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DTSTART:20170312T070000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20190204
DTEND;VALUE=DATE:20190205
DTSTAMP:20260408T061102
CREATED:20190117T000618Z
LAST-MODIFIED:20190117T155814Z
UID:8734-1549238400-1549324799@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:Developed by 11 MIT faculty members at IDSS\, this seven-week course is specially designed for data scientists\, business analysts\, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins Feb 4\, 2019.
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+1T2019/about?utm_medium=website&utm_source=idss&utm_campaign=ds-sp19&utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190201T110000
DTEND;TZID=America/New_York:20190201T120000
DTSTAMP:20260408T061102
CREATED:20190128T171951Z
LAST-MODIFIED:20190128T192955Z
UID:8777-1549018800-1549022400@idss-stage.mit.edu
SUMMARY:Optimization of the Sherrington-Kirkpatrick Hamiltonian
DESCRIPTION:Andrea Montanari\nProfessor\, Department of Electrical Engineering\, Department of Statistics Stanford University \nThis lecture is in conjunction with the LIDS Student Conference. \nAbstract: Let A be n × n symmetric random matrix with independent and identically distributed Gaussian entries above the diagonal. We consider the problem of maximizing xT Ax over binary vectors with ±1 entries. In the language of statistical physics\, this amounts to finding the ground state of the Sherrington-Kirkpatrick model of spin glasses. The asymptotic value of this optimization problem was characterized by Parisi via a celebrated variational principle\, subsequently proved by Talagrand. We give an algorithm that\, for any > 0\, outputs a feasible solution that is at least 1 − of the optimum value\, with probability converging to one as n goes to infinity. The algorithm’s time complexity is 0(n2). It is a message-passing algorithm\, but the specific structure of its update rules is new. As a side result\, we prove that\, at (low) non-zero temperature\, the algorithm constructs approximate solutions of the celebrated Thouless-Anderson-Palmer equations. \nBiography: \nAndrea Montanari received a Laurea degree in Physics in 1997\, and a Ph. D. in Theoretical Physics in 2001 (both from Scuola Normale Superiore in Pisa\, Italy). He has been post-doctoral fellow at Laboratoire de Physique Théorique de l’Ecole Normale Supérieure (LPTENS)\, Paris\, France\, and the Mathematical Sciences Research Institute\, Berkeley\, USA. Since 2002 he is Chargé de Recherche (with Centre National de la Recherche Scientifique\, CNRS) at LPTENS. In September 2006 he joined Stanford University as a faculty\, and since 2015 he is Full Professor in the Departments of Electrical Engineering and Statistics. \nHe was co-awarded the ACM SIGMETRICS best paper award in 2008. He received the CNRS bronze medal for theoretical physics in 2006\, the National Science Foundation CAREER award in 2008\, the Okawa Foundation Research Grant in 2013\, and the Applied Probability Society Best Publication Award in 2015. He is an Information Theory Society distinguished lecturer for 2015-2016. In 2016 he received the James L. Massey Research & Teaching Award of the Information Theory Society for young scholars. In 2018 he was an invited sectional speaker at the International Congress of Mathematicians. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/andrea-montanari/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190131
DTEND;VALUE=DATE:20190202
DTSTAMP:20260408T061102
CREATED:20180801T184336Z
LAST-MODIFIED:20180801T190825Z
UID:8109-1548892800-1549065599@idss-stage.mit.edu
SUMMARY:Laboratory for Information & Decision Systems (LIDS) Student Conference
DESCRIPTION:The annual LIDS Student Conference is a student-organized\, student-run event that provides an opportunity for grad students to present their research to peers as well as to the community at large.
