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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220202T060000
DTEND;TZID=America/New_York:20220202T180000
DTSTAMP:20260423T064727
CREATED:20220124T123532Z
LAST-MODIFIED:20220124T123532Z
UID:11803-1643781600-1643824800@idss-stage.mit.edu
SUMMARY:Groundhog Day
DESCRIPTION:How much wood would a woodchuck chuck if a woodchuck could chuck wood?
URL:https://idss-stage.mit.edu/calendar/groundhog-day/
LOCATION:One Main Street\, Cambridge\, MA\, United States
CATEGORIES:Conferences and Workshops,Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200508T110000
DTEND;TZID=America/New_York:20200508T120000
DTSTAMP:20260423T064727
CREATED:20200108T203459Z
LAST-MODIFIED:20200108T205206Z
UID:11560-1588935600-1588939200@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/ben-arous2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200505T160000
DTEND;TZID=America/New_York:20200505T170000
DTSTAMP:20260423T064727
CREATED:20200124T155644Z
LAST-MODIFIED:20200205T182920Z
UID:11484-1588694400-1588698000@idss-stage.mit.edu
SUMMARY:Michael Kearns - Professor and National Center Chair\, Department of Computer and Information Science\, University of Pennsylvania
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-michael-kearns-upenn/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200501T110000
DTEND;TZID=America/New_York:20200501T120000
DTSTAMP:20260423T064727
CREATED:20200108T203919Z
LAST-MODIFIED:20200121T195044Z
UID:11563-1588330800-1588334400@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/tbd-10/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200424T110000
DTEND;TZID=America/New_York:20200424T120000
DTSTAMP:20260423T064727
CREATED:20200108T202325Z
LAST-MODIFIED:20200109T142402Z
UID:11557-1587726000-1587729600@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/wellner2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200417T110000
DTEND;TZID=America/New_York:20200417T120000
DTSTAMP:20260423T064727
CREATED:20200108T201326Z
LAST-MODIFIED:20200108T201422Z
UID:11555-1587121200-1587124800@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/arias-castro2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200413T160000
DTEND;TZID=America/New_York:20200413T170000
DTSTAMP:20260423T064727
CREATED:20200129T182413Z
LAST-MODIFIED:20200205T183630Z
UID:11662-1586793600-1586797200@idss-stage.mit.edu
SUMMARY:David Blei - Professor of Computer Science and Statistics\, Columbia University
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/david-blei-columbia-university/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200410T110000
DTEND;TZID=America/New_York:20200410T120000
DTSTAMP:20260423T064727
CREATED:20200108T200821Z
LAST-MODIFIED:20200108T204923Z
UID:11553-1586516400-1586520000@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/rinaldo2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200407T160000
DTEND;TZID=America/New_York:20200407T170000
DTSTAMP:20260423T064727
CREATED:20191220T134359Z
LAST-MODIFIED:20200205T184008Z
UID:11488-1586275200-1586278800@idss-stage.mit.edu
SUMMARY:Guido Imbens - The Applied Econometrics Professor and Professor of Economics\, Graduate School of Business\, Stanford University
DESCRIPTION:About the author: Prof. Guido Imbens’ primary field of interest is Econometrics. Research topics in which he is interested include: causality\, program evaluation\, identification\, Bayesian methods\, semi-parametric methods\, instrumental variables. Guido Imbens does research in econometrics and statistics. His research focuses on developing methods for drawing causal inferences in observational studies\, using matching\, instrumental variables\, and regression discontinuity designs. Guido Imbens is Professor of Economics at the Stanford Graduate School of Business and the department of Economics. After graduating from Brown University Guido taught at Harvard University\, UCLA\, and UC Berkeley. He holds an honorary degree from the University of St Gallen. Professor Imbens joined the GSB in 2012 where he specializes in econometrics\, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-series-guido-imbens-stanford-university/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200403T090000
DTEND;TZID=America/New_York:20200403T170000
DTSTAMP:20260423T064727
CREATED:20191024T133047Z
LAST-MODIFIED:20200107T142422Z
UID:11049-1585904400-1585933200@idss-stage.mit.edu
SUMMARY:SDSCon 2020
DESCRIPTION:SDSCon 2020 is the fourth annual celebration of the statistics and data science community at MIT and beyond\, organized by MIT’s Statistics and Data Science Center (SDSC). Please see the conference website for registration information.
