He is currently developing new theoretical and experimental approaches for the design of biomolecular control systems and for their realization in living cells. \nProf. Khammash received his B.S. degree from Texas A&M University in 1986 and his Ph.D. from Rice University in 1990\, both in electrical engineering. In 1990\, he joined the engineering faculty of Iowa State University\, where he created the Dynamics and Control Program and led the control group until 2002. He then joined the engineering faculty at the University of California\, Santa Barbara (UCSB)\, where he was Director of the Center for Control\, Dynamical Systems and Computation (CCDC) until 2011 when he joined ETH Zurich. He is a Fellow of the IEEE\, IFAC\, and the Japan Society for the Promotion of Science (JSPS).
URL:https://idss-stage.mit.edu/calendar/a-rationally-designed-biomolecular-integral-feedback-control-system-for-robust-gene-regulation/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180424T150000
DTEND;TZID=America/New_York:20180424T160000
DTSTAMP:20260524T195750
CREATED:20180223T172912Z
LAST-MODIFIED:20180419T194543Z
UID:7441-1524582000-1524585600@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series: Jose M. F. Moura
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-jose-m-f-moura/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180418T140000
DTEND;TZID=America/New_York:20180418T150000
DTSTAMP:20260524T195750
CREATED:20180223T172740Z
LAST-MODIFIED:20180223T172740Z
UID:7439-1524060000-1524063600@idss-stage.mit.edu
SUMMARY:Community-based and Peer-to-peer Electricity Markets
DESCRIPTION:Abstract \nThe deployment of distributed renewable generation capacities\, new ICT capabilities\, as well as a more proactive role of consumers\, are all motivating rethinking electricity markets in a more distributed and consumer-centric fashion. After motivating the design of various forms of consumer-centric electricity markets\, we will focus on two alternative constructs (which could actually be unified) consisting in community-based and peer-to-peer electricity markets. The mathematical framework for these markets will be described\, with focus on negotiation and clearing algorithms in a distributed and decentralized setup. Opportunities and challenges related to these markets\, both mathematical and related to real-world applications\, will be discussed. Especially\, we will look at fairness aspects\, product differentiation\, as well as the design of network charges to account for ‘actual’ usage of a network. \nBiography \nPierre Pinson is a Professor at the Centre for Electric Power and Energy (CEE) of the Technical University of Denmark (DTU\, Dept. of Electrical Engineering)\, also heading a group focusing on Energy Analytics & Markets. He holds an M.Sc. In Applied Mathematics from INSA Toulouse and a Ph.D. In Energy Engineering from Ecole de Mines de Paris (France). He acts (or has acted) as an Editor for the IEEE Transactions on Power Systems\, the International Journal of Forecasting and Wind Energy. His main research interests are centered around the proposal and application of mathematical methods for electricity markets and power systems operations\, including forecasting. He has published extensively in some of the leading journals in Meteorology\, Power Systems Engineering\, Statistics and Operations Research. He has been a visiting researcher at the University of Oxford (Mathematical Institute) and the University of Washington in Seattle (Dpt. of Statistics)\, as well as a scientist at the European Center for Medium-range Weather Forecasts (ECMWF\, UK) and a visiting professor at Ecole Normale Superieure (Rennes\, France). In 2019 he will be a Simons Fellow at the University of Cambridge\, Isaac Newton Institute (“The mathematics of energy systems”). He is leading a number of initiatives aiming to profundly rethink electricity markets for future renewable-based power systems and with a more proactive role of consumers. This focus on consumer-centric and community-driven electricity markets translates into proposals for peer-to-peer energy exchange\, from mathematical framework to actual demonstration in Denmark.
