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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200320T110000
DTEND;TZID=America/New_York:20200320T120000
DTSTAMP:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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:20260518T073658
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191126T170000
DTEND;TZID=America/New_York:20191126T180000
DTSTAMP:20260518T073658
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:20260518T073658
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:20260518T073658
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191120T160000
DTEND;TZID=America/New_York:20191120T170000
DTSTAMP:20260518T073658
CREATED:20191115T211228Z
LAST-MODIFIED:20191115T211228Z
UID:11230-1574265600-1574269200@idss-stage.mit.edu
SUMMARY:A Causal Exposure Response Function with Local Adjustment for Confounding: A study of the health effects of long-term exposure to low levels of fine particulate matter
DESCRIPTION:Abstract:   \nIn the last two decades\, ambient levels of air pollution have declined substantially. Yet\, as mandated by the Clean Air Act\, we must continue to address the following question: is exposure to levels of air pollution that are well below the National Ambient Air Quality Standards (NAAQS) harmful to human health? Furthermore\, the highly contentious nature surrounding environmental regulations necessitates casting this question within a causal inference framework. Several parametric and semi-parametric regression modeling approaches have been used to estimate the exposure-response (ER) curve relating long-term exposure to air pollution and various health outcomes. However\, most of these approaches are not formulated in the context of a potential outcome framework for causal inference\, adjust for the same set of potential confounders across all levels of exposure\, and do not account for model uncertainty regarding covariate selection and the shape of the ER. In this paper\, we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment). LERCA allows for: a) different confounders and different strength of confounding at the different exposure levels; and b) model uncertainty regarding confounders’ selection and the shape of the ER. Also\, LERCA provides a principled way of assessing the observed covariates’ confounding importance at different exposure levels\, providing environmental researchers with important information regarding the set of variables to measure and adjust for in regression models. Using simulation studies\, we show that state of the art approaches perform poorly in estimating the ER curve in the presence of local confounding. Lastly\, LERCA is used on a large data set which includes health\, weather\, demographic\, and pollution information for 5\,362 zip codes and for the years of 2011-2013. \nBiography: \nDr. Francesca Dominici is Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Co-Director of the Data Science Initiative at Harvard University. She was recruited to the Harvard Chan School as a tenured Professor of Biostatistics in 2009. She was appointed Associate Dean of Information Technology in 2011 and Senior Associate Dean for Research in 2013. \n—- \nFor more information and an up-to-date schedule\, please see https://stellar.mit.edu/S/course/IDS/fa19/IDS.190/ \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. And the meetings are open to any interested researcher.   Talks will be followed by 30 minutes of tea/snacks and informal discussion.** \n 
URL:https://stat.mit.edu/calendar/a-causal-exposure-response-function-with-local-adjustment-for-confounding-a-study-of-the-health-effects-of-long-term-exposure-to-low-levels-of-fine-particulate-matter/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191119T160000
DTEND;TZID=America/New_York:20191119T170000
DTSTAMP:20260518T073658
CREATED:20191030T154157Z
LAST-MODIFIED:20191030T154157Z
UID:11098-1574179200-1574182800@idss-stage.mit.edu
SUMMARY:Stability of a Fluid Model for Fair Bandwidth Sharing with General File Size Distributions
DESCRIPTION:Abstract: \nMassoulie and Roberts introduced a stochastic model for a data communication network where file sizes are generally distributed and the network operates under a fair bandwidth sharing policy.  It has been a standing problem to prove stability of this general model when the average load on the system is less than the network’s capacity. A crucial step in an approach to this problem is to prove stability of an associated measure-valued fluid model. We shall describe prior work on this question done under various strong assumptions and indicate how to prove stability of the fluid model under mild conditions. \nThis talk is based on joint work with Yingjia Fu. \nBiography: \nRuth Williams holds the Charles Lee Powell Chair in Mathematics I at the University of California\, San Diego (UCSD). She is a mathematician who works in probability theory\, especially on stochastic processes and their applications. She is particularly known for her foundational work on reflecting diffusion processes in domains with corners\, for co-development with Maury Bramson of a systematic approach to proving heavy traffic limit theorems for multiclass queueing networks\, and for the development of fluid and diffusion approximations for the analysis and control of more general stochastic networks\, including those described by measure-valued processes. Her current research includes the study of stochastic models of complex networks\, for example\, those arising in Internet congestion control and systems biology. \nWilliams studied mathematics at the University of Melbourne where she earned her Bachelor of Science (Honours) and Master of Science degrees. She then studied at Stanford University where she earned her Ph.D. degree in Mathematics. She had a postdoc at the Courant Institute of Mathematical Sciences in New York before taking up a position as an Assistant Professor at the University of California\, San Diego (UCSD). She has remained at UCSD during her career\, where she is now a Distinguished Professor of Mathematics.