URL:https://sdscon.mit.edu
LOCATION:MIT Media Lab (E14-674)\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200320T110000
DTEND;TZID=America/New_York:20200320T120000
DTSTAMP:20260423T064727
CREATED:20200108T192919Z
LAST-MODIFIED:20200108T192919Z
UID:11551-1584702000-1584705600@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/finucane2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200313T110000
DTEND;TZID=America/New_York:20200313T120000
DTSTAMP:20260423T064727
CREATED:20200108T192614Z
LAST-MODIFIED:20200108T192614Z
UID:11549-1584097200-1584100800@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/spielman2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200306T110000
DTEND;TZID=America/New_York:20200306T120000
DTSTAMP:20260423T064727
CREATED:20200108T190607Z
LAST-MODIFIED:20200108T204744Z
UID:11547-1583492400-1583496000@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/gunasekar2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200306T080000
DTEND;TZID=America/New_York:20200306T170000
DTSTAMP:20260423T064727
CREATED:20191230T165049Z
LAST-MODIFIED:20191230T180114Z
UID:11505-1583481600-1583514000@idss-stage.mit.edu
SUMMARY:Women in Data Science (WiDS) – Cambridge\, MA
DESCRIPTION:This one-day technical conference will feature an all-female line up of speakers from academia and industry to talk 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. \nConference Website: https://www.widscambridge.org/
URL:https://idss-stage.mit.edu/calendar/women-in-data-science-wids-cambridge-ma-3/
LOCATION:Microsoft NERD Center\, Cambridge\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200303T160000
DTEND;TZID=America/New_York:20200303T170000
DTSTAMP:20260423T064727
CREATED:20200124T155554Z
LAST-MODIFIED:20200205T184442Z
UID:11607-1583251200-1583254800@idss-stage.mit.edu
SUMMARY:Rohini Pande - Henry J. Heinz II Professor of Economics and Director\, Economic Growth Center (Yale University)
DESCRIPTION:About the speaker: Rohini Pande is the Henry J. Heinz II Professor of Economics and Director of the Economic Growth Center\, Yale University. She is a co-editor of American Economic Review: Insights. \nPande’s research is largely focused on how formal and informal institutions shape power relationships and patterns of economic and political advantage in society\, particularly in developing countries. She is interested the role of public policy in providing the poor and disadvantaged political and economic power\, and how notions of economic justice and human rights can help justify and enable such change. Her most recent work focuses on testing innovative ways to make the state more accountable to its citizens\, such as strengthening women’s economic and political opportunities\, ensuring that environmental regulations reduce harmful emissions\, and providing citizens effective means to voice their demand for state services. In 2018\, Pande received the Carolyn Bell Shaw Award from the American Economic Association for promoting the success of women in the economics profession. She is the co-chair of the Political Economy and Government Group at Jameel Poverty Action Lab (J-PAL)\, a Board member of Bureau of Research on Economic Development (BREAD) and a former co-editor of The Review of Economics and Statistics. Before coming to Yale\, Pande was the Rafik Harriri Professor of International Political Economy at Harvard Kennedy School\, where she co-founded Evidence for Policy Design. \nPande received a PhD in economics from London School of Economics\, a BA/MA in Philosophy\, Politics and Economics from Oxford University and a BA in Economics from Delhi University. \n 
URL:https://idss-stage.mit.edu/calendar/rohini-pande-yale-university/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200228T110000
DTEND;TZID=America/New_York:20200228T120000
DTSTAMP:20260423T064727
CREATED:20200108T185358Z
LAST-MODIFIED:20200108T203703Z
UID:11545-1582887600-1582891200@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/ramanan2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200221T110000
DTEND;TZID=America/New_York:20200221T120000
DTSTAMP:20260423T064727
CREATED:20200108T155803Z
LAST-MODIFIED:20200108T155803Z
UID:11536-1582282800-1582286400@idss-stage.mit.edu
SUMMARY:TBD
DESCRIPTION:TBD
URL:https://stat.mit.edu/calendar/barber2020/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200214T110000
DTEND;TZID=America/New_York:20200214T120000
DTSTAMP:20260423T064727
CREATED:20200108T154414Z
LAST-MODIFIED:20200127T154235Z
UID:11534-1581678000-1581681600@idss-stage.mit.edu
SUMMARY:Diffusion K-means Clustering on Manifolds: provable exact recovery via semidefinite relaxations
