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DTSTART;TZID=America/New_York:20191125T160000
DTEND;TZID=America/New_York:20191125T170000
DTSTAMP:20260524T181228
CREATED:20190920T151129Z
LAST-MODIFIED:20190920T151129Z
UID:10851-1574697600-1574701200@idss-stage.mit.edu
SUMMARY:LIDS Seminar - Rayadurgam Srikant (University of Illinois at Urbana-Champaign)
DESCRIPTION:TBD \nBio: \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-rayadurgam-srikant-university-illinois-urbana-champaign
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191118T160000
DTEND;TZID=America/New_York:20191118T170000
DTSTAMP:20260524T181228
CREATED:20190920T151029Z
LAST-MODIFIED:20190920T151029Z
UID:10849-1574092800-1574096400@idss-stage.mit.edu
SUMMARY:LIDS Seminar - Sujay Sanghavi (University of Texas at Austin)
DESCRIPTION:TBD \nBio: \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-sujay-sanghavi-university-texas-austin
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191028T160000
DTEND;TZID=America/New_York:20191028T170000
DTSTAMP:20260524T181228
CREATED:20190920T150920Z
LAST-MODIFIED:20190920T150920Z
UID:10846-1572278400-1572282000@idss-stage.mit.edu
SUMMARY:The Age of Information in Networks: Moments\, Distributions\, and Sampling
DESCRIPTION:We examine a source providing status updates to monitors through a network with state defined by a continuous-time finite Markov chain. Using an age of information (AoI) metric\, we characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach\, we derive first-order linear differential equations for the temporal evolution of both the age moments and a moment generating function (MGF) of the age vector components. We show that the existence of a non-negative fixed point for the first moment is sufficient to guarantee convergence of all higher-order moments as well as a region of convergence for the stationary MGF vector of the age. The stationary MGF vector is then found for the age on a line network of preemptive memoryless servers. It is found that the age at a node is identical in distribution to the sum of independent exponential service times. This observation is then generalized to linear status sampling networks in which each node receives samples of the update process at each preceding node according to a renewal point process. For each node in the line\, the age is shown to be identical in distribution to a sum of independent renewal process age random variables. \nBio: Roy Yates is a Distinguished Professor with the Wireless Information Networks Laboratory (WINLAB) and the Electrical and Computer Engineering (ECE) department at Rutgers University. He received the B.S.E. degree in 1983 from Princeton University\, and the S.M. and Ph.D. degrees in 1986 and 1990 from M.I.T.\, all in Electrical Engineering. He is an author of three editions of the John Wiley textbook “Probability and Stochastic Processes: A Friendly Introduction for Electrical Engineers.” An IEEE Fellow in 2011\, Dr. Yates is a past associate editor of the IEEE Journal on Selected Areas of Communication Series in Wireless Communication and also a past Associate Editor for Communication Networks of the IEEE Transactions on Information Theory. \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/age-information-networks-moments-distributions-and-sampling%C2%A0
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191021T160000
DTEND;TZID=America/New_York:20191021T170000
DTSTAMP:20260524T181228
CREATED:20190920T150813Z
LAST-MODIFIED:20191010T121729Z
UID:10844-1571673600-1571677200@idss-stage.mit.edu
SUMMARY:LIDS Seminar - George Pappas (University of Pennsylvania)
DESCRIPTION:TBD \nBio: \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-george-pappas-university-pennsylvania
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191001T160000
DTEND;TZID=America/New_York:20191001T170000
DTSTAMP:20260524T181228
CREATED:20190920T150647Z
LAST-MODIFIED:20190920T150647Z
UID:10842-1569945600-1569949200@idss-stage.mit.edu
SUMMARY:Data-driven Coordination of Distributed Energy Resources
DESCRIPTION:The integration of distributed energy resources (DERs)\, e.g.\, rooftop photovoltaics installations\, electric energy storage devices\, and flexible loads\, is becoming prevalent. This integration poses numerous operational challenges on the lower-voltage systems to which the DERs are connected\, but also creates new opportunities for the provision of grid services. In the first part of the talk\, we discuss one such operational challenge—ensuring proper voltage regulation in the distribution network to which DERs are connected. To address this problem\, we propose a Volt/VAR control architecture that relies on the proper coordination of conventional voltage regulation devices\, e.g.\, tap changing under load (TCUL) transformers and switched capacitors and DERs with reactive power provision capability. In the second part of the talk\, we discuss one such opportunity—utilizing DERs to provide regulation services to the bulk power grid. To leverage this opportunity\, we propose a scheme for coordinating the response of the DERs so that the power injected into the distribution network (to which the DERs are connected) follows some regulation signal provided by the bulk power system operator. Throughout the talk\, we assume limited knowledge of the particular power system models and develop data-driven methods to learn them. We then utilize these models to design appropriate controls for determining the set-points of DERs (and other assets\, e.g.