URL:https://stat.mit.edu/calendar/stability-of-a-fluid-model-for-fair-bandwidth-sharing-with-general-file-size-distributions/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191118T160000
DTEND;TZID=America/New_York:20191118T170000
DTSTAMP:20260518T073658
CREATED:20190920T151029Z
LAST-MODIFIED:20190920T151029Z
UID:10849-1574092800-1574096400@idss-stage.mit.edu
SUMMARY:LIDS Seminar - Sujay Sanghavi (University of Texas at Austin)
DESCRIPTION:TBD \nBio: \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-sujay-sanghavi-university-texas-austin
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191115T110000
DTEND;TZID=America/New_York:20191115T120000
DTSTAMP:20260518T073658
CREATED:20191017T134056Z
LAST-MODIFIED:20191108T190943Z
UID:10984-1573815600-1573819200@idss-stage.mit.edu
SUMMARY:Understanding machine learning with statistical physics
DESCRIPTION:Abstract: \nThe affinity between statistical physics and machine learning has long history\, this is reflected even in the machine learning terminology that is in part adopted from physics. Current theoretical challenges and open questions about deep learning and statistical learning call for unified account of the following three ingredients: (a) the dynamics of the learning algorithm\, (b) the architecture of the neural networks\, and (c) the structure of the data. Most existing theories are not taking in account all of those three aspects in a satisfactory manner. In this talk I will describe some of the results stemming from statistical physics applied to machine learning and how it does include the three ingredients\, although in a very simplified manner. Then I will focus on the current results improving our modelling in each of the three aspects covering recent articles [1-4]. \n[1] Aubin\, B.\, Maillard\, A.\, Krzakala\, F.\, Macris\, N.\, & Zdeborová\, L.; The committee machine: Computational to statistical gaps in learning a two-layers neural network. NeurIPS’18.\n[2] Sarao Mannelli\, S.\, Biroli\, G.\, Cammarota\, C.\, Krzakala\, F.\, & Zdeborová\, L.; Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model. NeurIPS’19.\n[3] Aubin\, B.\, Loureiro\, B.\, Maillard\, A.\, Krzakala\, F.\, & Zdeborová\, L.; The spiked matrix model with generative priors. NeurIPS’19.\n[4] Goldt\, S.\, Mézard\, M.\, Krzakala\, F.\, & Zdeborová\, L.; Modelling the influence of data structure on learning in neural networks. Preprint arXiv:1909.11500. \nBiography: \nLenka Zdeborová is a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay\, France. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director’s Postdoctoral Fellow. In 2014\, she was awarded the CNRS bronze medal\, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant\, in 2018 the Irène Joliot-Curie prize. She is editorial board member for Journal of Physics A\, Physical review E and Physical Review X.  Lenka’s expertise is in applications of methods developed in statistical physics\, such as advanced mean field methods\, replica method and related message passing algorithms\, to problems in machine learning\, signal processing\, inference and optimization.
URL:https://stat.mit.edu/calendar/zdeborova/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191113T160000
DTEND;TZID=America/New_York:20191113T170000
DTSTAMP:20260518T073658
CREATED:20191107T164432Z
LAST-MODIFIED:20191107T170724Z
UID:11159-1573660800-1573664400@idss-stage.mit.edu
SUMMARY:Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox
DESCRIPTION:Abstract: \nAdvances in Markov chain Monte Carlo in the past 30 years have made Bayesian analysis a routine practice. However\, there is virtually no practice of performing Monte Carlo integration from the Bayesian perspective; indeed\,this problem has earned the “paradox” label in the context of computing normalizing constants (Wasserman\, 2013). We first use the modeling-what-we-ignore idea of Kong et al. (2003) to explain that the crux of the paradox is not with the likelihood theory\, which is essentially the same as for a standard non-parametric probability/density estimation (Vardi\, 1985); though via using group theory\, it provides a richer framework for modeling the trade-off between statistical efficiency and computational efficiency. But there is a real Bayesian paradox: Bayesian analysis cannot be applied exactly for solving Bayesian computation\, because to perform the exact Bayesian Monte Carlo integration would require more computation than needed to solve the original Monte Carlo problem. We then show that there is a practical resolution to this paradox using the profile likelihood obtained in Kong et al. (2006) and that this approximation is second-order valid asymptotically. We also investigate a more computationally efficient approximation via an artificial likelihood of Geyer (1994). This artificial likelihood approach is only first-order valid\, but there is a computationally trivial adjustment to render its second-order validity. We demonstrate empirically the efficiency of these approximated Bayesian estimators\, compared to the usual frequentist-based Monte Carlo estimators\, such as bridge sampling estimators (Meng and Wong\, 1996). \n[This is a joint work with Masatoshi Uehara.]