URL:https://idss-stage.mit.edu/calendar/laboratory-for-information-decision-systems-lids-student-conference/
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181214T110000
DTEND;TZID=America/New_York:20181214T120000
DTSTAMP:20260408T061102
CREATED:20180621T193833Z
LAST-MODIFIED:20181204T175526Z
UID:7926-1544785200-1544788800@idss-stage.mit.edu
SUMMARY:Large girth approximate Steiner triple systems
DESCRIPTION:Abstract:  In 1973 Erdos asked whether there are n-vertex partial Steiner triple systems with arbitrary high girth and quadratically many triples. (Here girth is defined as the smallest integer g \ge 4 for which some g-element vertex-set contains at least g-2 triples.) \nWe answer this question\, by showing existence of approximate Steiner triple systems with arbitrary high girth. More concretely\, for any fixed \ell \ge 4 we show that a natural constrained random process typically produces a partial Steiner triple system with (1/6-o(1))n^2 triples and girth larger than \ell. The process iteratively adds random triples subject to the constraint that the girth remains larger than \ell. Our result is best possible up to the o(1)-term\, which is a negative power of n. \nJoint work with Tom Bohman.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-22/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181212T160000
DTEND;TZID=America/New_York:20181212T170000
DTSTAMP:20260408T061102
CREATED:20181210T230157Z
LAST-MODIFIED:20181210T230531Z
UID:8673-1544630400-1544634000@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Info Session
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Info session is hosted by a member of the IDSS faculty and an SES student\, who introduce the program and answer your questions.\nSee the flier or our website for more information.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-info-session/
LOCATION:E18-411\, 50 Ames St.\, Bldg. E18\, Room 411\, Cambridge\, MA\, 02142\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181210T160000
DTEND;TZID=America/New_York:20181210T170000
DTSTAMP:20260408T061102
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:20181207T110000
DTEND;TZID=America/New_York:20181207T120000
DTSTAMP:20260408T061102
CREATED:20180621T193538Z
LAST-MODIFIED:20181130T173240Z
UID:7924-1544180400-1544184000@idss-stage.mit.edu
SUMMARY:Reducibility and Computational Lower Bounds for Some High-dimensional Statistics Problems
DESCRIPTION:Abstract: The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data needed by all known computationally efficient algorithms far exceeds what is needed for inefficient algorithms that search over all possible structures. A line of work initiated by Berthet and Rigollet in 2013 has aimed to explain these gaps by reducing from conjecturally hard problems in computer science. However\, the delicate nature of average-case reductions has limited the applicability of this approach. In this work we introduce several new techniques to give a web of average-case reductions showing strong computational lower bounds based on the planted clique conjecture. These include tight lower bounds for Planted Independent Set\, Planted Dense Subgraph\, Biclustering\, Sparse Spiked Wigner\, Sparse PCA\, as well as for new models we introduce. Joint work with Matthew Brennan and Wasim Huleihel. \n Bio:  Guy Bresler is an assistant professor in the Department of Electrical Engineering and Computer Science at MIT\, and a member of LIDS and IDSS.\nPreviously\, he was a postdoc at MIT and before that received his PhD from the Department of EECS at UC Berkeley.\nHe seeks to obtain engineering insight into practically relevant problems by formulating and solving mathematical models. Concretely\, he wants to understand the relationship between combinatorial structure and computational tractability of high-dimensional inference in the context of graphical models and other statistical models\, recommendation systems\, and biology.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-21/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181206T090000
DTEND;TZID=America/New_York:20181206T100000
DTSTAMP:20260408T061102
CREATED:20181005T212647Z
LAST-MODIFIED:20181126T183119Z
UID:8367-1544086800-1544090400@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty\, who introduces the program and answers your questions. \nPlease register in advance. \nSES Webinar Flier.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-4/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181203T160000
DTEND;TZID=America/New_York:20181203T170000
DTSTAMP:20260408T061102
CREATED:20180712T160757Z
LAST-MODIFIED:20181206T141227Z
UID:7987-1543852800-1543856400@idss-stage.mit.edu
SUMMARY:The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility
