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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20260408T080740
CREATED:20190204T181505Z
LAST-MODIFIED:20190204T181505Z
UID:8816-1552042800-1552046400@idss-stage.mit.edu
SUMMARY:Univariate total variation denoising\, trend filtering and multivariate Hardy-Krause variation denoising
DESCRIPTION:Abstract: \nTotal variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted function estimation. The results are based on joint work with Sabyasachi Chatterjee\, Billy Fang\, Donovan Lieu and Bodhisattva Sen.  \n Biography: \nAditya Guntuboyina is currently an Associate Professor at the Department of Statistics\, UC Berkeley. He has been at Berkeley since 2012 after finishing his PhD in Statistics from Yale University and a postdoctoral position at the Wharton Statistics Department in the University of Pennsylvania. His research interests include nonparametric and high-dimensional statistics\, shape constrained statistical estimation\, empirical processes and statistical information theory. His research is currently supported by an NSF CAREER award.
URL:https://stat.mit.edu/calendar/univariate-total-variation-denoising-trend-filtering-multivariate-hardy-krause-variation-denoising-adityaguntuboyina/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190305T160000
DTEND;TZID=America/New_York:20190305T170000
DTSTAMP:20260408T080740
CREATED:20190129T144952Z
LAST-MODIFIED:20190312T132639Z
UID:8791-1551801600-1551805200@idss-stage.mit.edu
SUMMARY:A Theory for Representation Learning via Contrastive Objectives
DESCRIPTION:Abstract:\nRepresentation learning seeks to represent complicated data (images\, text etc.) using a vector embedding\, which can then be used to solve complicated new classification tasks using simple methods like a linear classifier. Learning such embeddings is an important type of unsupervised learning (learning from unlabeled data) today. Several recent methods leverage pairs of “semantically similar” data points (eg sentences occuring next to each other in a text corpus). We call such methods contrastive learning (another term would be “like word2vec”) and propose a theoretical framework for analysing them. The challenge for theory here is that the training objective seems to have little to do with the downstream task. Our framework bridges this challenge and can show provable guarantees on the performance of the learnt representation on downstream classification tasks. I’ll show experiments supporting the theory.\nThe talk will be self-contained.\n(Joint work with Hrishikesh Khandeparkar\, Mikhail Khodak\, Orestis Plevrakis\, and Nikunj Saunshi.) \nAbout the Speaker:\nSanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University and Visiting Professor in Mathematics at the Institute for Advanced Study. He works on theoretical computer science and theoretical machine learning. He has received the Packard Fellowship (1997)\, Simons Investigator Award (2012)\, Gödel Prize (2001 and 2010)\, ACM Prize in Computing (formerly the ACM-Infosys Foundation Award in the Computing Sciences) (2012)\, and the Fulkerson Prize in Discrete Math (2012). He is a fellow of the American Academy of Arts and Sciences and member of the National Academy of Science.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-march/
LOCATION:32-155\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190304T080000
DTEND;TZID=America/New_York:20190304T170000
DTSTAMP:20260408T080740
CREATED:20180717T190526Z
LAST-MODIFIED:20191230T170939Z
UID:8040-1551686400-1551718800@idss-stage.mit.edu
SUMMARY:Women in Data Science (WiDS) – Cambridge\, MA
DESCRIPTION:This one-day technical conference brings together local academic leaders\,  industrial professionals and students to hear about the latest data science-related research in a number of domains\, to learn how leading-edge companies are leveraging data science for success\, and to connect with potential mentors\, collaborators\, and others in the field. \nWatch WiDS Cambridge on YouTube.
URL:https://idss-stage.mit.edu/calendar/women-in-data-science-wids-cambridge-ma-2/
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190301T110000
DTEND;TZID=America/New_York:20190301T120000
DTSTAMP:20260408T080740
CREATED:20190204T180630Z
LAST-MODIFIED:20190204T181211Z
UID:8814-1551438000-1551441600@idss-stage.mit.edu
SUMMARY:Why Aren’t Network Statistics Accompanied By Uncertainty Statements?