\, TCULs) in an optimal or nearly-optimal fashion. \nBio: \nAlejandro Dominguez-Garcıa is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign\, where he is affiliated with the Power and Energy Systems area. Also within ECE Illinois\, he is a Research Professor in the Coordinated Science Laboratory and in the Information Trust Institute and has been a Grainger Associate since 2011\, and a William L. Everitt Scholar since 2017. His research program aims at the development of technologies for providing a reliable and efficient supply of electricity. Specific activities within his program include work on: (i) control of distributed energy resources\, (ii) power system health monitoring and reliability analysis\, and (iii) quantifying and mitigating the impact of renewable-based generation.\n\nProfessor Dom´ınguez-Garc´ıa received the degree of “Ingeniero Industrial” from the University of Oviedo in 2001\, and the Ph.D. degree in electrical engineering and computer science from MIT in 2007. He also spent time as a post-doctoral research associate at MIT before joining the Illinois faculty in 2008. He received the NSF CAREER Award in 2010\, and the Young Engineer Award from the IEEE Power and Energy Society in 2012. In 2014\, he was invited by the National Academy of Engineering to attend the US Frontiers of Engineering Symposium and was selected by the University of Illinois at Urbana-Champaign Provost to receive a Distinguished Promotion Award. In 2015\, he received the U of I College of Engineering Dean’s Award for Excellence in Research. He is currently an associate editor of the IEEE Transactions on Control of Network Systems. He also served as an editor of the IEEE Transactions on Power Systems and IEEE Power Engineering Letters from 2011 to 2017.\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/data-driven-coordination-distributed-energy-resources
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190923T140000
DTEND;TZID=America/New_York:20190923T150000
DTSTAMP:20260524T181228
CREATED:20190920T150517Z
LAST-MODIFIED:20190920T150517Z
UID:10840-1569247200-1569250800@idss-stage.mit.edu
SUMMARY:Power of Experimental Design and Active Learning
DESCRIPTION:Classical supervised machine learning algorithms focus on the setting where the algorithm has access to a fixed labeled dataset obtained prior to any analysis. In most applications\, however\, we have control over the data collection process such as which image labels to obtain\, which drug-gene interactions to record\, which network routes to probe\, which movies to rate\, etc. Furthermore\, most applications face budget limitations on the amount of labels that can be collected. Experimental design and active learning are two paradigms that involve careful selection of data points to label from a large unlabeled pool. This talk will discuss and contrast the power of experimental design and active learning\, starting with some recent advances in these paradigms and then posing open questions involving their integration and application to deep models. \nBio: Aarti Singh is an Associate Professor in the Machine Learning Department at Carnegie Mellon University. Her research lies at the intersection of machine learning\, statistics and signal processing\, and focuses on designing statistically and computationally efficient algorithms for learning from direct\, compressive and interactive queries. Her work is recognized by an NSF Career Award\, the United States Air Force Young Investigator Award\, A. Nico Habermann Junior Faculty Chair Award\, Harold A. Peterson Best Dissertation Award\, and three best student paper awards. Her service honors include serving as Program Chair for the International Conference on Machine Learning (ICML) 2020\, Program Chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference\, member of the National Academy of Sciences (NAS) Committee on Applied and Theoretical Statistics\, guest editor for Electronic Journal of Statistics\, and Associate Editor of the IEEE Transactions on Information Theory and IEEE Transactions on Signal and Information Processing over Networks. \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/power-experimental-design-and-active-learning
LOCATION:E18-304\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190916T160000
DTEND;TZID=America/New_York:20190916T170000
DTSTAMP:20260524T181228
CREATED:20190920T150317Z
LAST-MODIFIED:20190920T150317Z
UID:10838-1568649600-1568653200@idss-stage.mit.edu
SUMMARY:Dynamic Monitoring and Decision Systems (DyMonDS) Framework for Data-Enabled Integration in Complex Electric Energy Systems