DESCRIPTION:Abstract: We introduce the diffusion K-means clustering method on Riemannian submanifolds\, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion K-means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as similarities given by a kernel that captures the local geometry of manifolds. Thus the diffusion K-means is a multi-scale clustering tool that is suitable for data with non-linear and non-Euclidean geometric features in mixed dimensions. Given the number of clusters\, we propose a polynomial-time convex relaxation algorithm via the semidefinite programming (SDP) to solve the diffusion K-means. In addition\, we also propose a nuclear norm (i.e.\, trace norm) regularized SDP that is adaptive to the number of clusters. In both cases\, we show that exact recovery of the SDPs for diffusion K-means can be achieved under suitable between-cluster separability and within-cluster connectedness of the submanifolds\, which together quantify the hardness of the manifold clustering problem. We further propose the localized diffusion K-means by using the local adaptive bandwidth estimated from the nearest neighbors. We show that exact recovery of the localized diffusion K-means is fully adaptive to the local probability density and geometric structures of the underlying submanifolds. \nBio: Xiaohui Chen received a Ph. D. in Electrical and Computer Engineering in 2013 from the University of British Columba (UBC)\, Vancouver\, Canada. He was a post-doctoral fellow at the Toyota Technological Institute at Chicago (TTIC)\, a philanthropically endowed academic computer science institute located on the University of Chicago campus. In 2013 he joined the University of Illinois at Urbana-Champaign (UIUC) as an Assistant Professor of Statistics\, and since 2019 he is an Associate Professor of Statistics at UIUC. In 2019-2020 he is visiting the Institute for Data\, Systems\, and Society (IDSS) at Massachusetts Institute of Technology (MIT). He received numerous notable awards\, including an NSF CAREER Award in 2018\, an Arnold O. Beckman Award at UIUC in 2018\, an ICSA Outstanding Young Researcher Award in 2019\, an Associate appointment in the Center for Advanced Study at UIUC in 2020-2021\, and a Simons Fellowship in Mathematics from the Simons Foundation in 2020-2021.
URL:https://stat.mit.edu/calendar/chen2020
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200212T120000
DTEND;TZID=America/New_York:20200212T130000
DTSTAMP:20260423T064727
CREATED:20200129T131207Z
LAST-MODIFIED:20200129T170949Z
UID:11649-1581508800-1581512400@idss-stage.mit.edu
SUMMARY:Webinar: Inside the MITx MicroMasters Program in Statistics and Data Science
DESCRIPTION:Interested in starting your data science journey? Register for this special free virtual event. You’ll receive a confirmation e-mail with further details about the webinar.\n\n \nDemand for professionals skilled in data\, analytics\, and machine learning is exploding. A recent report by IBM and Burning Glass states that there will be 364K new job openings in data-driven professions this year in the US alone. Data scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. \nTo help train for this in-demand field\, MIT’s Institute for Data\, Systems\, and Society (IDSS) has created the MITx MicroMasters® Program in Statistics and Data Science. \nIn this 60-minute engaging and interactive webinar\, you will: \n\nLearn more about the courses in the program.\nFind out how these courses could bring you to MIT or other universities around the world for a graduate program.\nHear about the exclusive benefits for learners who upgrade to the MicroMasters Program track.\nGet real-time answers to your questions.\n\n  \n 
URL:https://event.on24.com/wcc/r/2170691/02F897D60682F202E261E07985F9CB92
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200207T110000
DTEND;TZID=America/New_York:20200207T120000
DTSTAMP:20260423T064727
CREATED:20200108T153346Z
LAST-MODIFIED:20200113T195921Z
UID:11531-1581073200-1581076800@idss-stage.mit.edu
SUMMARY:Gaussian Differential Privacy\, with Applications to Deep Learning
DESCRIPTION:Abstract: \nPrivacy-preserving data analysis has been put on a firm mathematical foundation since the introduction of differential privacy (DP) in 2006. This privacy definition\, however\, has some well-known weaknesses: notably\, it does not tightly handle composition. This weakness has inspired several recent relaxations of differential privacy based on the Renyi divergences. We propose an alternative relaxation we term “f-DP”\, which has a number of nice properties and avoids some of the difficulties associated with divergence based relaxations. First\, f-DP preserves the hypothesis testing interpretation of differential privacy\, which makes its guarantees easily interpretable. It allows for lossless reasoning about composition and post-processing\, and notably\, a direct way to analyze privacy amplification by subsampling. We define a canonical single-parameter family of definitions within our class that is termed “Gaussian Differential Privacy”\, based on hypothesis testing of two shifted