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DTSTART:20180311T070000
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DTSTART:20181104T060000
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DTSTART:20200308T070000
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211115T160000
DTEND;TZID=America/New_York:20211115T170000
DTSTAMP:20221001T162441
CREATED:20210920T163335Z
LAST-MODIFIED:20210920T163335Z
UID:14982-1636992000-1636995600@idss.mit.edu
SUMMARY:Two F-words in Peer Review (Fraud and Feedback)
DESCRIPTION:
URL:https://lids.mit.edu/news-and-events/events/two%C2%A0f-words%C2%A0-peer-review-fraud-and-feedback
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211108T160000
DTEND;TZID=America/New_York:20211108T170000
DTSTAMP:20221001T162441
CREATED:20210920T163223Z
LAST-MODIFIED:20210920T163223Z
UID:14979-1636387200-1636390800@idss.mit.edu
SUMMARY:LIDS Seminar - Rediet Abebe (Berkeley)
DESCRIPTION:
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-rediet-abebe-berkeley
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211025T160000
DTEND;TZID=America/New_York:20211025T170000
DTSTAMP:20221001T162441
CREATED:20210920T163123Z
LAST-MODIFIED:20210920T163123Z
UID:14977-1635177600-1635181200@idss.mit.edu
SUMMARY:LIDS Seminar - Jitendra Malik (Berkeley)
DESCRIPTION:
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-jitendra-malik-berkeley
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210316T160000
DTEND;TZID=America/New_York:20210316T170000
DTSTAMP:20221001T162441
CREATED:20210212T180857Z
LAST-MODIFIED:20210212T180857Z
UID:14178-1615910400-1615914000@idss.mit.edu
SUMMARY:LIDS Seminar - David Simchi-Levi (MIT)
DESCRIPTION:
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-david-simchi-levi-mit
LOCATION:Zoom
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210309T160000
DTEND;TZID=America/New_York:20210309T170000
DTSTAMP:20221001T162441
CREATED:20210212T180749Z
LAST-MODIFIED:20210212T180749Z
UID:14176-1615305600-1615309200@idss.mit.edu
SUMMARY:Balancing Covariates In Randomized Experiments Using The Gram–Schmidt Walk
DESCRIPTION:ABSTRACT\n\nRandomized Controlled Trials (RCTs) are the principal way we estimate the effectiveness of new medications\, procedures\, policies\, and interventions. In a typical medical trial\, the subjects are divided into two (or more) groups. One group of subjects is given the new medicine\, and the other is given a placebo or old medicine. Randomization is used to ensure that these test and control groups are probably similar and enables us to make statistical statements about the estimated treatment effects. When we know nothing about the experimental subjects\, a uniform random assignment of subjects to groups is the best we can do. But\, when we know properties of the subjects\, called covariates\, that we expect could be correlated with the outcome of the trial\, we can obtain more accurate estimates of treatment effects by balancing those properties between the groups. \nWe show how to use the recent Gram-Schmidt Walk algorithm of Bansal\, Dadush\, Garg\, and Lovett to construct balanced RCTs that produce more accurate estimates of treatment effects when treatment outcomes are correlated with linear functions of the covariates\, and which can not be much worse than uniformly random RCTs in the worst case. \nWe will formally explain the experimental design problem we address\, the Gram-Schmidt walk algorithm\, and the major ideas behind our analyses. This is joint work with Chris Harshaw\, Fredrik Sävje\, and Peng Zhang. \n\n\nBIOGRAPHY\n\nDaniel Alan Spielman is the Sterling Professor of Computer Science\, and Professor of Statistics and Data Science\, and of Mathematics at Yale. He received his B.A. in Mathematics and Computer Science from Yale in 1992\, and his Ph.D. in Applied Mathematics from M.I.T. in 1995. After spending a year as an NSF Mathematical Sciences Postdoctoral Fellow in the Computer Science Department at U.C. Berkeley\, he became a professor in the Applied Mathematics Department at M.I.T. He moved to Yale in 2006. \nHe has received many awards\, including the 1995 ACM Doctoral Dissertation Award\, the 2002 IEEE Information Theory Paper Award\, the 2008 and 2015 Godel Prizes\, the 2009 Fulkerson Prize\, the 2010 Nevanlinna Prize\, the 2014 Polya Prize\, the 2021 NAS Held Prize\, a Simons Investigator Award\, and a MacArthur Fellowship. He is a Fellow of the Association for Computing Machinery and a member of the National Academy of Sciences and the Connecticut Academy of Science and Engineering. His main research interests include the design and analysis of algorithms\, network science\, machine learning\, digital communications\, and scientific computing. \n\n\n\n
URL:https://lids.mit.edu/news-and-events/events/balancing-covariates-randomized-experiments-using-gram–schmidt-walk
LOCATION:Zoom
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210223T160000