DESCRIPTION:Abstract:\nWe construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract\, we estimate children’s earnings distributions\, incarceration rates\, and other outcomes in adulthood by parental income\, race\, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby areas: for children of parents at the 25th percentile of the income distribution\, the standard deviation of mean household income at age 35 is $5\,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First\, the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract\, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second\, we show that the observational estimates are highly predictive of neighborhoods’ causal effects\, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low income families\, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth\, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Read the whole paper here.\n \nAbout the speaker:\nRaj Chetty is the William A. Ackman Professor of Economics at Harvard University. He is also the Director of the Equality of Opportunity Project\, which uses “big data” to understand how we can give children from disadvantaged backgrounds better chances of succeeding. Chetty’s research combines empirical evidence and economic theory to help design more effective government policies. His work on topics ranging from tax policy and unemployment insurance to education and affordable housing has been widely cited in academia\, media outlets\, and Congressional testimony. \nChetty received his Ph.D. from Harvard University in 2003 and is one of the youngest tenured professors in Harvard’s history. Before joining the faculty at Harvard\, he was a professor at UC-Berkeley and Stanford University. Chetty has received numerous awards for his research\, including a MacArthur “Genius” Fellowship and the John Bates Clark medal\, given to the economist under 40 whose work is judged to have made the most significant contribution to the field.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-series/
LOCATION:32-155
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181130T110000
DTEND;TZID=America/New_York:20181130T120000
DTSTAMP:20260408T061102
CREATED:20180621T193346Z
LAST-MODIFIED:20181121T171911Z
UID:7922-1543575600-1543579200@idss-stage.mit.edu
SUMMARY:Bias Reduction and Asymptotic Efficiency in Estimation of Smooth Functionals of High-Dimensional Covariance
DESCRIPTION:Abstract:  We discuss a recent approach to bias reduction in a problem of estimation of smooth functionals of high-dimensional parameters of statistical models. In particular\, this approach has been developed in the case of estimation of functionals of covariance operator Σ : Rd → Rd of the form f(Σ)\, B based on n i.i.d. observations X1\, . . . \, Xn sampled from the normal distribution with mean zero and covariance Σ\, f : R → R being a sufficiently smooth\nfunction and B being an operator with nuclear norm bounded by a constant. This includes such problems as estimation of bilinear forms (for instance\, matrix entries in a given basis) of spectral projections of unknown covari-ance that are of importance in principal component analysis. A “bootstrap chain” bias reduction method\, based on an approximate solution of a certain integral equation (the Wishart equation) on the cone of self-adjoint positive semidefinite operators\, yields asymptotically efficient estimators of the func-tional f(Σ)\, B under proper assumptions on the growth of dimension d and smoothness of function f. In particular\, this holds under the assumption that d ≤ nα for some α ∈ (0\, 1) and that f belongs to a Besov space Bs∞\,1(R) for s > 1 . The proof of asymptotic efficiency relies on a number of probabilistic and analytic tools (operator differentiability; Gaussian concentration; properties of Wishart operators and orthogonally invariant functions on the cone of positive semidefinite operators; information-theoretic lower bounds).\n Biography:  Vladimir Koltchinskii is a professor in Mathematics at Georgia Tech. His current research is primarily in high-dimensional statistics and probability.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-20/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181129T120000
DTEND;TZID=America/New_York:20181129T133000
DTSTAMP:20260408T061102
CREATED:20181017T142737Z
LAST-MODIFIED:20181017T143418Z
UID:8487-1543492800-1543498200@idss-stage.mit.edu
SUMMARY:IDSS Science Speed Dating Event
DESCRIPTION:Join IDSS faculty\, postdocs\, and graduate students for the first IDSS Science Speed Dating Event on Thursday\, November 29. The purpose of this event is to help the participants to expand their network\, find new research partners\, and strengthen the IDSS community. The event includes lunch. \nTo register for the IDSS Science Speed Dating Event\, please sign up here. Please contact Doro Unger-Lee at dor@mit.edu with any questions you may have about this event.