normal distributions. We prove that this family is focal to f-DP by introducing a central limit theorem\, which shows that the privacy guarantees of any hypothesis-testing based definition of privacy (including differential privacy) converge to Gaussian differential privacy in the limit under composition. This central limit theorem also gives a tractable analysis tool. We demonstrate the use of the tools we develop by giving an improved analysis of the privacy guarantees of noisy stochastic gradient descent. This is joint work with Jinshuo Dong and Aaron Roth. \nBiography: \nWeijie Su is an Assistant Professor of Statistics at the Wharton School\, University of Pennsylvania. He is an associated faculty of the Applied Mathematics and Computational Science program at the University of Pennsylvania and a co-director of Penn Research in Machine Learning. Prior to joining Penn\, he received his Ph.D. in Statistics from Stanford University in 2016. His research interests span machine learning\, mathematical statistics\, private data analysis\, large-scale optimization\, and multiple hypothesis testing. He is a recipient of the Theodore Anderson Dissertation Award in Theoretical Statistics in 2016 and the NSF CAREER Award in 2019.
URL:https://stat.mit.edu/calendar/su2020
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200203
DTEND;VALUE=DATE:20200204
DTSTAMP:20260423T064727
CREATED:20191218T185004Z
LAST-MODIFIED:20191218T185351Z
UID:11471-1580688000-1580774399@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 February 3\, 2020. \n 
URL:https://xpro.mit.edu/courses/course-v1:xPRO+DSx/?utm_medium=website&utm_source=idss&utm_campaign=dsx-r12-sp20&utm_content=idss-calendar
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200129T080000
DTEND;TZID=America/New_York:20200130T170000
DTSTAMP:20260423T064727
CREATED:20200108T165912Z
LAST-MODIFIED:20200108T165912Z
UID:11539-1580284800-1580403600@idss-stage.mit.edu
SUMMARY:25th Annual LIDS Student Conference
DESCRIPTION:Welcome to the 24th annual LIDS Student Conference! The annual LIDS student conference provides an opportunity for graduate students to present their research to peers as well as to the community at large. \nThe conference will be held on January 29 – 30\, at MIT’s Stata Center Rooms 32-141.
URL:https://idss-stage.mit.edu/calendar/25th-annual-lids-student-conference/
LOCATION:32-141\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191211T160000
DTEND;TZID=America/New_York:20191211T170000
DTSTAMP:20260423T064727
CREATED:20191205T150900Z
LAST-MODIFIED:20191205T150900Z
UID:11401-1576080000-1576083600@idss-stage.mit.edu
SUMMARY:The Statistical Finite Element Method
DESCRIPTION:Abstract: \nThe finite element method (FEM) is one of the great triumphs of modern day applied mathematics\, numerical analysis and software development. Every area of the sciences and engineering has been positively impacted by the ability to model and study complex physical and natural systems described by systems of partial differential equations (PDE) via the FEM . \n\n\nIn parallel the recent developments in sensor\, measurement\, and signalling technologies enables the phenomenological study of systems as diverse as protein signalling in the cell\, to turbulent combustion in jet engines\, to plastic deformation in bridges.The connection between sensor data and FEM is currently restricted to data assimilation for solving inverse problems or the calibration of PDE based models. This however places unwarranted faith in the fidelity of the underlying mathematical description of the actual system under study.\n\nIf one concedes that there is ‘missing physics’ or mis-specification between generative reality and the mathematical abstraction defining the FEM then a framework to systematically characterise and propagate this uncertainty in FEM is required.This talk will present a formal statistical construction of the FEM which systematically blends both mathematical description with observational data and provides both small and large scale examples from 3D printed structures to working rail bridges currently operated by the United Kingdom Network Rail.\n\n\n\nBiography: \n\n\nMark Girolami is a Computational Statistician having ten years experience as a Chartered Engineer within IBM. In March 2019 he was elected to the Sir Kirby Laing Professorship of Civil Engineering (1965) within the Department of Engineering at the University of Cambridge where he also holds the Royal Academy of Engineering Research Chair in Data Centric Engineering. Girolami takes up the Sir Kirby Laing Chair upon the retirement of Professor Lord Robert Mair. Professor Girolami is a fellow of Christ’s College Cambridge. \n\n\n\n– \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes.  For more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/\n \n**Meetings are open to any interested researcher.