DESCRIPTION:In this talk\, we introduce a unifying Dynamic Monitoring and Decision Systems (DyMonDS) framework that is based on multi-layered modeling for aggregation and minimal coordination of interactions between the layers of complex electric energy systems. Using this approach\, distributed control and optimization problems are formulated so that: (1) the low-level decision-makers optimize cost of local interactions while accounting for their heterogeneous technologies\, as well as for their social and risk preferences; and\, (2) the higher layer aggregators and coordinators optimize the cost of all interactions at their levels to enable cooperative control. The interactions of each layer are abstracted by using unifying energy state space and the Lagrange coefficients associated with the general physical laws. This sets the bases for both nonlinear control of power electronically-switched automation and for market design formulation. Potential benefits (such as enhanced reliability\, resiliency\, and efficiency) from integrating flexible technologies\, storage\, and control\, in particular\, are illustrated on simple IEEE test systems. \nBio: 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. She is also a Senior Research Scientist at MIT in LIDS and IDSS. She is an IEEE Life Fellow. She was the first recipient of the NSF Presidential Young Investigator Award for Power Systems. In addition to her academic work\, she has gained considerable industry experience as 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 with participants from academia\, government\, and industry. She was the founder and co-director of the Electric Energy Systems Group (EESG) at Carnegie Mellon University. Currently\, she is building EESG@MIT\, in the same spirit as EESG@CMU. \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/dynamic-monitoring-and-decision-systems-dymonds-framework-data-enabled
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190514T160000
DTEND;TZID=America/New_York:20190514T170000
DTSTAMP:20260524T181228
CREATED:20190301T172026Z
LAST-MODIFIED:20190501T142034Z
UID:8991-1557849600-1557853200@idss-stage.mit.edu
SUMMARY:Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery
DESCRIPTION:The overarching goal of my research is to develop cutting-edge machine learning\, AI and operations research theory\, methods\, algorithms\, and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways\, better utilize resources & reduce costs; and inform public health. \nTo do this\, Prof. van der Schaar is creating what she calls Learning Engines for Healthcare (LEH’s). An LEH is an integrated ecosystem that uses machine learning\, AI and operations research to provide clinical insights and healthcare intelligence to all the stakeholders (patients\, clinicians\, hospitals\, administrators). In contrast to an Electronic Health Record\, which provides a static\, passive\, isolated display of information\, an LEH provides a dynamic\, active\, holistic & individualized display of information including alerts. \nIn this talk Prof. van der Schaar will focus on 3 steps in the development of LEH’s: \n\nBuilding a comprehensive model that accommodates irregularly sampled\, temporally correlated\, informatively censored and non-stationary processes in order to understand and predict the longitudinal trajectories of diseases.\nEstablishing the theoretical limits of causal inference and using what has been established to create a new approach that makes it possible to better estimate individualized treatment effects.\nUsing Machine Learning itself to automate the design and construction of entire pipelines of Machine Learning algorithms for risk prediction\, screening\, diagnosis\, and prognosis.\n\nBio: Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning\, Artificial Intelligence\, and Medicine at the University of Cambridge\, a Turing Faculty Fellow at The Alan Turing Institute in London\, where she leads the effort on data science and machine learning for personalized medicine. Prior to this\, she was a Chancellor’s Professor at UCLA and MAN Professor of Quantitative Finance at the University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018).  She has also been the recipient of an NSF Career Award\, 3 IBM Faculty Awards\, the IBM Exploratory Stream Analytics Innovation Award\, the Philips Make a Difference Award and several best paper awards\, including the IEEE Darlington Award. She holds 35 granted USA patents. Her current research focus is on data science\, machine learning\, AI and operations research for medicine. \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-mihaela-van-der-schaar
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190430T160000
DTEND;TZID=America/New_York:20190430T170000
DTSTAMP:20260524T181228
CREATED:20190301T171816Z
LAST-MODIFIED:20190501T142201Z
UID:8989-1556640000-1556643600@idss-stage.mit.edu
SUMMARY:On Coupling Methods for Nonlinear Filtering and Smoothing
DESCRIPTION:Bayesian inference for non-Gaussian state-space models is a ubiquitous problem with applications ranging from geophysical data assimilation to mathematical finance. We will discuss how deterministic couplings between probability distributions enable new solutions to this problem. \nWe first consider filtering in high-dimensional models with nonlinear (potentially chaotic) dynamics and sparse observations in space and time. While the ensemble Kalman filter (EnKF) yields robust ensemble approximations of the filtering distribution in this setting\, it is limited by linear forecast-to-analysis transformations. To generalize the EnKF\, we propose a methodology that transforms the non-Gaussian forecast ensemble at each assimilation step into samples from the current filtering distribution via a sequence of local nonlinear couplings. These couplings are based on transport maps that can be computed quickly using convex optimization\, and that can be enriched in complexity to reduce the intrinsic bias of the EnKF. We discuss the low-dimensional structure inherited by the transport maps from the filtering problem\, including decay of