URL:https://idss-stage.mit.edu/calendar/idss-science-speed-dating-event/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181127T160000
DTEND;TZID=America/New_York:20181127T170000
DTSTAMP:20260408T061102
CREATED:20181031T164820Z
LAST-MODIFIED:20181129T142536Z
UID:8542-1543334400-1543338000@idss-stage.mit.edu
SUMMARY:Censored: Distraction and Diversion Inside China's Great Firewall
DESCRIPTION:Abstract:\nAs authoritarian governments around the world develop sophisticated technologies for controlling information\, many observers have predicted that these controls would be ineffective because they are easily thwarted and evaded by savvy Internet users. In Censored\, Margaret Roberts demonstrates that even censorship that is easy to circumvent can still be enormously effective. Taking advantage of digital data harvested from the Chinese Internet and leaks from China’s Propaganda Department\, this book sheds light on how and when censorship influences the Chinese public. \nRoberts finds that much of censorship in China works not by making information impossible to access but by requiring those seeking information to spend extra time and money for access. By inconveniencing users\, censorship diverts the attention of citizens and powerfully shapes the spread of information. When Internet users notice blatant censorship\, they are willing to compensate for better access. But subtler censorship\, such as burying search results or introducing distracting information on the web\, is more effective because users are less aware of it. Roberts challenges the conventional wisdom that online censorship is undermined when it is incomplete and shows instead how censorship’s porous nature is used strategically to divide the public. \nDrawing parallels between censorship in China and the way information is manipulated in the United States and other democracies\, Roberts reveals how Internet users are susceptible to control even in the most open societies. Demonstrating how censorship travels across countries and technologies\, Censored gives an unprecedented view of how governments encroach on the media consumption of citizens. \n  \nAbout the Speaker:\nMargaret Roberts is an Associate Professor in the Department of Political Science at the University of California\, San Diego. Roberts research focuses on better measuring and understanding the political information strategies of authoritarian governments\, with a specific focus on studying censorship and propaganda in China. She has also developed widely used methods for automated content analysis in the social sciences. Roberts received her PhD in Government from Harvard University in 2014\, an M.S. in Statistics and B.A. in International Relations and Economics from Stanford in 2009. Her work has appeared in venues such as the American Political Science Review\, American Journal of Political Science\, Political Analysis\, Journal of the American Statistical Association and Science.
URL:https://idss-stage.mit.edu/calendar/censored-distraction-and-diversion-inside-chinas-great-firewall/
LOCATION:32-141\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181127T090000
DTEND;TZID=America/New_York:20181127T100000
DTSTAMP:20260408T061102
CREATED:20181005T212414Z
LAST-MODIFIED:20181126T183046Z
UID:8365-1543309200-1543312800@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty\, who introduces the program and answers your questions. \nPlease register in advance. \nSES Webinar Flier.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-3/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181126T160000
DTEND;TZID=America/New_York:20181126T170000
DTSTAMP:20260408T061102
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:20260408T061102
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:20181116T110000
DTEND;TZID=America/New_York:20181116T120000
DTSTAMP:20260408T061102
CREATED:20180621T193041Z
LAST-MODIFIED:20181108T190207Z
UID:7920-1542366000-1542369600@idss-stage.mit.edu
SUMMARY:Model-X knockoffs for controlled variable selection in high dimensional nonlinear regression
DESCRIPTION:Abstract:  Many contemporary large-scale applications\, from genomics to advertising\, involve linking a response of interest to a large set of potential explanatory variables in a nonlinear fashion\, such as when the response is binary. Although this modeling problem has been extensively studied\, it remains unclear how to effectively select important variables while controlling the fraction of false discoveries\, even in high-dimensional logistic regression\, not to mention general high-dimensional nonlinear models. To address such a practical problem\, we propose a new framework of model-X knockoffs\, which reads from a different perspective the knockoff procedure (Barber and Candès\, 2015) originally designed for controlling the false discovery rate in low-dimensional linear models. Model-X knockoffs can deal with arbitrary (and unknown) conditional models and any dimensions\, including when the