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DTSTART;TZID=UTC:20171012T143000
DTEND;TZID=UTC:20171012T153000
DTSTAMP:20260407T124559
CREATED:20170831T223408Z
LAST-MODIFIED:20171003T145827Z
UID:6064-1507818600-1507822200@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series: Stefano Soatto (UCLA)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-stefano-soatto-ucla/
LOCATION:34-401 (Grier Room)\, The Stata Center (34-401)\, 50 Vassar Street\, Cambridge\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171006T110000
DTEND;TZID=America/New_York:20171006T120000
DTSTAMP:20260407T124559
CREATED:20170929T210606Z
LAST-MODIFIED:20171002T162240Z
UID:6516-1507287600-1507291200@idss-stage.mit.edu
SUMMARY:Transport maps for Bayesian computation - Youssef Marzouk (MIT)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/transport-maps-for-bayesian-computation/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20171003T163000
DTEND;TZID=UTC:20171003T173000
DTSTAMP:20260407T124559
CREATED:20170831T225119Z
LAST-MODIFIED:20170926T131728Z
UID:6072-1507048200-1507051800@idss-stage.mit.edu
SUMMARY:IDSS Distinguished Seminar Series: Latanya Sweeney (Harvard University)
DESCRIPTION:Title: How Technology Design will Dictate Our Civic Future \nAbstract:\nTechnology designers are the new policymakers. No one elected them\, and most people do not know their names\, but the decisions they make when producing the latest gadgets and online innovations dictate the code by which we conduct our daily lives and govern our country. Challenges to the privacy and security of our personal data are part of the first wave of this change; as technology progresses\, says Latanya Sweeney\, every demographic value and every law comes up for grabs and will likely be redefined by what technology does or does not enable. How will it all fit together or fall apart? Join Sweeney\, who after serving as chief technology officer at the U.S. Federal Trade Commission\, has been helping others unearth unforeseen consequences and brainstorm on how to engineer the way forward. \nBio:\nLatanya Sweeney is a Professor at Harvard University; Faculty Dean at Harvard’s Currier House; Editor-in-Chief of Technology Science; Director and Founder of Harvard’s Data Privacy Lab; the former Chief Technology Officer at the U.S. Federal Trade Commission; and Commissioner in the U.S. Commission on Evidence-based Policy Making. Dr. Sweeney holds four patents and is credited with more than 100 academic publications. She is a recipient of the prestigious American Psychiatric Association’s Privacy Advocacy Award\, an elected fellow of the American College of Medical Informatics\, and has testified before government bodies worldwide. Dr. Sweeney became the first African American woman to earn a PhD in computer science from MIT in 2001. More information about her is available at latanyasweeney.org.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-series-latanya-sweeney-harvard-university/
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170919T160000
DTEND;TZID=America/New_York:20170919T170000
DTSTAMP:20260407T124559
CREATED:20171002T153658Z
LAST-MODIFIED:20190501T145308Z
UID:6526-1505836800-1505840400@idss-stage.mit.edu
SUMMARY:Networking for Big Data: Theory and Optimization for NDN
DESCRIPTION:The advent of Big Data is stimulating the development of new networking architectures which facilitate the acquisition\, transmission\, storage\, and computation of data. In particular\, Named Data Networking (NDN) is an emerging content-centric networking architecture which focuses on enabling end users to obtain the data they want\, rather than to communicate with specific nodes. By naming content instead of their locations\, NDN transforms data into a first-class network entity. \nIn this talk\, we present a new analytical and design framework for the optimization of key network functionalities within the NDN architecture\, which is also broadly applicable to content delivery and peer-to-peer networks. The framework includes the joint optimization of traffic engineering and caching strategies\, in order to best utilize both bandwidth and storage for efficient content distribution. It also includes optimal congestion control when user demand for content becomes excessive. We first develop distributed and adaptive algorithms for joint request forwarding and dynamic cache placement and eviction\, which effectively achieve network load balancing\, thereby maximizing the user demand rate that the NDN network can satisfy. Next\, we develop content-based congestion