URL:https://idss-stage.mit.edu/calendar/community-based-and-peer-to-peer-electricity-markets/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180410T150000
DTEND;TZID=America/New_York:20180410T160000
DTSTAMP:20260524T195750
CREATED:20180223T172617Z
LAST-MODIFIED:20180223T172617Z
UID:7437-1523372400-1523376000@idss-stage.mit.edu
SUMMARY:Finding Online Extremists in Social Networks
DESCRIPTION:Abstract \nOnline extremists in social networks pose a new form of threat to the general public. These extremists range from cyber bullies who harass innocent users to terrorist organizations such as ISIS that use social networks to spread propaganda. Currently\, social networks suspend the accounts of such extremists in response to user complaints\, but these extremist users simply create new accounts and continue their activities. In this talk\, we present a new set of operational capabilities to help authorities mitigate the threat posed by online extremist groups in social networks. \nUsing data from several hundred thousand extremist accounts on Twitter\, we develop a behavioral model for these users\, in particular\, what their accounts look like and who they connect with. This model is used to identify new extremist accounts by predicting if they will be suspended for extremist activity. We also use this model to track existing extremist users as they create new accounts by identifying if two accounts belong to the same user. Finally\, we use this model as the basis for an efficient policy to search the social network for suspended users’ new accounts. Our search approach is based on a variant of the classic Polya’s urn setup. We find a simple characterization of the optimal search policy for this model under fairly general conditions. Our search policy and main theoretical results generalize easily to search problems in other fields. \nJoint work with Jytte Klausen and Christopher Marks. \nBiography \nTauhid is an Assistant Professor of Operations Management at the MIT Sloan School of Management. He received his BS\, MEng\, and Ph.D. degrees in electrical engineering and computer science from MIT. His research focuses on solving operational problems involving social network data using probabilistic models\, network algorithms\, and modern statistical methods. Some of the topics he studies in the social networks space include predicting the popularity of content\, finding online extremists\, and geo-locating users. His broader interests cover data-driven approaches to investing in startup companies\, non-traditional choice modeling\, algorithmic sports betting\, and biometric data. His work has been featured in the Wallstreet Journal\, Wired\, Mashable\, the LA Times\, and Time Magazine.
URL:https://idss-stage.mit.edu/calendar/finding-online-extremists-in-social-networks/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180320T150000
DTEND;TZID=America/New_York:20180320T160000
DTSTAMP:20260524T195750
CREATED:20180223T172446Z
LAST-MODIFIED:20180223T172446Z
UID:7435-1521558000-1521561600@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series - Lizhong Zheng
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-lizhong-zheng/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180313T150000
DTEND;TZID=America/New_York:20180313T160000
DTSTAMP:20260524T195750
CREATED:20180223T172349Z
LAST-MODIFIED:20180223T172349Z
UID:7433-1520953200-1520956800@idss-stage.mit.edu
SUMMARY:The Power of Multiple Samples in Generative Adversarial Networks
DESCRIPTION:Abstract \nWe bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953\, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs\, where a trained neural network (called a critic) determines whether a given sample is from the real data or the generated (fake) data. By jointly training the generator and the critic\, the hope is that eventually\, the trained generator will generate realistic samples. One of the major challenges in GAN is known as “mode collapse”; the lack of diversity in the samples generated by thus trained generators. We propose a new training framework\, where the critic is fed with multiple samples jointly (which we call packing)\, as opposed to each sample separately as done in standard GAN training. With this simple but fundamental departure from existing GANs\, experimental results show that the diversity of the generated samples improve significantly. We analyze this practical gain by first providing a formal mathematical definition of mode collapse and making a fundamental connection between the idea of packing and the intensity of mode collapse. Precisely\, we show that the packed critic naturally penalizes mode collapse\, thus encouraging generators with less mode collapse. The analyses critically rely on operational interpretation of hypothesis testing and corresponding data processing inequalities\, which lead to sharp analyses with simple proofs. For this talk\, Prof. Sewoong Oh will assume no prior background on GANs. \nBiography \nSewoong Oh is an Assistant Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his Ph.D. from the Department of Electrical Engineering at Stanford University. Following his Ph.D.\, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. His research interest is in theoretical machine learning\, including spectral methods\, ranking\, crowdsourcing\, estimation of information measures\, differential privacy\, and generative adversarial networks. He was co-awarded the best paper award at the SIGMETRICS in 2015\, NSF CAREER award in 2016 and GOOGLE Faculty Research Award.