URL:https://stat.mit.edu/calendar/girolami/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191206T110000
DTEND;TZID=America/New_York:20191206T120000
DTSTAMP:20260423T064727
CREATED:20191017T134413Z
LAST-MODIFIED:20191112T204103Z
UID:10988-1575630000-1575633600@idss-stage.mit.edu
SUMMARY:Inferring the Evolutionary History of Tumors
DESCRIPTION:Abstract: \nBulk sequencing of tumor DNA is a popular strategy for uncovering information about the spectrum of mutations arising in the tumor\, and is often supplemented by multi-region sequencing\, which provides a view of tumor heterogeneity. The statistical issues arise from the fact that bulk sequencing makes the determination of sub-clonal frequencies\, and other quantities of interest\, difficult. In this talk I will discuss this problem\, beginning with its setting in population genetics. The data provide an estimate of the site frequency spectrum (SFS) of the mutations in the tumor\, which is used as the basis for inference. I will describe how Approximate Bayesian Computation can be used for inference in problems like this one in which likelihoods are intractable. I will also describe a model for selective clonal sweeps that estimates the number of subclones that have arisen in the tumor; here the inference is based on a method of moments using the SFS. Time permitting\, I will describe some novel experimental methods we are developing to understand the 3D structure of tumors\, paving the way for some challenging inferential problems that will require engagement from data scientists and others. \nBiography: \nSimon Tavaré joined Columbia University in 2018 as the Herbert and Florence Irving Director of the Irving Institute for Cancer Dynamics and a professor in the Departments of Statistics and Biological Sciences. From 1978 to 2003\, Simon worked in the USA and from 2003\, he was a professor in the Department of Applied Mathematics and Theoretical Physics and the Department of Oncology at the University of Cambridge\, England. From February 2013 to January 2018\, he was director of the Cancer Research UK Cambridge Institute\, which had become a department of the University of Cambridge in January 2013. His research focuses on statistical bioinformatics and computational biology\, particularly evolutionary approaches to understanding cancer biology. Dr. Tavaré is an elected fellow of the Academy of Medical Sciences and of the Royal Society\, and a member of the European Molecular Biology Organization. He was president of the London Mathematical Society from 2015 to 2017 and was elected a fellow of the American Mathematical Society and a foreign associate of the U.S. National Academy of Sciences in 2018.