control algorithms which naturally work in concert with forwarding and caching to achieve a favorable tradeoff between the aggregate user utility from admitted content requests and the total user delay. Numerical experiments within a number of network settings demonstrate the superior performance of these algorithms in terms of multiple metrics. \nJoint work with Tracey Ho\, Ying Cui\, Ran Liu\, Michael Burd\, and Derek Leong \nBio: Edmund Yeh received his B.S. in Electrical Engineering with Distinction and Phi Beta Kappa from Stanford University in 1994. He then studied at Cambridge University on the Winston Churchill Scholarship\, obtaining his M.Phil in Engineering in 1995. He received his Ph.D. in Electrical Engineering and Computer Science from MIT under Professor Robert Gallager in 2001. He is currently Professor of Electrical and Computer Engineering at Northeastern University. He was previously Assistant and Associate Professor of Electrical Engineering\, Computer Science\, and Statistics at Yale University. He has held visiting positions at MIT\, Stanford\, Princeton\, UC Berkeley\, EPFL\, and TU Munich. \n\n\nProfessor Yeh was one of the PIs on the original NSF-funded FIA Named Data Networking project. He will serve as General Co-Chair for ACM Conference on Information Centric Networking (ICN) 2018 in Boston. He is the recipient of the Alexander von Humboldt Research Fellowship\, the Army Research Office Young Investigator Award\, the Winston Churchill Scholarship\, the National Science Foundation and Office of Naval Research Graduate Fellowships\, the Barry M. Goldwater Scholarship\, the Frederick Emmons Terman Engineering Scholastic Award\, and the President’s Award for Academic Excellence (Stanford University). Professor Yeh has served as the Secretary of the Board of Governors of the IEEE Information Theory Society. He received the Best Paper Award at the 2015 IEEE International Conference on Communications (ICC) Communication Theory Symposium. \n\n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/networking-big-data-theory-and-optimization-ndn
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170919T160000
DTEND;TZID=UTC:20170919T170000
DTSTAMP:20260407T124559
CREATED:20170831T221731Z
LAST-MODIFIED:20170831T230256Z
UID:6060-1505836800-1505840400@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series: Edmund Yeh (Northeastern University)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-edmund-yeh-northeastern-university/
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170919T150000
DTEND;TZID=America/New_York:20170919T160000
DTSTAMP:20260407T124559
CREATED:20170908T183528Z
LAST-MODIFIED:20170908T190103Z
UID:6350-1505833200-1505836800@idss-stage.mit.edu
SUMMARY:The BLOSSOMS - Augmented World Project - Dr. Miri Barak (Israel Institute of Technology)
DESCRIPTION:Abstract:\nThis talk presents a critical analysis of the participants’ views about the significant role of advanced technologies in STEM education.  The talk discusses the triangular relations among academia\, government\, and schools\, with examples from the two countries.  It also discusses ways for integrating technology-enhanced Project Base Learning (PBL) with the use of Augmented World (AW).  The BLOSSOMS-AW project responds to the call for reforming education in science\, technology\, engineering\, and mathematics (STEM).  Its goal is to develop\, implement\, and evaluate a model for technology-enhanced PBL for the promotion of social constructivist teaching and scientific thinking. PBL is carried out through the integration of two complementary environments: MIT BLOSSOMS– an online environment for interactive video lessons and problem solving modules (http://blossoms.mit.edu)\, and Augmented World– a location-based\, content-generation\, peer-assessment platform (http://augmentedworld.site). \n  \nBio:\nDr. Miri Barak is an Assistant Professor at the Faculty of Education in Science and Technology\, Technion\, Israel Institute of Technology. Her research is situated at the interface of science and engineering education\, with a special interest in Web-based learning and 21st century skills. Dr. Barak’s work is guided by social constructivist learning theories that provide the theoretical and practical frameworks for understanding the mechanisms of teaching and learning in technology-enhanced environments.  Dr. Barak was a postdoctoral fellow at the Center for Educational Computing Initiatives (CECI)\, MIT.  At the Technion\, she is leading an international research project on massive online open courses (MOOCs).