URL:https://idss-stage.mit.edu/calendar/the-power-of-multiple-samples-in-generative-adversarial-networks/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180227T150000
DTEND;TZID=America/New_York:20180227T160000
DTSTAMP:20260524T195750
CREATED:20180223T172101Z
LAST-MODIFIED:20180223T172101Z
UID:7430-1519743600-1519747200@idss-stage.mit.edu
SUMMARY:Safe Learning in Robotics
DESCRIPTION:Abstract \nA great deal of research in recent years has focused on robot learning. In many applications\, guarantees that specifications are satisfied throughout the learning process are paramount. For the safety specification\, we present a controller synthesis technique based on the computation of reachable sets using optimal control. We show recent results in system decomposition to speed up this computation\, and how offline computation may be used in online applications. We then present a method combining reachability with machine learning\, which uses approximate knowledge of the dynamics to provide a least-restrictive\, safety-preserving control law which intervenes only when the computed safety guarantees require it\, or when confidence in the computed guarantee decays in light of new observations. We will illustrate these methods on a quadrotor UAV experimental platform which we have at Berkeley. \nBiography \nClaire Tomlin is the Charles A. Desoer Professor of Engineering in EECS at Berkeley. She was an Assistant\, Associate\, and Full Professor in Aeronautics and Astronautics at Stanford from 1998 to 2007\, and in 2005 joined Berkeley. Claire works in the area of control theory and hybrid systems\, with applications to air traffic management\, UAV systems\, energy\, robotics\, and systems biology. She is a MacArthur Foundation Fellow (2006)\, an IEEE Fellow (2010)\, and in 2017 was awarded the IEEE Transportation Technologies Award.​​
URL:https://idss-stage.mit.edu/calendar/safe-learning-in-robotics/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180220T150000
DTEND;TZID=America/New_York:20180220T160000
DTSTAMP:20260524T195750
CREATED:20180223T171917Z
LAST-MODIFIED:20180223T171917Z
UID:7427-1519138800-1519142400@idss-stage.mit.edu
SUMMARY:Submodular Optimization: From Discrete to Continuous and Back
DESCRIPTION:Abstract \nMany procedures in statistics and artificial intelligence require solving non-convex problems. Historically\, the focus has been to convexify the non-convex objectives. In recent years\, however\, there has been significant progress to optimize non-convex functions directly. This direct approach has led to provably good guarantees for specific problem instances such as latent variable models\, non-negative matrix factorization\, robust PCA\, matrix completion\, etc. Unfortunately\, there is no free lunch and it is well known that in general finding the global optimum of a non-convex optimization problem is NP-hard. This computational barrier has mainly shifted the goal of non-convex optimization towards two directions: a) finding an approximate local minimum by avoiding saddle points or b) characterizing general conditions under which the underlying non-convex optimization is tractable. \nIn this talk\, I will consider a broad class of non-convex optimization problems that possess special combinatorial structures. More specifically\, I will focus on maximization of stochastic continuous submodular functions that demonstrate diminishing returns. Despite the apparent lack of convexity in such functions\, we will see that first order methods can indeed provide strong approximation guarantees. In particular\, for monotone and continuous submodular functions\, we will show that projected stochastic gradient methods achieve a ½ approximation ratio. We then see how we can reach the tight (1-1/e) approximation guarantee by developing a new class of stochastic projection-free gradient methods. A simple variant of these algorithms also achieves a (1/e) approximation ratio in the non-monotone case. Finally\, by using stochastic continuous optimization as an interface\, we will also provide tight approximation guarantees for maximizing a (monotone or non-monotone) stochastic submodular set function subject to a general matroid constraint. \nIn this talk\, I will not assume any particular background on submodularity or optimization and will try to motivate and define all the necessary concepts. \nBiography \nAmin Karbasi is an assistant professor in the School of Engineering and Applied Science (SEAS) at Yale University\, where he leads the Inference\, Information\, and Decision (I.I.D.) Systems Group. Prior to that he was a post-doctoral scholar at ETH Zurich\, Switzerland (2013-2014). He obtained his Ph.D. (2012) and M.Sc. (2007) in computer and communication sciences from EPFL\, Switzerland and his B.Sc. (2004) in electrical engineering from the same university.