DESCRIPTION:Abstract: \nOver 500K scientific articles have been published since 1999 with the word “network” in the title. And the vast majority of these report network summary statistics of one type or another.  However\, these numbers are rarely accompanied by any quantification of uncertainty. Yet any error inherent in the measurements underlying the construction of the network\, or in the network construction procedure itself\, necessarily must propagate to any summary statistics reported. Perhaps surprisingly\, there is little in the way of formal statistical methodology for this problem.  I summarize results from our recent work\, for the case of estimating the density of low-order subgraphs. Under a simple model of network error\, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed. We then develop method-of-moment estimators of subgraph density and error rates for the case where a minimal number of network replicates are available (i.e.\, just 2 or 3). These estimators are shown to be asymptotically normal as the number of vertices increases to infinity. We also provide confidence intervals for quantifying the uncertainty in these estimates\, implemented through a novel bootstrap algorithm. We illustrate the use of our estimators in the context of gene coexpression networks — the correction for measurement error is found to have potentially substantial impact on standard summary statistics.  This is joint work with Qiwei Yao and Jinyuan Chang. \n Biography: \nEric Kolaczyk is a Professor of Statistics and Director of the Program in Statistics in the Department of Mathematics & Statistics at Boston University.  He is also a university Data Science Faculty Fellow\, and affiliated with the Division of Systems Engineering and the Programs in Bioinformatics and in Computational Neuroscience.   His current research interests revolve mainly around the statistical analysis of network-indexed data\, including both theory/methods development and collaborative research.  He has published several books on the topic of network analysis.  As an associate editor\, he has served on the boards of JASA and JRSS-B in statistics\, IEEE IP and TNSE in engineering\, and SIMODS in mathematics.  Currently he is the co-chair of the NAS Roundtable on Data Science Education.  He is an elected fellow of the AAAS\, ASA\, and IMS\, an elected senior member of the IEEE\, and an elected member of the ISI.
URL:https://stat.mit.edu/calendar/arent-network-statistics-accompanied-uncertainty-statements-erickolaczyk/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190226T160000
DTEND;TZID=America/New_York:20190226T170000
DTSTAMP:20260408T080740
CREATED:20190301T170447Z
LAST-MODIFIED:20190501T142448Z
UID:8979-1551196800-1551200400@idss-stage.mit.edu
SUMMARY:Coded Computing: A Transformative Framework for Resilient\, Secure\, and Private Distributed Learning
DESCRIPTION:This talk introduces “Coded Computing”\, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing and machine learning\, such as resiliency to stragglers and bandwidth bottleneck. Furthermore\, coded computing can enable (information-theoretically) secure and private learning over untrusted workers that is gaining increasing importance in various application domains. In particular\, we present CodedPrivateML for distributed learning\, which keeps both the data and the model private while allowing efficient parallelization of training across untrusted distributed workers. We demonstrate that CodedPrivateML can provide an order of magnitude speedup (up to ~30x) over the cryptographic approaches that rely on secure multiparty computing. \nBio: Salman Avestimehr is a Professor of Electrical Engineering and co-director of Communication Sciences Institute at the University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science\, both from the University of California\, Berkeley. Prior to that\, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003.  His research interests include information theory and coding\, distributed computing\, and machine learning. Dr. Avestimehr has received a number of awards\, including a Communications Society and Information Theory Society Joint Paper Award\, the Presidential Early Career Award for Scientists and Engineers (PECASE)\, a Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research\, a National Science Foundation CAREER award\, and several best paper awards. He is currently an Associate Editor for the IEEE Transactions on Information Theory and a General Co-Chair of the 2020 International Symposium on Information Theory (ISIT). \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/coded-computing-a-transformative-framework-for-resilient-secure-and-private-distributed-learning/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190222T110000
DTEND;TZID=America/New_York:20190222T120000
DTSTAMP:20260408T080740
CREATED:20190204T175935Z
LAST-MODIFIED:20190213T164650Z
UID:8812-1550833200-1550836800@idss-stage.mit.edu
SUMMARY:Capacity lower bound for the Ising perceptron
DESCRIPTION:Abstract: \nThe perceptron is a toy model of a simple neural network that stores a collection of given patterns. Its analysis reduces to a simple problem in high-dimensional geometry\, namely\, understanding the intersection of the cube (or sphere) with a collection of random half-spaces. Despite the simplicity of this model\, its high-dimensional asymptotics are not well understood. I will describe what is known and present recent results. \nThis is joint work with Jian Ding. \n Biography: \nNike Sun is a faculty member in the MIT mathematics department.