correlations\, conditional independence\, and local likelihoods. We then exploit this structure to regularize the estimation of the maps in high dimensions and with a limited ensemble size. \nWe also present variational methods—again based on transport—for smoothing and sequential parameter estimation in non-Gaussian state-space models. These methods rely on results linking the Markov properties of a target measure to the existence of low-dimensional couplings\, induced by transport maps that are decomposable. The resulting algorithms can be understood as a generalization\, to the non-Gaussian case\, of the square-root Rauch–Tung–Striebel Gaussian smoother. \nThis is joint work with Ricardo Baptista\, Daniele Bigoni\, and Alessio Spantini. \nBio: Youssef Marzouk is an associate professor in the Department of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Engineering. He is also director of MIT’s Aerospace Computational Design Laboratory and a member of MIT’s Statistics and Data Science Center. \nHis research interests lie at the intersection of physical modeling with statistical inference and computation. In particular\, he develops methodologies for uncertainty quantification\, inverse problems\, large-scale Bayesian computation\, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering\, environmental\, and geophysics applications. \nHe received his SB\, SM\, and PhD degrees from MIT and spent several years at Sandia National Laboratories before joining the MIT faculty in 2009. He is a recipient of the Hertz Foundation Doctoral Thesis Prize (2004)\, the Sandia Laboratories Truman Fellowship (2004-2007)\, the US Department of Energy Early Career Research Award (2010)\, and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering (2012). He is an Associate Fellow of the AIAA and currently serves on the editorial boards of the SIAM Journal on Scientific Computing\, Advances in Computational Mathematics\, and the SIAM/ASA Journal on Uncertainty Quantification. He is an avid coffee drinker and occasional classical pianist. \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-youssef-marzouk
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190423T160000
DTEND;TZID=America/New_York:20190423T170000
DTSTAMP:20260524T181228
CREATED:20190301T171624Z
LAST-MODIFIED:20190501T142253Z
UID:8987-1556035200-1556038800@idss-stage.mit.edu
SUMMARY:Memory-Efficient Adaptive Optimization for Humungous-Scale Learning
DESCRIPTION:Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each model parameter\, thus doubling the memory footprint of the optimizer. In behemoth-size applications\, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel\, simple\, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training some of the largest deep models used at Google. \nBio: Yoram Singer is the head of Principles Of Effective Machine learning (POEM) research group in Google Brain and a professor of Computer Science at Princeton.  He was a member of the technical staff at AT&T Research from 1995 through 1999 and an associate professor at the Hebrew University from 1999 through 2007. He is a fellow of AAAI. His research on machine learning algorithms received several awards. \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-yoram-singer
LOCATION:32-G449 (KIva/Patel)
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190409T160000
DTEND;TZID=America/New_York:20190409T170000
DTSTAMP:20260524T181228
CREATED:20190301T171340Z
LAST-MODIFIED:20190501T142316Z
UID:8985-1554825600-1554829200@idss-stage.mit.edu
SUMMARY:Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity
DESCRIPTION:We consider a seller who can dynamically adjust the price of a product at the individual customer level\, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand model\, parameters of which depend on $s$ out of the $d$ features. The seller initially does not know the relationship between the customer features and the product demand\, but learns this through sales observations over a selling horizon of $T$ periods. We prove that the seller’s expected regret\, i.e.\, the revenue loss against a clairvoyant who knows the underlying demand relationship\, is at least of order $s\sqrt{T}$ under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order $s\sqrt{T}log(T)$. We extend this policy to a more realistic setting where the seller does not know the true demand predictors\, and show this policy has an expected regret of order $s\sqrt{T}(log(d)＋log(T))$\, which is also near-optimal. Finally\, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets\, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then-optimize policies. Furthermore\, our policy significantly improves upon the loan company’s historical pricing decisions in terms of annual expected revenue. \nBio: Gah-Yi Ban is an Assistant Professor of Management Science and Operations at London Business School. Gah-Yi’s research is in Big Data analytics\, specifically decision-making with complex\, high-dimensional and/or highly uncertain data with applications to operations management and finance. Gah-Yi’s research has appeared on most-downloaded lists of Management Science and Operations Research\, and awarded Honorable Mention in 2018 INFORMS JFIG Paper Competition. Gah-Yi graduated from UC Berkeley with MSc/MA/PhD in Industrial Engineering/ Statistics/Operations Research. \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://idss-stage.mit.edu/calendar/lids-seminar-gah-yi-ban-london-business-school/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190312T160000