URL:https://idss-stage.mit.edu/calendar/submodular-optimization-from-discrete-to-continuous-and-back/
LOCATION:34-101
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180213T150000
DTEND;TZID=America/New_York:20180213T160000
DTSTAMP:20260524T195750
CREATED:20180223T171528Z
LAST-MODIFIED:20180223T171528Z
UID:7424-1518534000-1518537600@idss-stage.mit.edu
SUMMARY:Supervisory Control of Discrete Event Systems: A Retrospective and Two Recent Results on Security and Privacy
DESCRIPTION:Abstract \nLafortune will begin with a brief retrospective of the theory of supervisory control of discrete event systems\, initiated in the seminal work of Ramadge & Wonham over 30 years ago\, and compare it with recent work in formal methods in control. He will then present results from his group on two problems: (i) sensor deception attacks in the supervisory control layer of a cyber-physical system; and (ii) obfuscation of system secrets by insertion of fictitious events in the output stream of the system. In each case\, he will describe the group’s solution procedure\, which is based on synthesizing a discrete game structure that embeds all valid solutions. \nBiography \nStéphane Lafortune is a professor in the Department of Electrical Engineering and Computer Science at the University of Michigan\, Ann Arbor\, USA. He obtained his degrees from École Polytechnique de Montréal (B.Eng)\, McGill University (M.Eng)\, and the University of California at Berkeley (PhD)\, all in electrical engineering. He is a Fellow of IEEE (1999) and of IFAC (2017). \nLafortune’s research interests are in discrete event systems and include multiple problem domains: modeling\, diagnosis\, control\, optimization\, and applications to computer and software systems. He co-authored\, with C. Cassandras\, the textbook Introduction to Discrete Event Systems (2nd Edition\, Springer\, 2008). He has served as Editor-in-Chief of the journal Discrete Event Dynamic Systems: Theory and Applications since 2015.
URL:https://idss-stage.mit.edu/calendar/supervisory-control-of-discrete-event-systems-a-retrospective-and-two-recent-results-on-security-and-privacy/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171205T160000
DTEND;TZID=America/New_York:20171205T170000
DTSTAMP:20260524T195750
CREATED:20171002T160334Z
LAST-MODIFIED:20190501T144332Z
UID:6543-1512489600-1512493200@idss-stage.mit.edu
SUMMARY:Regularized Nonlinear Acceleration
DESCRIPTION:We describe a convergence acceleration technique for generic optimization problems. Our scheme computes estimates of the optimum from a nonlinear average of the iterates produced by any optimization method. The weights in this average are computed via a simple linear system\, whose solution can be updated online. This acceleration scheme runs in parallel to the base algorithm\, providing improved estimates of the solution on the fly\, while the original optimization method is running. Numerical experiments are detailed on classical classification problems. \nBio: After dual PhDs from Ecole Polytechnique and Stanford University in optimisation and finance\, followed by a postdoc at U.C. Berkeley\, Alexandre d’Aspremont joined the faculty at Princeton University as an assistant then associate professor with joint appointments at the ORFE department and the Bendheim Center for Finance. He returned to Europe in 2011 thanks to a grant from the European Research Council and is now a research director at CNRS\, attached to Ecole Normale Supérieure in Paris. His research focuses on convex optimization and applications to machine learning\, statistics and finance. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/regularized-nonlinear-acceleration
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171129T160000
DTEND;TZID=America/New_York:20171129T170000
DTSTAMP:20260524T195750
CREATED:20171002T155836Z
LAST-MODIFIED:20190501T144513Z
UID:6541-1511971200-1511974800@idss-stage.mit.edu
SUMMARY:Comparison Lemmas\, Non-Smooth Convex Optimization and Structured Signal Recovery
DESCRIPTION:In the past couple of decades\, non-smooth convex optimization has emerged as a powerful tool for the recovery of structured signals (sparse\, low rank\, finite constellation\, etc.) from possibly noisy measurements in a variety applications in statistics\, signal processing and machine learning. While the algorithms (basis pursuit\, LASSO\, etc.) are often fairly well established\, rigorous frameworks for the exact analysis of the performance of such methods are only just emerging. The talk will introduce and describe a fairly general theory for how to determine the performance (minimum number of measurements\, mean-square-error\, probability-of-error\, etc.) of such methods for various measurement ensembles (Gaussian\, Haar\, etc.). The framework enables one to assess the performance of these methods before actual implementation and allows one to optimally choose parameters