number of explanatory variables p exceeds the sample size n. Our approach requires the design matrix be random (independent and identically distributed rows) with a known distribution for the explanatory variables\, although we show preliminary evidence that our procedure is robust to unknown/estimated distributions. As we require no knowledge/assumptions about the conditional distribution of the response\, we effectively shift the burden of knowledge from the response to the explanatory variables\, in contrast to the canonical model-based approach which assumes a parametric model for the response but very little about the explanatory variables. To our knowledge\, no other procedure solves the controlled variable selection problem in such generality\, but in the restricted settings where competitors exist\, we demonstrate the superior power of knockoffs through simulations. We also apply our procedure to data from a case-control study of Crohn’s disease in the United Kingdom\, making twice as many discoveries as the original analysis of the same data. \n Biography:  Lucas Janson is an Assistant Professor in the Department of Statistics at Harvard University\, where he works on high-dimensional inference\, autonomous robotic motion planning\, and statistical machine learning. Prior to Harvard\, he was a PhD student in Statistics at Stanford University advised by Professor Emmanuel Candès.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-19/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181113T080000
DTEND;TZID=America/New_York:20181113T170000
DTSTAMP:20260408T061102
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:20181109T110000
DTEND;TZID=America/New_York:20181109T120000
DTSTAMP:20260408T061102
CREATED:20180621T192716Z
LAST-MODIFIED:20181107T151946Z
UID:7918-1541761200-1541764800@idss-stage.mit.edu
SUMMARY:Optimal hypothesis testing for stochastic block models with growing degrees
DESCRIPTION:Abstract:  In this talk\, we discuss optimal hypothesis testing for distinguishing a stochastic block model from an Erdos–Renyi random graph when the average degree grows to infinity with the graph size. We show that linear spectral statistics based on Chebyshev polynomials of the adjacency matrix can approximate signed cycles of growing lengths when the graph is sufficiently dense. The signed cycles have been shown by Banerjee (2018) to determine the likelihood ratio statistic asymptotically. In this way one achieves sharp asymptotic optimal power of the testing problem within polynomial time complexity. Time permitting\, we will also discuss how linear spectral statistics of a weighted non-backtracking matrix can be used to approximate the likelihood ratio. The talk is based on joint work with Debapratim Banerjee. \n Biography:  Dr.Zongming Ma is an Associate Professor of Statistics of the Wharton School at the University of Pennsylvania. He received his PhD in Statistics from Stanford University in 2010 and has since then been on the faculty of the Wharton Statistics Department. Dr.Ma’s research interests include high-dimensional statistical inference\, non-parametric statistics\, network data analysis\, and their applications in biomedical data analysis. He is a recipient of a Sloan Research Fellowship and an NSF CAREER Award.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-18/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181105T160000
DTEND;TZID=America/New_York:20181105T170000
DTSTAMP:20260408T061102
CREATED:20180712T160448Z
LAST-MODIFIED:20181121T141808Z
UID:7982-1541433600-1541437200@idss-stage.mit.edu
SUMMARY:The Regression Discontinuity Design: Methods and Applications
DESCRIPTION:Abstract:\nThe Regression Discontinuity (RD) design is one of the most widely used non-experimental strategies for the study of treatment effects in the social\, behavioral\, biomedical\, and statistical sciences. In this design\, units are assigned a score and a treatment is offered if the value of that score exceeds a known threshold—and withheld otherwise. In this talk\, I will discuss the assumptions under which the RD design can be used to learn about treatment effects\, and how to make valid inferences about them based on modern theoretical results in nonparametrics that emphasize the importance of extrapolation of regression functions and misspecification biases near the RD cutoff. I will also discuss the common approach of augmenting nonparametric regression models using predetermined covariates in RD setups\, and how this affects nonparametric identification of as well as statistical inference about the RD parameter. If time permits\, I will also discuss a more general version of the RD design based on multiple cutoffs\, which expands the generalizability of the standard RD design by allowing researchers to test richer hypotheses regarding the heterogeneity of the treatment effect and\, under additional assumptions\, to extrapolate the treatment effect to score values far from the cutoff. \n \nRocío