DTEND;TZID=America/New_York:20210223T170000
DTSTAMP:20221001T162441
CREATED:20210212T180453Z
LAST-MODIFIED:20210218T170846Z
UID:14174-1614096000-1614099600@idss.mit.edu
SUMMARY:Challenges in Reliable Machine Learning
DESCRIPTION:ABSTRACT\n\nAs machine learning is increasingly deployed\, there is a need for reliable and robust methods that go beyond simple test accuracy. In this talk\, we will discuss two challenges that arise in reliable machine learning. The first is robustness to adversarial examples\, which are small imperceptible perturbations to legitimate test inputs that cause machine learning classifiers to misclassify. While recent work has proposed many attacks and defenses\, why exactly they arise still remains a mystery. In this talk\, we’ll take a closer look at this question. \nThe second problem is overfitting\, that many generative models are known to be prone to. Motivated by privacy concerns\, we formalize a form of overfitting that we call data-copying — where the generative model memorizes and outputs training samples or small variations thereof. We provide a three-sample test for detecting data-copying and study the performance of our test on several canonical models and datasets. \n\n\nBIOGRAPHY\n\nKamalika Chaudhuri is an Associate Professor at the University of California\, San Diego. She received a Bachelor of Technology degree in Computer Science and Engineering in 2002 from the Indian Institute of Technology\, Kanpur\, and a PhD in Computer Science from the University of California at Berkeley in 2007. After a postdoctoral stint at UCSD\, she joined the CSE department at UC San Diego as an assistant professor in 2010. She received an NSF CAREER Award in 2013 and a Hellman Faculty Fellowship in 2012. She has served as the program co-chair for AISTATS 2019 and ICML 2019. \nKamalika’s research interests lie in the foundations of trustworthy machine learning — or machine learning beyond accuracy\, which includes problems such as learning from sensitive data while preserving privacy\, learning under sampling bias\, and in the presence of an adversary.
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-kamalika-chaudhuri-university-california-san-diego
LOCATION:Zoom
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T160000
DTEND;TZID=America/New_York:20201130T170000
DTSTAMP:20221001T162441
CREATED:20200922T192059Z
LAST-MODIFIED:20200922T192059Z
UID:13089-1606752000-1606755600@idss.mit.edu
SUMMARY:LIDS Seminar - Magnus Egerstedt (Georgia Institute of Technology)
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-magnus-egerstedt-georgia-institute-technology
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201123T160000
DTEND;TZID=America/New_York:20201123T170000
DTSTAMP:20221001T162441
CREATED:20200922T191922Z
LAST-MODIFIED:20200922T191922Z
UID:13087-1606147200-1606150800@idss.mit.edu
SUMMARY:LIDS Seminar - Andreas Krause (ETH Zürich)
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-andreas-krause-eth-zürich
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201116T160000
DTEND;TZID=America/New_York:20201116T170000
DTSTAMP:20221001T162441
CREATED:20200922T191808Z
LAST-MODIFIED:20200922T191808Z
UID:13085-1605542400-1605546000@idss.mit.edu
SUMMARY:LIDS Seminar - Kamalika Chaudhuri (University of California San Diego)
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-kamalika-chaudhuri-university-california-san-diego
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201109T160000
DTEND;TZID=America/New_York:20201109T170000
DTSTAMP:20221001T162441
CREATED:20200922T191652Z
LAST-MODIFIED:20201021T144500Z
UID:13083-1604937600-1604941200@idss.mit.edu
SUMMARY:LIDS Seminar - Mary Wootters (Stanford)
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-mary-wootters-stanford
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201026T160000
DTEND;TZID=America/New_York:20201026T170000
DTSTAMP:20221001T162441
CREATED:20200922T191441Z
LAST-MODIFIED:20201021T144318Z
UID:13080-1603728000-1603731600@idss.mit.edu
SUMMARY:Hadamard Differential Calculus and Applications
DESCRIPTION:ABSTRACT\n\nThe Hadamard differential was introduced in 1923 by Hadamard and promoted in 1937 by Fréchet who extended it to vector spaces of functions. Infinite dimension is equivalent to the Fréchet differential introduced in 1911\, but in function spaces\, Hadamard is more general than Fréchet\, which is restricted to normed vector spaces. In 1978 Penot gave the appropriate definition of a semi-differential in the sense of Hadamard by using semi-paths with a semi-tangent. \nThe Hadamard semidifferentiable functions are probably the largest family of nondifferentiable functions that retain all the features of the classical differential calculus including the chain rule. Continuous convex and semiconvex functions and all norms are Hadamard semidifferentiable. They have a very large