such as regularizer coefficients\, number of measurements\, etc. The theory subsumes earlier results as special cases. It builds on an inconspicuous 1962 lemma of Slepian (for comparing Gaussian processes)\, as well as on a non-trivial generalization due to Gordon in 1988\, and produces concepts from convex geometry (such as Gaussian widths and Moreau envelopes) in a very natural way. The talk will also consider extensions to certain non-Gaussian settings and their applications in massive MIMO\, one-bit compressed sensing\, graphical LASSO and phase retrieval. \n\n\nBio: Babak Hassibi is the inaugural Mose and Lillian S. Bohn Professor of Electrical Engineering at the California Institute of Technology\, where he has been since 2001\, From 2011 to 2016 he was the Gordon M Binder/Amgen Professor of Electrical Engineering and during 2008-2015 he was Executive Officer of Electrical Engineering\, as well as Associate Director of Information Science and Technology. Prior to Caltech\, he was a Member of the Technical Staff in the Mathematical Sciences Research Center at Bell Laboratories\, Murray Hill\, NJ. He obtained his PhD degree from Stanford University in 1996 and his BS degree from the University of Tehran in 1989. His research interests span various aspects of information theory\, communications\, signal processing\, control and machine learning. He is an ISI highly cited author in Computer Science and\, among other awards\, is the recipient of the US Presidential Early Career Award for Scientists and Engineers (PECASE) and the David and Lucille Packard Fellowship in Science and Engineering \n\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/babak-hassibi-california-institute-technology
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171114T160000
DTEND;TZID=America/New_York:20171114T170000
DTSTAMP:20260524T195750
CREATED:20171002T155150Z
LAST-MODIFIED:20190501T144705Z
UID:6538-1510675200-1510678800@idss-stage.mit.edu
SUMMARY:Quantum Limits on the Information Carried by Electromagnetic Radiation
DESCRIPTION:In many practical applications information is conveyed by means of electromagnetic radiation and a natural question concerns the fundamental limits of this process. Identifying information with entropy\, one can ask about the maximum amount of entropy associated to the propagating wave. \nThe standard statistical physics approach to compute entropy is to take the logarithm of the number of possible energy states of a system. Since any continuum field can assume an uncountably infinite number of energy configurations\, the approach underlying any finite entropy calculation must also necessarily include some grouping of states together in a procedure known as coarse-graining or\, in information-theoretic parlance\, signal quantization. The problem then reduces to counting the eigenstates of the Hamiltonian of the quantum wave field. \nIn this talk\, we examine the relationship between entropy computations in a statistical physics and an information-theory context. In the latter context\, rather than attempting to directly count the number of energy eigenstates of the quantum wave field\, we constrain the geometry of the signal space and decompose the waveform into a minimum number of orthogonal basis modes. We then ask how many bits are required to represent any waveform in the space spanned by this optimal representation with a minimum quantized energy error. We show that for scalar quantization this entropy computation is completely analogous to the one for the number state channel of statistical physics\, and it has the attractive feature that the complexity of state counting is now replaced by the geometric problem of optimally covering the signal space by high-dimensional boxes\, whose size is lower bounded by quantum constraints. For bandlimited radiation in a three-dimensional space\, using this approach we can recover the Bekenstein entropy bound on the largest amount of information that can be radiated from a sphere of given radius. We also compare results with black body radiation occurring over an infinite spectrum of frequencies and along the way we provide some new results on the asymptotic dimensionality and $\epsilon$-entropy of bandlimited\, square-integrable signals. \n\n\nBio: Massimo Franceschetti received the Laurea degree (with highest honors) in computer engineering from the University of Naples\, Naples\, Italy\, in 1997\, the M.S. and Ph.D. degrees in electrical engineering from the California Institute of Technology\, Pasadena\, CA\, in 1999\, and 2003\, respectively. He is Professor of Electrical and Computer Engineering at the University of California at San Diego (UCSD). Before joining UCSD\, he was a postdoctoral scholar at the University of California at Berkeley for two years. His research interests are in physical and information-based foundations of communication and control systems. He was awarded the C. H. Wilts Prize in 2003 for best doctoral thesis in electrical engineering at Caltech\, the S.A. Schelkunoff Award in 2005 for best paper in the IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION\, a National Science Foundation (NSF) CAREER award in 2006\, an Office of Naval Research (ONR) Young Investigator Award in 2007\, the IEEE Communications Society Best Tutorial Paper Award in 2010\, and the IEEE Control theory society Ruberti young researcher award in 2012. \n\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/quantum-limits-information-carried-electromagnetic-radiation