URL:https://stat.mit.edu/calendar/tavare/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191205T140000
DTEND;TZID=America/New_York:20191205T160000
DTSTAMP:20260423T064727
CREATED:20191125T211047Z
LAST-MODIFIED:20191125T211509Z
UID:11364-1575554400-1575561600@idss-stage.mit.edu
SUMMARY:SES Dissertation Defense – Ian Schneider
DESCRIPTION:Market Design Opportunities for an Evolving Power System\nABSTRACT\nThe rapid growth of variable renewable energy is transforming the electric power system. Variable renewable energy\, specifically wind and solar power\, has grown five-fold since 2009; it constitutes a quarter of all electricity production in several U.S. states. Renewable energy brings benefits but also new challenges. Wind and solar energy production is variable and uncertain\, subject to weather changes that are challenging to forecast. These resources are non-dispatchable; their energy output cannot be fully controlled. At scale\, this poses technical challenges for the electricity grid and techno-economic challenges for electricity markets. \nImprovements to electricity markets and to grid operations can help reduce the cost of a reliable low-carbon power system. This thesis covers several topics to help enable well-functioning electricity markets in systems with high levels of renewable energy. First\, this thesis develops a game-theoretic model of producer strategy in electricity markets with high levels of renewable energy. It demonstrates how uncertainty\, correlation between stochastic resources\, and public forecasting impact producer strategy. It uncovers new issues that could impact market power in systems with high levels of renewable energy. Second\, the thesis models and explains the effects of retail electricity competition on producer market power and forward contracting. I show that increased retail competition could increase producer market power and decrease forward contracting; these issues could be important considerations for policies that relate to retail competition. Finally\, this thesis proposes new methods for improving incentive-based demand response programs. Demand response programs pay customers for reducing consumption when the marginal cost of electricity is high\, but they are challenged because utilities have imperfect information regarding customer demand. I show how tools from online learning can be used to improve the sequential decision problem of choosing customer baselines in demand response programs. This could help improve demand participation in energy markets\, which could ultimately help reduce the costs of operating a low-carbon power system. \nCOMMITTEE\nMunther Dahleh (chair & supervisor)\, Mardavij Roozbehani (supervisor)\, Paul Joskow
URL:https://idss-stage.mit.edu/calendar/ses-dissertation-defense-ian-schneider/
LOCATION:E18-304\, United States
ATTACH;FMTTYPE=image/png:https://idss-stage.mit.edu/wp-content/uploads/2017/09/Schneider-Ian.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191204T160000
DTEND;TZID=America/New_York:20191204T170000
DTSTAMP:20260423T064727
CREATED:20191121T153120Z
LAST-MODIFIED:20191121T153656Z
UID:11315-1575475200-1575478800@idss-stage.mit.edu
SUMMARY:Flexible Perturbation Models for Robustness to Misspecification
DESCRIPTION:Abstract: \nIn many applications\, there are natural statistical models with interpretable parameters that provide insight into questions of interest. While useful\, these models are almost always wrong in the sense that they only approximate the true data generating process. In some cases\, it is important to account for this model error when quantifying uncertainty in the parameters. We propose to model the distribution of the observed data as a perturbation of an idealized model of interest by using a nonparametric mixture model in which the base distribution is the idealized model. This provides robustness to small departures from the idealized model and\, further\, enables uncertainty quantification regarding the model error itself. Inference can easily be performed using existing methods for the idealized model in combination with standard methods for mixture models. Remarkably\, inference can be even more computationally efficient than in the idealized model alone\, because similar points are grouped into clusters that are treated as individual points from the idealized model. We demonstrate with simulations and an application to flow cytometry. \n– \nFor more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \n**Taking IDS.190 satisfies the seminar requirement for students in MIT’s Interdisciplinary Doctoral Program in Statistics (IDPS)\, but formal registration is open to any graduate student who can register for MIT classes. And the meetings are open to any interested researcher.   Talks will be followed by 30 minutes of tea/snacks and informal discussion.**
URL:https://stat.mit.edu/calendar/flexible-perturbation-models-for-robustness-to-misspecification/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191202T160000
DTEND;TZID=America/New_York:20191202T170000
DTSTAMP:20260423T064727
CREATED:20190619T191750Z
LAST-MODIFIED:20191203T191735Z
UID:9784-1575302400-1575306000@idss-stage.mit.edu
SUMMARY:Automating the Digitization of Historical Data on a Large Scale