URL:https://idss-stage.mit.edu/calendar/dr-miri-barak-israel-institute-of-technology/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170912T163000
DTEND;TZID=UTC:20170912T173000
DTSTAMP:20260407T124559
CREATED:20170831T223845Z
LAST-MODIFIED:20171006T183252Z
UID:6066-1505233800-1505237400@idss-stage.mit.edu
SUMMARY:Fast and Slow Learning from Reviews
DESCRIPTION:Speaker: Daron Acemoglu (MIT)\nMany online platforms present summaries of reviews by previous users. Even though such reviews could be useful\, previous users leaving reviews are typically a selected sample of those who have purchased the good in question\, and may consequently have a biased assessment. In this paper\, we construct a simple model of dynamic Bayesian learning and profit-maximizing behavior of online platforms to investigate whether such review systems can successfully aggregate past information and the incentives of the online platform to choose the relevant features of the review system. \nOn the consumer side\, we assume that each individual cares about the underlying quality of the good in question\, but in addition has heterogeneous ex ante and ex post preferences (meaning that she has a different strength of preference for the good in question than other users\, and her enjoyment conditional on purchase is also a random variable). After purchasing a good\, depending on how much they have enjoyed it\, users can decide to leave a positive or a negative review (or leave no review if they do not have strong preferences). New users observe a summary statistic of past reviews (such as fraction of all reviews that are positive or fraction of all users that have left positive review etc.). Our first major result shows that\, even though reviews come from a selected sample of users\, Bayesian learning ensures that as the number of potential users grows\, the assessment of the underlying state converges almost surely to the true quality of the good. More importantly\, we provide a tight characterization of the speed of learning (which is a contribution relative to most of the works in this area that focus on whether there is learning or not). \nUnder the assumption that the online platform receives a constant revenue from every user that purchases (because of commissions from sellers or from advertising revenues)\, we then show that\, in any Bayesian equilibrium\, the profits of the online platform are a function of the speed of learning of users. Using this result\, we study the design of the review system by the online platform\, and show the possibility of both fast and slow learning from reviews.\nAuthors: Daron Acemoglu\, Ali Makhdoumi\, Azarakhsh Malekian and Asu Ozdadaglar. \nBiography\nDaron Acemoglu is the Elizabeth and James Killian Professor of Economics at MIT. In 2005 he received the John Bates Clark Medal awarded to economists under forty judged to have made the most significant contribution to economic thought and knowledge. Among many other awards\, in 2017 he was given an Honorary Doctorate (Bath University)\, Great Immigrant List of the Carnegie foundations\, BBVA Frontiers of Knowledge Award in Economics and a Carnegie Fellow.