DTEND;TZID=America/New_York:20190312T170000
DTSTAMP:20260524T181228
CREATED:20190301T170833Z
LAST-MODIFIED:20190501T142423Z
UID:8981-1552406400-1552410000@idss-stage.mit.edu
SUMMARY:Automatic Computation of Exact Worst-Case Performance for First-Order Methods
DESCRIPTION:Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). \nWe show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs\, which provide both the performance bounds and functions on which these are reached.  Our formulation is based on a necessary and sufficient condition for smooth (strongly) convex interpolation\, allowing for a finite representation for smooth (strongly) convex functions in this context. These results allow improving the performance bounds of many classical algorithms\, and better understanding their dependence on the algorithm’s parameters\, leading to new optimized parameters\, and thus stronger performances. \nOur approach can be applied via the PESTO Toolbox\, which let the user describe algorithms in a natural way. \nBio: Julien M. Hendrickx is professor of mathematical engineering at Université catholique de Louvain\, in the Ecole Polytechnique de Louvain since 2010. He is on sabbatical at Boston University in 2018-19\, holding a WBI-World excellence fellowship. \nHe obtained an engineering degree in applied mathematics (2004) and a PhD in mathematical engineering (2008) from the same university. He has been a visiting researcher at the University of Illinois at Urbana Champaign in 2003-2004\, at the National ICT Australia in 2005 and 2006\, and at the Massachusetts Institute of Technology in 2006 and 2008. He was a postdoctoral fellow at the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology 2009 and 2010\, holding postdoctoral fellowships of the F.R.S.-FNRS (Fund for Scientific Research) and of Belgian American Education Foundation. \nDoctor Hendrickx is the recipient of the 2008 EECI award for the best PhD thesis in Europe in the field of Embedded and Networked Control\, and of the Alcatel-Lucent-Bell 2009 award for a PhD thesis on original new concepts or application in the domain of information or communication technologies. \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://idss-stage.mit.edu/calendar/automatic-computation-of-exact-worst-case-performance-for-first-order-methods/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190226T160000
DTEND;TZID=America/New_York:20190226T170000
DTSTAMP:20260524T181228
CREATED:20190301T170447Z
LAST-MODIFIED:20190501T142448Z
UID:8979-1551196800-1551200400@idss-stage.mit.edu
SUMMARY:Coded Computing: A Transformative Framework for Resilient\, Secure\, and Private Distributed Learning
DESCRIPTION:This talk introduces “Coded Computing”\, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing and machine learning\, such as resiliency to stragglers and bandwidth bottleneck. Furthermore\, coded computing can enable (information-theoretically) secure and private learning over untrusted workers that is gaining increasing importance in various application domains. In particular\, we present CodedPrivateML for distributed learning\, which keeps both the data and the model private while allowing efficient parallelization of training across untrusted distributed workers. We demonstrate that CodedPrivateML can provide an order of magnitude speedup (up to ~30x) over the cryptographic approaches that rely on secure multiparty computing. \nBio: Salman Avestimehr is a Professor of Electrical Engineering and co-director of Communication Sciences Institute at the University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science\, both from the University of California\, Berkeley. Prior to that\, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003.  His research interests include information theory and coding\, distributed computing\, and machine learning. Dr. Avestimehr has received a number of awards\, including a Communications Society and Information Theory Society Joint Paper Award\, the Presidential Early Career Award for Scientists and Engineers (PECASE)\, a Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research\, a National Science Foundation CAREER award\, and several best paper awards. He is currently an Associate Editor for the IEEE Transactions on Information Theory and a General Co-Chair of the 2020 International Symposium on Information Theory (ISIT). \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://idss-stage.mit.edu/calendar/coded-computing-a-transformative-framework-for-resilient-secure-and-private-distributed-learning/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190219T160000
DTEND;TZID=America/New_York:20190219T170000
DTSTAMP:20260524T181228
CREATED:20190301T165622Z
LAST-MODIFIED:20190501T142520Z
UID:8976-1550592000-1550595600@idss-stage.mit.edu
SUMMARY:Safeguarding Privacy in Dynamic Decision-Making Problems