\nReferences: \nWasserman\, L. (2013) All of Statistics: A Concise Course in Statistical Inference.  Springer Science & Business Media. Also see https://normaldeviate.wordpress.com/2012/10/05/the-normalizing-constant-paradox/ \nKong\, A.\,P. McCullagh\, X.-L. Meng\, D. Nicolae\, and Z. Tan (2003). A theory of statistical models for Monte Carlo integration (with Discussions). J. R. Statist. Soc. B 65\, 585-604. \nhttp://stat.harvard.edu/XLM/JRoyStatSoc/JRoyStatSocB65-3_585-618_2003.pdf \nVardi\, Y. (1985). Empirical distributions in selection bias models. Ann. Statist. 13 (1)\, 178-203.  https://projecteuclid.org/download/pdf_1/euclid.aos/1176346585 \nKong\, A.\, P. McCullagh\, X.-L. Meng\, and D. Nicolae (2006). Further explorations of likelihood theory for Monte Carlo integration. In Advances in Statistical Modeling and Inference: Essays in Honor of Kjell A. Doksum (Ed: V. Nair)\, 563-592. World Scientific Press.  http://www.stat.harvard.edu/XLM/books/kmmn.pdf \nGeyer\, C. J. (1994). Estimating normalizing constants and reweighting mixtures in Markov chain Monte Carlo.Technical Report\, School of Statistics\,University of Minnesota\, Minneapolis 568. \nhttps://scholar.google.com/scholar?cluster=6307665497304333587&hl=en&as_sdt=0\,22 \nMeng\, X.-L. and Wong\, W.H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistics Sinica6\, 831-860. http://stat.harvard.edu/XLM/StatSin/StatSin6-4_831-860_1996.pdf \n\nBiography: \nXiao-Li Meng\, the Whipple V. N. Jones Professor of Statistics\, and the Founding Editor-in-Chief of Harvard Data Science Review\, is well known for his depth and breadth in research\, his innovation and passion in pedagogy\, his vision and effectiveness in administration\, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001\, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas\, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics\, and he is the author of “The XL-Files\,” a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g.\, the interplay among Bayesian\, Fiducial\, and frequentist perspectives; frameworks for multi-source\, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g.\, posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural\, social\, and medical sciences and engineering (e.g.\, complex statistical modeling in astronomy and astrophysics\, assessing disparity in mental health services\, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard\, where he served as the Chair of the Department of Statistics (2004-2012) and the Dean of Graduate School of Arts and Sciences (2012-2017). \n\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/artificial-bayesian-monte-carlo-integration-a-practical-resolution-to-the-bayesian-normalizing-constant-paradox/
LOCATION:E18-304\, United States
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191112T160000
DTEND;TZID=America/New_York:20191112T173000
DTSTAMP:20260518T073658
CREATED:20190802T192236Z
LAST-MODIFIED:20190802T192751Z
UID:10468-1573574400-1573579800@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.\nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-info-session-2/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191108T110000
DTEND;TZID=America/New_York:20191108T120000
DTSTAMP:20260518T073658
CREATED:20191017T133140Z
LAST-MODIFIED:20191104T140845Z
UID:10982-1573210800-1573214400@idss-stage.mit.edu
SUMMARY:SDP Relaxation for Learning Discrete Structures: Optimal Rates\, Hidden Integrality\, and Semirandom Robustness
DESCRIPTION:Abstract:\n\nWe consider the problems of learning discrete structures from network data under statistical settings. Popular examples include various block models\, Z2 synchronization and mixture models. Semidefinite programming (SDP) relaxation has emerged as a versatile and robust approach to these problems. We show that despite being a relaxation\, SDP achieves the optimal Bayes error rate in terms of distance to the target solution. Moreover\, SDP relaxation is provably robust under the so-called semirandom model\, which frustrates many existing algorithms. Our proof involves a novel primal-dual analysis that establishes what we call the hidden integrality property: the SDP relaxation tightly approximates the optimal (yet unimplementable) integer programs with oracle information.\n\nJoint work with Yingjie Fei (Cornell Ph.D.)\, who won 2nd place in INFORMS Nicholson Student Paper Competition.\n\nBio: Yudong Chen is an assistant professor at the School of Operations Research and Information Engineering (ORIE)\, Cornell University. Before joining Cornell\, he was a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences at University of California\, Berkeley. He obtained his Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin\, and his M.S. and B.S. from Tsinghua University. His research interests include machine learning\, high-dimensional and robust statistics\, convex and non-convex optimization\, and applications in communication and computer networks.