Titiunik is the James Orin Murfin Professor of Political Science at the University of Michigan. She specializes in quantitative methodology for the social sciences\, with emphasis on quasi-experimental methods for causal inference and political methodology. Her research interests lie at the intersection of political science\, political economy\, and applied statistics\, particularly on the development and application of quantitative methods to the study of political institutions. Her recent methodological research includes the development of statistical methods for the analysis and interpretation of treatment effects and program evaluation\, with emphasis on regression discontinuity (RD) designs. Her recent substantive research centers on democratic accountability and the role of party systems in developing democracies. Rocio’s work appears in various journals in the social sciences and statistics\, including the American Political Science Review\, the American Journal of Political Science\, the Journal of Politics\, Econometrica\, the Journal of the American Statistical Association\, and the Journal of the Royal Statistical Society. In 2016\, she received the Emerging Scholar Award from the Society for Political Methodology\, which honors a young researcher who is making notable contributions to the field of political methodology. She is a member of the leadership team of the Empirical Implications of Theoretical Models (EITM) Summer Institute\, member-at-large of the Society for Poltical Methodology\, and member of Evidence in Governance and Politics (EGAP). She is also an Associate Editor for Political Science Research and Methods and the American Journal of Political Science\, and has served in the advisory panel for the Methodology\, Measurement\, and Statistics program of the National Science Foundation.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-rocio-titiunik/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181102T110000
DTEND;TZID=America/New_York:20181102T120000
DTSTAMP:20260408T061102
CREATED:20180621T192450Z
LAST-MODIFIED:20181029T150448Z
UID:7916-1541156400-1541160000@idss-stage.mit.edu
SUMMARY:Joint estimation of parameters in Ising Model
DESCRIPTION:Abstract:  Inference in the framework of Ising models has received significant attention in Statistics and Machine Learning in recent years. In this talk we study joint estimation of the inverse temperature parameter β\, and the magnetization parameter B\, given one realization from the Ising model\, under the assumption that the underlying graph of the Ising model is completely specified. We show that if the graph is either irregular or sparse\, then both the parameters can be estimated at rate n−1/2  using Besag’s pseudo-likelihood. Conversely\, if the underlying graph is dense and regular\, we show that no consistent estimates exist for (β\, B).\nThis is joint work with Promit Ghosal from Columbia University. \n Biography:  Sumit is currently an Assistant Professor in the Statistics Department at Columbia. Prior to this\, he received his PhD in Statistics at Stanford\, under the guidance of Persi Diaconis.\nHis research interests lie in the intersection of Theoretical Statistics and Applied Probability. In Statistics\, his main focus is developing inferential procedures on probability distributions on combinatorial spaces\, such as permutations\, graphs\, and spin configurations. On the probability side\, his main focus is studying persistence of stochastic processes\, and graph coloring problems.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-17/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181030T170000
DTEND;TZID=America/New_York:20181030T180000
DTSTAMP:20260408T061102
CREATED:20181005T212240Z
LAST-MODIFIED:20181005T213127Z
UID:8362-1540918800-1540922400@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty\, who introduces the program and answers your questions. \nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-2/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181029T160000
DTEND;TZID=America/New_York:20181029T170000
DTSTAMP:20260408T061102
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:20181026T110000
DTEND;TZID=America/New_York:20181026T120000
DTSTAMP:20260408T061102
CREATED:20180621T192221Z
LAST-MODIFIED:20180626T142057Z
UID:7914-1540551600-1540555200@idss-stage.mit.edu
SUMMARY:Alan Frieze
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-16/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181025T090000
DTEND;TZID=America/New_York:20181025T170000
DTSTAMP:20260408T061102
CREATED:20180717T192722Z
LAST-MODIFIED:20180906T134254Z
UID:8046-1540458000-1540486800@idss-stage.mit.edu
SUMMARY:IDSS Retail Conference 2018
DESCRIPTION:The retail sector is undergoing fundamental changes as a result of data analytics and computer science. The IDSS Retail Conference 2018 will bring together experts from academia and industry to discuss a variety of topics addressing “Data Driven Disruption” in the Retail Sector.