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171031T160000
DTEND;TZID=America/New_York:20171031T170000
DTSTAMP:20260524T195750
CREATED:20171002T154828Z
LAST-MODIFIED:20190501T144833Z
UID:6535-1509465600-1509469200@idss-stage.mit.edu
SUMMARY:Structure\, Randomness and Universality
DESCRIPTION:What is the minimum possible number of vertices of a graph that contains every k-vertex graph as an induced subgraph? What is the minimum possible number of edges in a graph that contains every k-vertex graph with maximum degree 3 as a subgraph? These questions and related one were initiated by Rado in the 60s\, and received a considerable amount of attention over the years\, partly motivated by algorithmic applications. The study of the subject combines probabilistic arguments and explicit\, structured constructions. I will survey the topic focusing on a recent asymptotic solution of the first question\, where an asymptotic formula\, improving earlier estimates by several researchers\, is obtained by combining combinatorial and probabilistic arguments with group theoretic tools. \nBio: Noga Alon is a Baumritter Professor of Mathematics and Computer Science in Tel Aviv University\, Israel. He received his Ph. D. in Mathematics at the Hebrew University of Jerusalem in 1983 and had visiting positions in various research institutes including MIT\, the Institute for Advanced Study in Princeton\, IBM Almaden Research Center\, Bell Laboratories\, Bellcore and Microsoft Research. He joined Tel Aviv University in 1985\, served as the head of the School of Mathematical Sciences in 1999-2000\, and supervised about 20 PhD students. Since 2009 he is also a member of Microsoft Research\, Israel. He serves on the editorial boards of more than a dozen international technical journals and has given invited lectures in many conferences\, including plenary addresses in the 1996 European Congress of Mathematics and in the 2002 International Congress of Mathematician. He published more than five hundred research papers and one book. \n\n\nHis research interests are mainly in Combinatorics\, Graph Theory and their applications in Theoretical Computer Science. His main contributions include the study of expander graphs and their applications\, the investigation of derandomization techniques\, the foundation of streaming algorithms\, the development and applications of algebraic and probabilistic methods in Discrete Mathematics and the study of problems in Information Theory\, Combinatorial Geometry and Combinatorial Number Theory. \nHe is an ACM Fellow and an AMS Fellow\, a member of the Israel Academy of Sciences and Humanities since 1997 and of the Academia Europaea since 2008\, and received the Erdös prize in 1989\, the Feher prize in 1991\, the Polya Prize in 2000\, the Bruno Memorial Award in 2001\, the Landau Prize in 2005\, the Gödel Prize in 2005\, the Israel Prize in 2008\, the EMET Prize in 2011\, the Dijkstra Prize in 2016\, an Honorary Doctorate from ETH Zurich in 2013 and from the University of Waterloo in 2015. \n\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/joint-seminar-csail-theory-computation-toc
LOCATION:32-G449 (Kiva)\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171024T160000
DTEND;TZID=America/New_York:20171024T170000
DTSTAMP:20260524T195750
CREATED:20171002T154138Z
LAST-MODIFIED:20190501T145009Z
UID:6530-1508860800-1508864400@idss-stage.mit.edu
SUMMARY:Regularized Nonlinear Acceleration
DESCRIPTION:We describe a convergence acceleration technique for generic optimization problems. Our scheme computes estimates of the optimum from a nonlinear average of the iterates produced by any optimization method. The weights in this average are computed via a simple linear system\, whose solution can be updated online. This acceleration scheme runs in parallel to the base algorithm\, providing improved estimates of the solution on the fly\, while the original optimization method is running. Numerical experiments are detailed on classical classification problems. \nBio: After dual PhDs from Ecole Polytechnique and Stanford University in optimisation and finance\, followed by a postdoc at U.C. Berkeley\, Alexandre d’Aspremont joined the faculty at Princeton University as an assistant then associate professor with joint appointments at the ORFE department and the Bendheim Center for Finance. He returned to Europe in 2011 thanks to a grant from the European Research Council and is now a research director at CNRS\, attached to Ecole Normale Supérieure in Paris. His research focuses on convex optimization and applications to machine learning\, statistics and finance. \n\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/alexandre-tsybakov-ensae-paristech