URL:https://stat.mit.edu/calendar/chen/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191106T160000
DTEND;TZID=America/New_York:20191106T170000
DTSTAMP:20260518T073658
CREATED:20191031T161910Z
LAST-MODIFIED:20191031T161910Z
UID:11125-1573056000-1573059600@idss-stage.mit.edu
SUMMARY:Probabilistic Inference and Learning with Stein’s Method
DESCRIPTION:IDS.190 – Topics in Bayesian Modeling and Computation \n**PLEASE NOTE ROOM CHANGE TO BUILDING 37-212 FOR THE WEEKS OF 10/30 AND 11/6** \nSpeaker: \nLester Mackey (Microsoft Research) \nAbstract: \n\nStein’s method is a powerful tool from probability theory for bounding the distance between probability distributions.  In this talk\, I’ll describe how this tool designed to prove central limit theorems can be adapted to assess and improve the quality of practical inference procedures.  I’ll highlight applications to Markov chain sampler selection\, goodness-of-fit testing\, variational inference\, and nonconvex optimization and close with several opportunities for future work.\n\n\n\n\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/probabilistic-inference-and-learning-with-steins-method/
LOCATION:37-212
CATEGORIES:IDS.190 - Topics in Bayesian Modeling and Computation
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191106T160000
DTEND;TZID=America/New_York:20191106T170000
DTSTAMP:20260518T073658
CREATED:20191028T131513Z
LAST-MODIFIED:20191028T132229Z
UID:11074-1573056000-1573059600@idss-stage.mit.edu
SUMMARY:One-shot Information Theory via Poisson Processes
DESCRIPTION:Abstract: \nIn information theory\, coding theorems are usually proved in the asymptotic regime where the blocklength tends to infinity. While there are techniques for finite blocklength analysis\, they are often more complex than their asymptotic counterparts. In this talk\, we study the use of Poisson processes in proving coding theorems\, which not only gives sharp one-shot and finite blocklength results\, but also gives significantly shorter proofs than conventional asymptotic techniques in some settings. Instead of using fixed-size random codebooks\, we construct the codebook as a Poisson process. We present a lemma\, called the Poisson matching lemma\, which can replace both packing and covering lemmas in proofs based on typicality. We then demonstrate its use in settings such as channel coding with channel state information at the encoder\, lossy source coding with side information at the decoder\, joint source-channel coding\, broadcast channels\, and distributed lossy source coding. This shows that the Poisson matching lemma is a viable alternative to typicality for most problems in network information theory. \nThe talk is based on a joint work with Prof. Venkat Anantharam (UC Berkeley). \nBio: \nCheuk Ting Li received the B.Sc. degree in mathematics and B.Eng. degree in information engineering from The Chinese University of Hong Kong in 2012\, and the M.S. and Ph.D. degree in electrical engineering from Stanford University in 2014 and 2018 respectively. He was awarded the 2016 IEEE Jack Keil Wolf ISIT Student Paper Award. He is currently a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences\, University of California\, Berkeley. His research interests include finite blocklength schemes in information theory\, generation of random variables\, and information-theoretic secrecy. \n 
URL:https://idss-stage.mit.edu/calendar/one-shot-information-theory-via-poisson-processes/
LOCATION:E18-304\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191104T160000
DTEND;TZID=America/New_York:20191104T170000
DTSTAMP:20260518T073658
CREATED:20190722T171135Z
LAST-MODIFIED:20191105T195746Z
UID:10365-1572883200-1572886800@idss-stage.mit.edu
SUMMARY:Causal Inference in the Age of Big Data
DESCRIPTION:The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social\, behavioral\, and medical treatments. With the availability of fine-grained data\, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead\, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges\, Machine Learning (ML) is now being used for evaluating and predicting the effectiveness of interventions in a wide range of domains from technology firms to clinical medicine and election campaigns. However\, there are a number of issues that arise with the use of ML for causal inference. For example\, although ML and related statistical models are good for prediction\, they are not designed to estimate causal effects. Instead\, they focus on predicting observed outcomes. In this talk\, a number of meta-algorithms are presented that can take advantage of any supervised learning method to estimate the Conditional Average Treatment Effect function. Also\, discussed are new theoretical results on confidence intervals and overlap in high-dimensional covariates and a new algorithm for optimal linear aggregation functions for tree-based estimators. \nAbout the speaker: Jasjeet Sekhon is the Robson Professor of Political Science and Statistics at the University of California\, Berkeley. His current research focuses on creating new machine learning methods for estimating causal effects in observational and experimental studies and evaluating social science\, digital\, public health\, and medical interventions. He is also the Head of Causal Inference at Bridgewater Associates. \nReception to follow.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-jasjeet-sekhon-uc-berkeley/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
END:VCALENDAR