URL:https://stat.mit.edu/calendar/capacity-lower-bound-ising-perceptron-nikesun/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190219T160000
DTEND;TZID=America/New_York:20190219T170000
DTSTAMP:20260408T080740
CREATED:20190301T165622Z
LAST-MODIFIED:20190501T142520Z
UID:8976-1550592000-1550595600@idss-stage.mit.edu
SUMMARY:Safeguarding Privacy in Dynamic Decision-Making Problems
DESCRIPTION:The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for her goal\, while simultaneously ensuring these actions do not inadvertently reveal her private information\, even when observed and analyzed by a powerful adversary? We will examine two well-known decision problems (path planning and online learning)\, and in both cases establish sharp\, information-theoretic complexity vs. privacy tradeoff. As a by-product\, our analysis also leads to simple yet provably efficient algorithms for both the decision maker and eavesdropping adversary. Based in part on joint work with John N. Tsitsiklis and Zhi Xu (MIT). \nBio: Kuang Xu was born in Suzhou\, China. He is an Assistant Professor of Operations\, Information and Technology at the Stanford Graduate School of Business\, Stanford University. He received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign\, Urbana\, Illinois\, USA\, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology\, Cambridge\, Massachusetts\, USA. His research interests lie in the fields of applied probability theory\, optimization\, and operations research\, seeking to understand fundamental properties and design principles of large-scale stochastic systems\, with applications in queueing networks\, healthcare\, privacy\, and statistical learning theory. He has received several awards including a First Place in INFORMS George E. Nicholson Student Paper Competition\, a Best Paper Award\, as well as a Kenneth C. Sevcik Outstanding Student Paper Award from ACM SIGMETRICS. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/kuang-xu/
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190215T110000
DTEND;TZID=America/New_York:20190215T120000
DTSTAMP:20260408T080740
CREATED:20190204T172955Z
LAST-MODIFIED:20190204T173354Z
UID:8809-1550228400-1550232000@idss-stage.mit.edu
SUMMARY:TAP free energy\, spin glasses\, and variational inference
DESCRIPTION:Abstract: \nWe consider the Sherrington-Kirkpatrick model of spin glasses with ferromagnetically biased couplings. For a specific choice of the couplings mean\, the resulting Gibbs measure is equivalent to the Bayesian posterior for a high-dimensional estimation problem known as “Z2 synchronization”. Statistical physics suggests to compute the expectation with respect to this Gibbs measure (the posterior mean in the synchronization problem)\, by minimizing the so-called Thouless-Anderson-Palmer (TAP) free energy\, instead of the mean field (MF) free energy. We prove that this identification is correct\, provided the ferromagnetic bias is larger than a constant (i.e. the noise level is small enough in synchronization). Namely\, we prove that the scaled l_2 distance between any low energy local minimizers of the TAP free energy and the mean of the Gibbs measure vanishes in the large size limit. Our proof technique is based on upper bounding the expected number of critical points of the TAP free energy using the Kac-Rice formula. \nThis is joint work with Song Mei and Andrea Montanari. \n Biography: \nZhou Fan is an Assistant Professor in the Department of Statistics and Data Science at Yale University. His research interests include random matrix theory\, high dimensional and multivariate statistics\, inference in random graphs and networks\, discrete algorithms\, and applications in genetics and computational biology. Zhou received his Ph.D. in Statistics at Stanford University\, working with Iain M. Johnstone and Andrea Montanari. Prior to this\, Zhou developed statistical and software tools for molecular dynamics simulations at D. E. Shaw Research.