DESCRIPTION:The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for her goal\, while simultaneously ensuring these actions do not inadvertently reveal her private information\, even when observed and analyzed by a powerful adversary? We will examine two well-known decision problems (path planning and online learning)\, and in both cases establish sharp\, information-theoretic complexity vs. privacy tradeoff. As a by-product\, our analysis also leads to simple yet provably efficient algorithms for both the decision maker and eavesdropping adversary. Based in part on joint work with John N. Tsitsiklis and Zhi Xu (MIT). \nBio: Kuang Xu was born in Suzhou\, China. He is an Assistant Professor of Operations\, Information and Technology at the Stanford Graduate School of Business\, Stanford University. He received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign\, Urbana\, Illinois\, USA\, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology\, Cambridge\, Massachusetts\, USA. His research interests lie in the fields of applied probability theory\, optimization\, and operations research\, seeking to understand fundamental properties and design principles of large-scale stochastic systems\, with applications in queueing networks\, healthcare\, privacy\, and statistical learning theory. He has received several awards including a First Place in INFORMS George E. Nicholson Student Paper Competition\, a Best Paper Award\, as well as a Kenneth C. Sevcik Outstanding Student Paper Award from ACM SIGMETRICS. \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://idss-stage.mit.edu/calendar/kuang-xu/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181210T160000
DTEND;TZID=America/New_York:20181210T170000
DTSTAMP:20260524T181228
CREATED:20180810T161120Z
LAST-MODIFIED:20190501T143433Z
UID:8174-1544457600-1544461200@idss-stage.mit.edu
SUMMARY:Symmetry\, Bifurcation\, and Multi-Agent Decision-Making
DESCRIPTION:Prof. Leonard will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups\, including house-hunting honeybees and schooling fish\, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment. \nBio: Prof. Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in Applied and Computational Mathematics at Princeton University. She is a MacArthur Fellow\, and Fellow of the American Academy of Arts and Sciences\, SIAM\, IEEE\, IFAC\, and ASME. She received her BSE in Mechanical Engineering from Princeton University and her PhD in Electrical Engineering from the University of Maryland. Her research is in control and dynamics with application to multi-agent systems\, mobile sensor networks\, collective animal behavior\, and human decision dynamics. \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-6
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181126T160000
DTEND;TZID=America/New_York:20181126T170000
DTSTAMP:20260524T181228
CREATED:20180810T160857Z
LAST-MODIFIED:20190501T143456Z
UID:8172-1543248000-1543251600@idss-stage.mit.edu
SUMMARY:Transportation Systems Resilience: Capacity-Aware Control and Value of Information
DESCRIPTION:Resilience of a transportation system is its ability to operate under adverse events like incidents and storms. Availability of real-time traffic data provides new opportunities for predicting travelers’ routing behavior and implementing network control operations during adverse events. In this talk\, we will discuss two problems: controlling highway corridors in response to disruptions and modeling strategic route choices of travelers with heterogeneous access to incident information. Firstly\, we present an approach to designing control strategies for highway corridors facing stochastic capacity disruptions such random incidents and vehicle platoons/moving bottlenecks. We exploit the properties of traffic flow dynamics under recurrent incidents to derive verifiable conditions for stability of traffic queues\, and also obtain guarantees on the system throughput. Secondly\, we introduce a routing game in which travelers receive asymmetric and incomplete information about uncertain network state\, and make route choices based on their private beliefs about the state and other travelers’ behavior. We study the effects of information heterogeneity on travelers’ equilibrium route choices and costs. Our analysis is useful for evaluating the value of receiving state information for travelers\, which can be positive\, zero\, or negative in equilibrium. These results demonstrate the advantages of considering network state uncertainty in both strategic and operational aspects of system resilience. \nBio: Saurabh Amin is Robert N. Noyce Career Development Associate Professor in the Department of Civil and Environmental Engineering at MIT. He is also affiliated with the Institute of Data\, Systems and Society and the Operations Research Center at MIT. His research focuses on the design of network inspection and control algorithms for infrastructure systems resilience. He studies the effects of security attacks and natural events on the survivability of cyber-physical systems\, and designs incentive mechanisms to reduce network risks. Dr. Amin received his Ph.D. from the University of California\, Berkeley in 2011. His research is supported by NSF CPS FORCES Frontiers project\, NSF CAREER award\, Google Faculty Research award\, DoD-Science of Security Program\, and Siebel Energy Institute Grant. \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-saurabh-amin
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181119T160000
DTEND;TZID=America/New_York:20181119T170000
DTSTAMP:20260524T181228
CREATED:20180810T160618Z
LAST-MODIFIED:20190501T143517Z
UID:8170-1542643200-1542646800@idss-stage.mit.edu
SUMMARY:Modeling Electricity Markets with Complementarity: Why It's Important (and Fun)