URL:https://idss-stage.mit.edu/calendar/fast-and-slow-learning-from-reviews/
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170908T110000
DTEND;TZID=UTC:20170908T120000
DTSTAMP:20260407T124559
CREATED:20170831T225546Z
LAST-MODIFIED:20170908T172603Z
UID:6074-1504868400-1504872000@idss-stage.mit.edu
SUMMARY:New Provable Techniques for Learning and Inference in Probabilistic Graphical Models
DESCRIPTION:Speaker: Andrej Risteski (Princeton University)\nA common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the “best fit” model parameters from raw data\, while inference is the task of answering probabilistic queries for a model with known parameters (e.g. what is the marginal distribution of a subset of variables\, after conditioning on the values of some other variables). Learning can be thought of as finding a graphical model that “explains” the raw data\, while the inference queries extract the “knowledge” the graphical model contains. \nI will focus on a few vignettes from my research which give new provable techniques for these tasks:\n– In the context of learning\, I will talk about method-of-moments techniques for learning noisy-or Bayesian networks\, which are used for modeling the causal structure of diseases and symptoms.\n– In the context of inference\, I will talk about a new understanding of a class of algorithms for calculating partition functions\, called variational methods\, through the lens of convex programming hierarchies. Time permitting\, I will also speak about MCMC methods for sampling from highly multimodal distributions using simulated tempering. \nThe talk will assume no background\, and is meant as a “meet and greet” talk surveying various questions I’ve worked on and am interested in. \nBiography\nI work in the intersection of machine learning and theoretical computer science\, with the primary goal of designing provable and practical algorithms for problems arising in machine learning. Broadly\, this includes tasks like clustering\, maximum likelihood estimation\, inference\, learning generative models. \nAll of these tend to be non-convex in nature and intractable in general. However\, in practice\, a plethora of heuristics like gradient descent\, alternating minimization\, convex relaxations\, variational methods work reasonably well. In my research\, I endeavor to understand what are realistic conditions under which we can give guarantees of the performance of these algorithms\, as well as devise new\, more efficient methods. \nI was a PhD student in the Computer Science Department at Princeton University\, working under the advisement of Sanjeev Arora. Starting September 2017\, I will hold a joint position in the Institute for Data\, Systems\, and Society (IDSS) and the Applied Mathematics department at MIT\, as a Norbert Wiener Fellow and applied mathematics instructor respectively.
URL:https://idss-stage.mit.edu/calendar/new-provable-techniques-for-learning-and-inference-in-probabilistic-graphical-models/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170907T160000
DTEND;TZID=UTC:20170907T170000
DTSTAMP:20260407T124559
CREATED:20170901T232109Z
LAST-MODIFIED:20170901T232451Z
UID:6081-1504800000-1504803600@idss-stage.mit.edu
SUMMARY:Beyond Big Data
DESCRIPTION:Speaker: Matthew Salganik (Princeton University)\nThe digital age has transformed the ways that researchers are able to study social behavior. These new opportunities mean that the future of social research will involve combining approaches from social scientists and data scientists\, a hybrid that is often called computational social science. After providing some perspective on this growing field\, the talk will focus on the Fragile Families Challenge\, a scientific mass collaboration involving hundreds of social scientists and data scientists working together on a project to improve the lives of disadvantaged children in the United States. \nBiography \nMatthew Salganik is Professor of Sociology at Princeton University\, and he is affiliated with several of Princeton’s interdisciplinary research centers: the Office for Population Research\, the Center for Information Technology Policy\, the Center for Health and Wellbeing\, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of the forthcoming book Bit by Bit: Social Research in the Digital Age. \nSalganik’s research has been published in journals such as Science\, PNAS\, Sociological Methodology\, and Journal of the American Statistical Association. His papers have won the Outstanding Article Award from the Mathematical Sociology Section of the American Sociological Association and the Outstanding Statistical Application Award from the American Statistical Association. Popular accounts of his work have appeared in the New York Times\, Wall Street Journal\, Economist\, and New Yorker. Salganik’s research is funded by the National Science Foundation\, National Institutes of Health\, Joint United Nations Program\nfor HIV/AIDS (UNAIDS)\, Russell Sage Foundation\, Sloan Foundation\, Facebook\, and Google. During sabbaticals from Princeton\, he has been a Visiting Professor at Cornell Tech and a Senior Research at Microsoft Research. \nThis is a special IDSS Seminar.
URL:https://idss-stage.mit.edu/calendar/beyond-big-data/
LOCATION:MIT Building 34\, Room 401B\, The Grier Room (34-401B)\, 50 Vassar Street\, Cambridge\, MA\, 02139\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170522T160000
DTEND;TZID=America/New_York:20170522T160000
DTSTAMP:20260407T124559
CREATED:20190627T212122Z
LAST-MODIFIED:20190627T212842Z
UID:10083-1495468800-1495468800@idss-stage.mit.edu
SUMMARY:Industrial Autonomous Systems: Vision and State of the Art
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing.