intersection with the family of functions studied in Nonsmooth Analysis (Clarke)\, but they are not contained into one another. Because of its geometric character\, the notion of Hadamard semidifferential readily extends to functions defined on smooth embedded differential submanifolds. It also naturally extends to metric groups that naturally occur in optimization problems with respect to the geometry to make sense of the so-called shape and topological derivatives. Applications to Danskin’s Theorem and a problem in Plasma Physics will be briefly described. \n\nBIOGRAPHY\n\nMichel C. Delfour is a professor of mathematics and statistics at the University of Montreal and is the author or coauthor of 13 books and about 200 papers. Delfour’s areas of research include shape and topological optimal design\, analysis and control of delay and distributed parameter systems\, control and stabilization of large flexible space structures\, numerical methods in differential equations and optimization\, and transfinite interpolation. He is a Fellow of SIAM\, the Canadian Mathematical Society\, the Academy of Science at the Royal Society of Canada and\, formerly\, the Guggenheim Foundation. \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-michel-delfour-université-de-montréal
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201019T160000
DTEND;TZID=America/New_York:20201019T170000
DTSTAMP:20221001T162441
CREATED:20200922T191158Z
LAST-MODIFIED:20201021T144211Z
UID:13077-1603123200-1603126800@idss.mit.edu
SUMMARY:SOAP: New Breakthroughs in Scheduling Theory
DESCRIPTION:ABSTRACT\n\nScheduling policies are at the heart of computer systems. The right scheduling policy can dramatically reduce response times\, ensure fairness\, provide class-based priority\, etc.\, without requiring additional resources. While stochastic response time analysis of different scheduling policies has been the focus of thousands of theoretical papers\, results are limited to analyzing a relatively small number of “simple” scheduling policies. In this talk\, we introduce the SOAP class of scheduling policies: Schedule Ordered by Age-based Priority. The SOAP policies include most of the scheduling policies in the literature as well as an infinite number of variants that have never been analyzed or maybe even conceived. SOAP policies include the highly intractable SERPT policy (Shortest-Expected-Remaining-Processing-Time)\, as well as the famously complex Gittins Index policy. SOAP also includes all sorts of “combination” policies\, like a policy that performs Shortest Job First on jobs with known size and Foreground-Background scheduling on jobs with unknown size\, or a policy that is allowed to preempt jobs only when they hit certain checkpoint ages. In this talk\, we present a stochastic response time analysis of all SOAP policies in the M/G/1 setting. If time permits\, we will briefly mention new SOAP-related work for the M/G/k.\n\nThis talk is based on the SOAP paper with Ziv Scully and Alan Scheller-Wolf (Sigmetrics 2018\, APS 2018 Best Student Paper Finalist). SOAP follow-up work includes papers with Isaac Grosof and Ziv Scully in Performance 2018 (Best Student Paper Award)\, Sigmetrics 2019 (Best Student Paper Award)\, Sigmetrics 2020 (Best Video Award)\, Performance 2020\, and Sigmetrics 2021.\n\nBIOGRAPHY\n\nMor Harchol-Balter is the Bruce J. Nelson Professor of Computer Science at CMU. She received her Ph.D. from U.C. Berkeley in 1996 and did her postdoc at MIT from 1996-1999\, under the NSF Mathematical Sciences Postdoctoral Fellowship. Mor is a Fellow of both the ACM and IEEE. She is a recipient of the NSF CAREER award\, many teaching awards\, and dozens of faculty research awards from companies with whom she collaborates\, particularly Google\, Microsoft\, Facebook\, IBM\, and Intel. Mor’s work focuses on designing new resource allocation policies\, including load balancing policies\, power management policies\, and scheduling policies. Mor is heavily involved in the SIGMETRICS/PERFORMANCE research community\, where she has received many best paper awards. She is also the author of a popular textbook\, “Performance Analysis and Design of Computer Systems\,” published by Cambridge University Press\, which bridges Operations Research and Computer Science. Mor is best known for her vivacious keynote talks and her many successful PhD students. \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-mor-harchol-balter-carnegie-mellon-university
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200928T110000
DTEND;TZID=America/New_York:20200928T120000
DTSTAMP:20221001T162441
CREATED:20200922T190950Z
LAST-MODIFIED:20201021T144024Z
UID:13073-1601290800-1601294400@idss.mit.edu
SUMMARY:Distributed Machine Learning over Networks