DESCRIPTION:Electric power: done wrong\, it drags the economy and environment down; done right\, it could help to create a more efficient\, brighter\, and cleaner future. Better policy\, planning\, and operations models–both simple analytical\, and complex computational ones–are essential if we’re going to do it right. Better modeling is also fun\, as the math of electricity models is inherently interesting and revealing –models often show flaws in our intuition. Used intelligently\, models can point us towards better regulations\, investments\, and operating policies. Simple models provide insights\, while complex models provide the numbers needed to choose specific investments and policies. \n\nComplementarity is one optimization-based approach to modeling energy markets that has more flexibility to model market failures than standard optimization methods. Prof. Hobbs will highlight one application using the power market model COMPETES: the design of renewable portfolio standards\, and an analysis of their price and economic efficiency impacts in the Year 2030. The focus is on energy versus capacity subsidies in the European Union; capacity subsidies are being promoted as potentially being more effective in promoting technology learning. They also have less of an impact upon electricity prices. Prof. Hobbs will also examine the cost of country-specific targets versus EU-wide targets. \nAcknowledgments: Government of the Netherlands and NSF for funding; my PBL colleagues Ozge Ozdemir\, Paul Koustaal\, and Marit van Hout. \n\nBio: B.F. Hobbs earned a Ph.D. (Environmental Systems Engineering) in 1983 from Cornell University. He holds the Theodore M. and Kay W. Schad Chair of Environmental Management at the Johns Hopkins University\, where he has been in the Department of Geography & Environmental Engineering (now Environmental Health & Engineering) since 1995. He also holds a joint appointment in the Department of Applied Mathematics & Statistics and is founding director of the JHU Environment\, Energy\, Sustainability & Health Institute. He co-directs the EPA Yale-JHU Center for Solutions for Energy\, Air\, Climate and Health (SEArCH). Previously\, he was at Brookhaven and Oak Ridge National Laboratories and a member of the Systems Engineering and Civil Engineering faculty at Case Western Reserve University. \nHis research and teaching concern the application of systems analysis and economics to electric utility regulation\, planning\, and operations\, as well as environmental and water resources systems. Dr. Hobbs has previously held visiting appointments at CalTech\, Comillas Pontifical University\, Helsinki University of Technology\, University of Washington\, Netherlands Energy Research Center\, and Cambridge University. He chairs the Market Surveillance Committee of the California Independent System Operator. He was named an NSF Presidential Young Investigator in 1986. Dr. Hobbs is a Fellow of the IEEE and INFORMS. \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-5
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181113T080000
DTEND;TZID=America/New_York:20181113T170000
DTSTAMP:20260524T181228
CREATED:20180810T160435Z
LAST-MODIFIED:20190501T143540Z
UID:8168-1542096000-1542128400@idss-stage.mit.edu
SUMMARY:Functional Representation of Random variables and Applications
DESCRIPTION:The functional representation lemma says that given random variables X and Y\, there exists a random variable Z\, independent of X\, and a function g(x\,z) such that Y=g(X\,Z). This lemma has had several applications in information theory aimed at simplifying computations of certain information functional. I will present a strengthened version of this lemma and applications to several one-shot coding problems. The first application is to channel simulation with common randomness\, where we obtain an improved bound on the achievable rate by Harsha et al. that applies to arbitrary (not just discrete) random variables. More interestingly\, the Poisson construction used in the proof of the strengthened lemma leads to new and simple achievability results for one-shot coding theorems\, including lossy source coding\, multiple description coding\, and the Gray–Wyner system. I will end with an application of the Poisson construction to minimax learning for remote inference. \nThe new results presented in this talk are joint with Cheuk Ting Li\, Xiugang Wu\, and Ayfer Ozgur. \nBio: Abbas El Gamal is the Hitachi America Professor in the School of Engineering at Stanford University. He received his B.Sc. Honors degree from Cairo University in 1972\, and his M.S. in Statistics and Ph.D. in Electrical Engineering both from Stanford University in 1977 and 1978\, respectively. From 1978 to 1980\, he was an Assistant Professor of Electrical Engineering at USC. From 2003 to 2012\, he was Director of the Information Systems Laboratory at Stanford University. From 2012-2017 he was the Fortinet Founders Chair of the Department of Electrical Engineering at Stanford University. His research contributions have been in network information theory\, FPGAs\, digital imaging devices and systems\, and smart grid modeling and control. He has authored or coauthored over 230 papers and holds 35 patents in these areas. He is a coauthor of the book Network Information Theory (Cambridge Press 2011). He is a member of the US National Academy of Engineering and a Fellow of the IEEE. He received several awards for his research contributions\, including the 2016 IEEE Richard Hamming Medal and the 2012 Claude E. Shannon Award. He served on the Board of Governors of the Information Theory Society from 2009 to 2016 and was its President in 2014. He has been involved in several Silicon Valley startups as co-founder\, a board of director member\, advisor and in several key technical and management positions. \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/lids-seminar-series-abbas-el-gamal