DESCRIPTION:Over the past two centuries\, we have transitioned from an overwhelmingly agricultural world to one with vastly different patterns of economic organization. This transition has been remarkably uneven across space and time\, and has important implications for some of the most central challenges facing societies today. Deepening our understanding of the determinants of economic transformation requires data on the long-run trajectories of individuals and firms. However\, these data overwhelmingly remain trapped in hard copy\, with cost estimates for manual digitization totaling millions of dollars for even relatively modestly sized datasets. Automation has the potential to massively scale up the extraction of historical quantitative data from hard copy documents\, significantly expanding and democratizing access. However\, the synthesis of methodology required to digitize and catalog most historical data is not available off-the-shelf through commercial OCR software\, which performs poorly at recognizing irregular document layouts. Off-the-shelf tools for assembling raw unstructured output into structured databases likewise do not exist. \nWe develop methods for automating the digitization and classification of historical data on a large scale\, illustrating their application to a rich corpus of historical Japanese documents about firms and individuals. An array of methods from computer vision\, natural language processing\, and machine learning are used to detect complex document layouts and assemble a rich structured dataset that tracks the evolution of network relationships between Japanese managers\, government officials\, and firms across the 20th century. \nAbout the Speaker: Melissa Dell is a professor in the Economics Department and a faculty research fellow at the National Bureau of Economic Research. Her research focuses on long-run economic development\, primarily in Latin America and Asia. She has examined the impacts of weather on economic growth and is currently conducting research about the long-run effects of agrarian reform and agricultural technology investments in Mexico and East Asia. She received a PhD in Economics from MIT\, a masters degree in Economics from Oxford\, and a BA from Harvard College. \nReception to follow.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-melissa-dell-harvard-university/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191126T170000
DTEND;TZID=America/New_York:20191126T180000
DTSTAMP:20260423T064727
CREATED:20190802T191803Z
LAST-MODIFIED:20190802T192508Z
UID:10466-1574787600-1574791200@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-7/
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191125T160000
DTEND;TZID=America/New_York:20191125T170000
DTSTAMP:20260423T064727
CREATED:20190920T151129Z
LAST-MODIFIED:20190920T151129Z
UID:10851-1574697600-1574701200@idss-stage.mit.edu
SUMMARY:LIDS Seminar - Rayadurgam Srikant (University of Illinois at Urbana-Champaign)
DESCRIPTION:TBD \nBio: \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-rayadurgam-srikant-university-illinois-urbana-champaign
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191122T110000
DTEND;TZID=America/New_York:20191122T120000
DTSTAMP:20260423T064727
CREATED:20191017T134223Z
LAST-MODIFIED:20191115T204645Z
UID:10986-1574420400-1574424000@idss-stage.mit.edu
SUMMARY:Automated Data Summarization for Scalability in Bayesian Inference
DESCRIPTION:Abstract: \nMany algorithms take prohibitively long to run on modern\, large data sets. But even in complex\ndata sets\, many data points may be at least partially redundant for some task of interest. So one might instead construct and use a weighted subset of the data (called a “coreset”) that is much smaller than the original dataset. Typically running algorithms on a much smaller data set will take much less computing time\, but it remains to understand whether the output can be widely useful. (1) In particular\, can running an analysis on a smaller coreset yield answers close to those from running on the full data set? (2) And can useful coresets be constructed automatically for new analyses\, with minimal extra work from the user? We answer in the affirmative for a wide variety of problems in Bayesian inference. We demonstrate how to construct “Bayesian coresets” as an automatic\, practical pre-processing step. We prove that our method provides geometric decay in relevant approximation error as a function of coreset size. Empirical analysis shows that our method reduces approximation error by orders of magnitude relative to uniform random subsampling of data. Though we focus on Bayesian methods here\, we also show that our construction can be applied in other domains. \nBiography: \nTamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)\, the MIT Statistics and Data Science Center\, and the Institute for Data\, Systems\, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California\, Berkeley in 2014. Previously\, she received an AB in Mathematics from Princeton University (2007)\, a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008)\, an MPhil by research in Physics from the University of Cambridge (2009)\, and an MS in Computer Science from the University of California\, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning. She has been awarded an AISTATS Notable Paper Award (2019)\, NSF CAREER Award (2018)\, a Sloan Research Fellowship (2018)\, an Army Research Office Young Investigator Program award (2017)\, Google Faculty Research Awards\, an Amazon Research Award\, the ISBA Lifetime Members Junior Researcher Award\,\nthe Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods)\, the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research)\, the Berkeley Fellowship\, an NSF Graduate Research Fellowship\, a Marshall Scholarship\, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average). \n–\nThe MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.
URL:https://stat.mit.edu/calendar/broderick/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
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END:VCALENDAR