DESCRIPTION:ABSTRACT\n\nThe success of machine learning models is in part due to their capacity to train on large amounts of data. Distributed systems are the common way to process more data than one computer can store\, but they can also be used to increase the pace at which models are trained by splitting the work among many computing nodes. In this talk\, I will study the corresponding problem of minimizing a sum of functions which are respectively accessible by separate nodes in a network. New centralized and decentralized algorithms will be presented\, together with their convergence guarantees in deterministic and stochastic convex settings\, leading to optimal algorithms for this particular class of distributed optimization problems. \n\nBIOGRAPHY\n\nFrancis Bach is a researcher at Inria\, leading since 2011 the machine learning team which is part of the Computer Science department at Ecole Normale SupÃ©rieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005\, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris\, then he joined the computer vision project-team at Inria/Ecole Normale SupÃ©rieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in sparse methods\, kernel-based learning\, large-scale optimization\, computer vision and signal processing. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council\, and received the Inria young researcher prize in 2012\, the ICML test-of-time award in 2014\, as well as the Lagrange prize in continuous optimization in 2018\, and the Jean-Jacques Moreau prize in 2019. He was elected in 2020 at the French Academy of Sciences. In 2015\, he was program co-chair of the International Conference in Machine learning (ICML)\, and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research. \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/distributed-machine-learning-over-networks
LOCATION:Online
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191125T160000
DTEND;TZID=America/New_York:20191125T170000
DTSTAMP:20221001T162441
CREATED:20190920T151129Z
LAST-MODIFIED:20190920T151129Z
UID:10851-1574697600-1574701200@idss.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:20221001T162441
CREATED:20190920T151029Z
LAST-MODIFIED:20190920T151029Z
UID:10849-1574092800-1574096400@idss.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:20221001T162441
CREATED:20190920T150920Z
LAST-MODIFIED:20190920T150920Z
UID:10846-1572278400-1572282000@idss.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:20221001T162441
CREATED:20190920T150813Z
LAST-MODIFIED:20191010T121729Z
UID:10844-1571673600-1571677200@idss.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:20221001T162441
CREATED:20190920T150647Z
LAST-MODIFIED:20190920T150647Z
UID:10842-1569945600-1569949200@idss.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:20221001T162441
CREATED:20190920T150517Z
LAST-MODIFIED:20190920T150517Z
UID:10840-1569247200-1569250800@idss.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:20221001T162441
CREATED:20190920T150317Z
LAST-MODIFIED:20190920T150317Z
UID:10838-1568649600-1568653200@idss.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:20221001T162441
CREATED:20190301T172026Z
LAST-MODIFIED:20190501T142034Z
UID:8991-1557849600-1557853200@idss.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:20221001T162441
CREATED:20190301T171816Z
LAST-MODIFIED:20190501T142201Z
UID:8989-1556640000-1556643600@idss.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:20221001T162441
CREATED:20190301T171624Z
LAST-MODIFIED:20190501T142253Z
UID:8987-1556035200-1556038800@idss.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:20221001T162441
CREATED:20190301T171340Z
LAST-MODIFIED:20190501T142316Z
UID:8985-1554825600-1554829200@idss.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.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:20221001T162441
CREATED:20190301T170833Z
LAST-MODIFIED:20190501T142423Z
UID:8981-1552406400-1552410000@idss.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.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:20221001T162441
CREATED:20190301T170447Z
LAST-MODIFIED:20190501T142448Z
UID:8979-1551196800-1551200400@idss.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.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:20221001T162441
CREATED:20190301T165622Z
LAST-MODIFIED:20190501T142520Z
UID:8976-1550592000-1550595600@idss.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.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:20221001T162441
CREATED:20180810T161120Z
LAST-MODIFIED:20190501T143433Z
UID:8174-1544457600-1544461200@idss.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:20221001T162441
CREATED:20180810T160857Z
LAST-MODIFIED:20190501T143456Z
UID:8172-1543248000-1543251600@idss.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:20221001T162441
CREATED:20180810T160618Z
LAST-MODIFIED:20190501T143517Z
UID:8170-1542643200-1542646800@idss.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
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