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181029T160000
DTEND;TZID=America/New_York:20181029T170000
DTSTAMP:20260524T181228
CREATED:20180810T160254Z
LAST-MODIFIED:20190501T143409Z
UID:8166-1540828800-1540832400@idss-stage.mit.edu
SUMMARY:Computing with Assemblies
DESCRIPTION:Computation in the brain has been modeled productively at many scales\, ranging from molecules to dendrites\, neurons\, and synapses\, all the way to the whole brain models useful in cognitive science. I will discuss recent work on an intermediate layer\, involving assemblies of neurons — that is to say\, sets of neurons firing together in a repetitive pattern whenever we think of a particular memory\, concept or idea. Assemblies have been conjectured six decades ago by Hebb\, and have been over the past decade noticed in both the animal and the human brain. Further\, experiments\, simulations\, and theoretical analysis suggest that assemblies can be copied from one brain area to another\, and associated with other assemblies to encode affinity. We propose a broader “calculus” of assemblies\, including operations such as “reciprocal-project” and “merge”\, comprising a powerful computational model. One interesting hypothesis is that assembly operations may underlie some of the most advanced functions of the brain\, such as reasoning\, planning\, language\, math. Work with Santosh Vempala\, Wolfgang Maass\, and Michael Collins. \n\n\nBio: Christos H. Papadimitriou is the Donovan Family professor of computer science at Columbia University. Before joining Columbia in 2017\, he taught at UC Berkeley for 22 years\, and before that at Harvard\, MIT\, NTU Athens\, Stanford\, and UCSD. He has written five textbooks and many articles on algorithms and complexity\, and their applications to optimization\, databases\, control\, AI\, robotics\, economics and game theory\, the Internet\, evolution\, and more recently the study of the brain. He holds a Ph.D. from Princeton as well as eight honorary doctorates\, and he has won the Knuth prize\, the Goedel prize\, and the von Neumann Medal. He is a member of the National Academy of Sciences of the US\, the American Academy of Arts and Sciences\, and the National Academy of Engineering; in 2013 the president of Greece named him Commander of the Order of the Phoenix. He has also written three novels: “Turing”\, “Logicomix” and his latest “Independence”. \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/lids-seminar-series-4
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181022T160000
DTEND;TZID=America/New_York:20181022T170000
DTSTAMP:20260524T181228
CREATED:20180810T160117Z
LAST-MODIFIED:20190501T143713Z
UID:8164-1540224000-1540227600@idss-stage.mit.edu
SUMMARY:Distributed Statistical Estimation of High-Dimensional Distributions and Parameters under Communication Constraints
DESCRIPTION:Modern data sets are often distributed across multiple machines and processors\, and bandwidth and energy limitations in networks and within multiprocessor systems often impose significant bottlenecks on the performance of algorithms. Motivated by this trend\, we consider the problem of estimating high-dimensional distributions and parameters in a distributed network\, where each node in the network observes an independent sample from the underlying distribution and can communicate it to a central processor by writing at most k bits on a public blackboard. We obtain matching upper and lower bounds for the minimax risk of estimating the underlying distribution or parameter under various common statistical models. Our results show that the impact of the communication constraint can be qualitatively different depending on the tail behavior of the score function associated with each model. The key ingredient in our proof is a geometric characterization of Fisher information from quantized samples. \nJoint work with Leighton Barnes\, Yanjun Han\, and Tsachy Weissman. \nBio: Ayfer Ozgur received her Ph.D. degree in 2009 from the Information Processing Group at EPFL\, Switzerland. In 2010 and 2011\, she was a post-doctoral scholar at the same institution. She is an Assistant Professor in the Electrical Engineering Department at Stanford University since 2012. Her research interests include distributed communication and learning\, wireless systems\, and information theory. Dr. Ozgur received the EPFL Best Ph.D. Thesis Award in 2010\, an NSF CAREER award in 2013\, the Okawa Foundation Research Grant and the IEEE Communication Theory Technical Committee (CTTC) Early Achievement Award in 2018. \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/lids-seminar-series-3
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181015T160000
DTEND;TZID=America/New_York:20181015T170000
DTSTAMP:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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:20260524T181228
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
END:VCALENDAR