URL:https://idssretail2018.mit.edu/
LOCATION:Microsoft New England Research and Development Center\, One Memorial Drive\, Cambridge\, 02139\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181022T160000
DTEND;TZID=America/New_York:20181022T170000
DTSTAMP:20260408T061102
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:20181019T110000
DTEND;TZID=America/New_York:20181019T120000
DTSTAMP:20260408T061102
CREATED:20180621T191820Z
LAST-MODIFIED:20181010T202034Z
UID:7912-1539946800-1539950400@idss-stage.mit.edu
SUMMARY:Algorithmic thresholds for tensor principle component analysis
DESCRIPTION:Abstract:  Consider the problem of recovering a rank 1 tensor of order k that has been subject to Gaussian noise. The log-likelihood for this problem is highly non-convex. It is information theoretically possible to recover the tensor with a finite number of samples via maximum likelihood estimation\, however\, it is expected that one needs a polynomially diverging number of samples to efficiently recover it. What is the cause of this large statistical–to–algorithmic gap? To study this question\, we investigate the thresholds for efficient recovery for a simple family of algorithms\, Langevin dynamics and gradient descent. We view this problem as a member of a broader class of problems which correspond to recovering a signal from a non-linear observation that has been perturbed by isotropic Gaussian noise. We propose a mechanism for success/failure of recovery of such algorithms in terms of the strength of the signal on the high entropy region of the initialization. Joint work with G. Ben Arous (NYU) and R. Gheissari (NYU). \n Biography:  Aukosh Jagannath is a Benjamin Pierce Fellow and NSF Postdoctoral fellow at Harvard University with undergraduate and graduate degree from NYU. He works in probability at the interface of statistical physics\, data science\, combinatorics\, and statistics.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-15/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181017T120000
DTEND;TZID=America/New_York:20181017T130000
DTSTAMP:20260408T061102
CREATED:20181005T211137Z
LAST-MODIFIED:20181005T213025Z
UID:8360-1539777600-1539781200@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty\, who introduces the program and answers your questions. \nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181016T160000
DTEND;TZID=America/New_York:20181016T170000
DTSTAMP:20260408T061102
CREATED:20180712T160153Z
LAST-MODIFIED:20181024T145321Z
UID:7973-1539705600-1539709200@idss-stage.mit.edu
SUMMARY:Can machine learning survive the artificial intelligence revolution?
DESCRIPTION:  \nAbstract:\nData and algorithms are ubiquitous in all scientific\, industrial and personal domains. Data now come in multiple forms (text\, image\, video\, web\, sensors\, etc.)\, are massive\, and require more and more complex processing beyond their mere indexation or the computation of simple statistics\, such as recognizing objects in images or translating texts. For all of these tasks\, commonly referred to as artificial intelligence (AI)\, significant recent progress has allowed algorithms to reach performances that were deemed unreachable a few years ago and that make these algorithms useful to everyone.\nMany scientific fields contribute to AI\, but most of the visible progress come from machine learning and tightly connected fields such as computer vision and natural language processing. Indeed\, many of the recent advances are due to the availability of massive data to learn from\, large computing infrastructures and new machine learning models (in particular deep neural networks).\nBeyond the well publicized visibility of some advances\, machine learning has always been a field characterized by the constant exchanges between theory and practice\, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Is this still possible? \nIn this talk\, Francis Bach will present recent illustrating machine learning successes and propose some answers to the question above. \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 Supérieure. 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 Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in graphical models\, sparse methods\, kernel-based learning\, large-scale convex 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. 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.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-francis-bach/
LOCATION:32-141\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181015T160000
DTEND;TZID=America/New_York:20181015T170000
DTSTAMP:20260408T061102
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;VALUE=DATE:20181015
DTEND;VALUE=DATE:20181016
DTSTAMP:20260408T061102
CREATED:20180802T010632Z
LAST-MODIFIED:20180802T010844Z
UID:8118-1539561600-1539647999@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+4T2018/about?utm_medium=website&utm_source=idss&utm_campaign=ds-fl18-sept&utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
END:VCALENDAR