URL:https://stat.mit.edu/calendar/tap-free-energy-spin-glasses-variational-inference/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190208T110000
DTEND;TZID=America/New_York:20190208T120000
DTSTAMP:20260408T080740
CREATED:20190128T195456Z
LAST-MODIFIED:20190128T195657Z
UID:8782-1549623600-1549627200@idss-stage.mit.edu
SUMMARY:Medical Image Imputation
DESCRIPTION:Abstract: \nWe present an algorithm for creating high resolution anatomically\nplausible images that are consistent with acquired clinical brain MRI\nscans with large inter-slice spacing. Although large databases of\nclinical images contain a wealth of information\, medical acquisition\nconstraints result in sparse scans that miss much of the\nanatomy. These characteristics often render computational analysis\nimpractical as standard processing algorithms tend to fail when\napplied to such images. Our goal is to enable application of existing\nalgorithms that were originally developed for high resolution research\nscans to severely undersampled images. We illustrate the applications\nof the method in the context of neurodegeneration and white matter\ndisease studies in stroke patients. \nBiography:\nPolina Golland is a Henry Ellis Warren (1894) professor of Electrical\nEngineering and Computer Science at MIT and a principal investigator\nin the MIT Computer Science and Artificial Intelligence Laboratory\n(CSAIL). She received her PhD in 2001 from MIT and her Bachelor and\nMasters degrees in 1993 and 1995 from Technion\, Israel. Polina’s\nprimary research interest is in developing novel techniques for\nmedical image analysis and understanding. With her students\, Polina\nhas demonstrated novel approaches to image segmentation\, shape\nanalysis\, functional image analysis and population studies. She has\nserved as an associate editor of the IEEE Transactions on Medical\nImaging and of the IEEE Transactions on Pattern Analysis. Polina is\ncurrently on the editorial board of the Journal of Medical Image\nAnalysis. She is a Fellow of the International Society for Medical\nImage Computing and Computer Assisted Interventions. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/medical-image-imputation/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190205T160000
DTEND;TZID=America/New_York:20190205T170000
DTSTAMP:20260408T080740
CREATED:20190116T163023Z
LAST-MODIFIED:20190206T203341Z
UID:8749-1549382400-1549386000@idss-stage.mit.edu
SUMMARY:Collective Decision Making: Theory and Experiments
DESCRIPTION:Abstract:\nRanging from jury decisions to political elections\, situations in which groups of individuals determine a collective outcome are ubiquitous. There are two important observations that pertain to almost all collective processes observed in reality. First\, decisions are commonly preceded by some form of communication among individual decision makers\, such as jury deliberations\, or election polls. Second\, even when looking at a particular context\, say U.S. civil jurisdiction\, there is great variance in the type of institutions that are employed to aggregate private information or preferences into group decisions. In this talk\, I will present some theoretical models and experimental results that provide insight into how groups aggregate information and opinions\, and the sorts of instruments that might be beneficial for improving collective outcomes in various settings. \nAbout the speaker:\nLeeat Yariv is the Uwe E. Reinhardt Professor of Economics at Princeton University. She is also the director of the Princeton Experimental Laboratory for the Social Sciences (PExL)\, which provides a platform for experimental research in the social sciences. Yariv’s research combines experimental and empirical evidence together with economic theory to study how individuals connect with one another and how they make decisions together. Her research has touched upon a wide range of topics within the areas of social networks\, political economy\, and market design. Yariv received a B.Sc. in Mathematics\, a B.Sc. in Physics\, and an M.Sc. in Mathematics from Tel-Aviv University. She received an M.A. and a Ph.D. in Economics from Harvard University. Prior to joining Princeton University\, she was a professor at UCLA and Caltech. Yariv is a fellow of the Econometric Society and of the Society for the Advancement of Economic Theory. She has served on multiple journal editorial boards\, including those of Econometrica\, American Economic Review\, and Journal of Economic Literature.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-leeat-yariv/
LOCATION:32-155\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190204
DTEND;VALUE=DATE:20190205
DTSTAMP:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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:20260408T080740
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
END:VCALENDAR