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171017T160000
DTEND;TZID=America/New_York:20171017T170000
DTSTAMP:20260524T195750
CREATED:20171002T153935Z
LAST-MODIFIED:20190501T145140Z
UID:6528-1508256000-1508259600@idss-stage.mit.edu
SUMMARY:The Maps Inside Your Head
DESCRIPTION:How do our brains make sense of a complex and unpredictable world? In this talk\, I will discuss an information theory approach to the neural topography of information processing in the brain. First I will review the brain’s architecture\, and how neural circuits map out the sensory and cognitive worlds. Then I will describe how highly complex sensory and cognitive tasks are carried out by the cooperative action of many specialized neurons and circuits\, each of which has a simple function. I will illustrate my remarks with one sensory example and one cognitive example. For the sensory example\, I will consider the sense of smell (“olfaction”)\, whereby humans and other animals distinguish vast arrays of odor mixtures using very limited neural resources. For the cognitive example\, I will consider the “sense of place”\, that is\, how animals mentally represent their physical location. Both examples demonstrate that brains have evolved neural circuits that exploit sophisticated principles of mathematics and information processing – principles that scientists have only recently discovered. \nBio: Vijay Balasubramanian is the Cathy and Marc Lasry Professor in the Physics Department at the University of Pennsylvania\, where he is also Director of the Computational Neuroscience Initiative. He received B.Sc. degrees in Physics and Computer Science\, and an M.Sc. in Computer Science\, from MIT. He earned a Ph.D. in Theoretical Physics at Princeton University\, and was a Junior Fellow of the Harvard Society of Fellows. \n\n\n____________________________________ \n\nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/maps-inside-your-head
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20171012T143000
DTEND;TZID=UTC:20171012T153000
DTSTAMP:20260524T195750
CREATED:20170831T223408Z
LAST-MODIFIED:20171003T145827Z
UID:6064-1507818600-1507822200@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series: Stefano Soatto (UCLA)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-stefano-soatto-ucla/
LOCATION:34-401 (Grier Room)\, The Stata Center (34-401)\, 50 Vassar Street\, Cambridge\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170919T160000
DTEND;TZID=America/New_York:20170919T170000
DTSTAMP:20260524T195750
CREATED:20171002T153658Z
LAST-MODIFIED:20190501T145308Z
UID:6526-1505836800-1505840400@idss-stage.mit.edu
SUMMARY:Networking for Big Data: Theory and Optimization for NDN
DESCRIPTION:The advent of Big Data is stimulating the development of new networking architectures which facilitate the acquisition\, transmission\, storage\, and computation of data. In particular\, Named Data Networking (NDN) is an emerging content-centric networking architecture which focuses on enabling end users to obtain the data they want\, rather than to communicate with specific nodes. By naming content instead of their locations\, NDN transforms data into a first-class network entity. \nIn this talk\, we present a new analytical and design framework for the optimization of key network functionalities within the NDN architecture\, which is also broadly applicable to content delivery and peer-to-peer networks. The framework includes the joint optimization of traffic engineering and caching strategies\, in order to best utilize both bandwidth and storage for efficient content distribution. It also includes optimal congestion control when user demand for content becomes excessive. We first develop distributed and adaptive algorithms for joint request forwarding and dynamic cache placement and eviction\, which effectively achieve network load balancing\, thereby maximizing the user demand rate that the NDN network can satisfy. Next\, we develop content-based congestion control algorithms which naturally work in concert with forwarding and caching to achieve a favorable tradeoff between the aggregate user utility from admitted content requests and the total user delay. Numerical experiments within a number of network settings demonstrate the superior performance of these algorithms in terms of multiple metrics. \nJoint work with Tracey Ho\, Ying Cui\, Ran Liu\, Michael Burd\, and Derek Leong \nBio: Edmund Yeh received his B.S. in Electrical Engineering with Distinction and Phi Beta Kappa from Stanford University in 1994. He then studied at Cambridge University on the Winston Churchill Scholarship\, obtaining his M.Phil in Engineering in 1995. He received his Ph.D. in Electrical Engineering and Computer Science from MIT under Professor Robert Gallager in 2001. He is currently Professor of Electrical and Computer Engineering at Northeastern University. He was previously Assistant and Associate Professor of Electrical Engineering\, Computer Science\, and Statistics at Yale University. He has held visiting positions at MIT\, Stanford\, Princeton\, UC Berkeley\, EPFL\, and TU Munich. \n\n\nProfessor Yeh was one of the PIs on the original NSF-funded FIA Named Data Networking project. He will serve as General Co-Chair for ACM Conference on Information Centric Networking (ICN) 2018 in Boston. He is the recipient of the Alexander von Humboldt Research Fellowship\, the Army Research Office Young Investigator Award\, the Winston Churchill Scholarship\, the National Science Foundation and Office of Naval Research Graduate Fellowships\, the Barry M. Goldwater Scholarship\, the Frederick Emmons Terman Engineering Scholastic Award\, and the President’s Award for Academic Excellence (Stanford University). Professor Yeh has served as the Secretary of the Board of Governors of the IEEE Information Theory Society. He received the Best Paper Award at the 2015 IEEE International Conference on Communications (ICC) Communication Theory Symposium. \n\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/networking-big-data-theory-and-optimization-ndn
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170919T160000
DTEND;TZID=UTC:20170919T170000
DTSTAMP:20260524T195750
CREATED:20170831T221731Z
LAST-MODIFIED:20170831T230256Z
UID:6060-1505836800-1505840400@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series: Edmund Yeh (Northeastern University)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-edmund-yeh-northeastern-university/
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170522T160000
DTEND;TZID=America/New_York:20170522T160000
DTSTAMP:20260524T195750
CREATED:20190627T212122Z
LAST-MODIFIED:20190627T212842Z
UID:10083-1495468800-1495468800@idss-stage.mit.edu
SUMMARY:Industrial Autonomous Systems: Vision and State of the Art
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing.
URL:https://idss-stage.mit.edu/calendar/industrial-autonomous-systems-vision-and-state-of-the-art-2/
LOCATION:32-D677\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170516T160000
DTEND;TZID=America/New_York:20170516T160000
DTSTAMP:20260524T195750
CREATED:20190627T212122Z
LAST-MODIFIED:20190627T212647Z
UID:10085-1494950400-1494950400@idss-stage.mit.edu
SUMMARY:Stable Optimal Control and Semicontractive Dynamic Programming
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/stable-optimal-control-and-semicontractive-dynamic-programming-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170419T160000
DTEND;TZID=America/New_York:20170419T160000
DTSTAMP:20260524T195750
CREATED:20190627T212126Z
LAST-MODIFIED:20190627T212126Z
UID:10091-1492617600-1492617600@idss-stage.mit.edu
SUMMARY:The Landscape of Some Statistical Problems
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/the-landscape-of-some-statistical-problems-2/
LOCATION:E18-304\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170413T160000
DTEND;TZID=America/New_York:20170413T160000
DTSTAMP:20260524T195750
CREATED:20190627T212127Z
LAST-MODIFIED:20190627T212127Z
UID:10093-1492099200-1492099200@idss-stage.mit.edu
SUMMARY:Data-Driven Models in Power Systems
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/data-driven-models-in-power-systems-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170411T160000
DTEND;TZID=America/New_York:20170411T160000
DTSTAMP:20260524T195750
CREATED:20190627T212127Z
LAST-MODIFIED:20190627T212127Z
UID:10094-1491926400-1491926400@idss-stage.mit.edu
SUMMARY:Geometries of Word Embeddings
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/geometries-of-word-embeddings-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170406T160000
DTEND;TZID=America/New_York:20170406T160000
DTSTAMP:20260524T195750
CREATED:20190627T212128Z
LAST-MODIFIED:20190627T212128Z
UID:10097-1491494400-1491494400@idss-stage.mit.edu
SUMMARY:A System Level Approach to Controller Synthesis
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/a-system-level-approach-to-controller-synthesis-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170404T160000
DTEND;TZID=America/New_York:20170404T160000
DTSTAMP:20260524T195750
CREATED:20190627T212132Z
LAST-MODIFIED:20190627T212132Z
UID:10098-1491321600-1491321600@idss-stage.mit.edu
SUMMARY:Capacity via Symmetry: Extensions and Practical Consequences
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/capacity-via-symmetry-extensions-and-practical-consequences-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
END:VCALENDAR