URL:https://idss-stage.mit.edu/calendar/industrial-autonomous-systems-vision-and-state-of-the-art-2/
LOCATION:32-D677\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170519T110000
DTEND;TZID=America/New_York:20170519T110000
DTSTAMP:20260407T124559
CREATED:20190627T212122Z
LAST-MODIFIED:20190627T212122Z
UID:10084-1495191600-1495191600@idss-stage.mit.edu
SUMMARY:Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/fast-rates-for-bandit-optimization-with-upper-confidence-frank-wolfe-2/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170516T160000
DTEND;TZID=America/New_York:20170516T160000
DTSTAMP:20260407T124559
CREATED:20190627T212122Z
LAST-MODIFIED:20190627T212647Z
UID:10085-1494950400-1494950400@idss-stage.mit.edu
SUMMARY:Stable Optimal Control and Semicontractive Dynamic Programming
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/stable-optimal-control-and-semicontractive-dynamic-programming-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170512T110000
DTEND;TZID=America/New_York:20170512T110000
DTSTAMP:20260407T124559
CREATED:20190627T212123Z
LAST-MODIFIED:20190627T212123Z
UID:10086-1494586800-1494586800@idss-stage.mit.edu
SUMMARY:Invariance and Causality
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/invariance-and-causality-2/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20170511
DTEND;VALUE=DATE:20170513
DTSTAMP:20260407T124559
CREATED:20190627T212123Z
LAST-MODIFIED:20190627T212123Z
UID:10087-1494460800-1494633599@idss-stage.mit.edu
SUMMARY:LIDS Smart Urban Infrastructures Workshop
DESCRIPTION:The LIDS Smart Urban Infrastructures Workshop is a two-day event showcasing current work and emerging research opportunities at the intersection of smart services and urban infrastructure systems.
URL:https://idss-stage.mit.edu/calendar/lids-smart-urban-infrastructures-workshop-2/
LOCATION:MIT Media Lab (E14-648)\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170505T110000
DTEND;TZID=America/New_York:20170505T110000
DTSTAMP:20260407T124559
CREATED:20190627T212126Z
LAST-MODIFIED:20190627T212126Z
UID:10088-1493982000-1493982000@idss-stage.mit.edu
SUMMARY:Some related phase transitions in phylogenetics and social network analysis 
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/some-related-phase-transitions-in-phylogenetics-and-social-network-analysis-2/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170428T110000
DTEND;TZID=America/New_York:20170428T110000
DTSTAMP:20260407T124559
CREATED:20190627T212126Z
LAST-MODIFIED:20190627T212126Z
UID:10089-1493377200-1493377200@idss-stage.mit.edu
SUMMARY:Testing properties of distributions over big domains
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/testing-properties-of-distributions-over-big-domains-2/
LOCATION:32-141\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170425T160000
DTEND;TZID=America/New_York:20170425T160000
DTSTAMP:20260407T124559
CREATED:20190627T212126Z
LAST-MODIFIED:20190627T212126Z
UID:10090-1493136000-1493136000@idss-stage.mit.edu
SUMMARY:Recent Methodological Advances in Automated Causal Discovery
DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.  
URL:https://idss-stage.mit.edu/calendar/recent-methodological-advances-in-automated-causal-discovery-2/
LOCATION:32-141\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170420T080000
DTEND;TZID=America/New_York:20170420T170000
DTSTAMP:20260407T124559
CREATED:20171102T174309Z
LAST-MODIFIED:20180501T190417Z
UID:6918-1492675200-1492707600@idss-stage.mit.edu
SUMMARY:SDSCon 2017: Statistics and Data Science Conference
DESCRIPTION:SDSCon 2017 is a celebration of MIT’s statistics and data science community. Organized by MIT’s Statistics and Data Center (SDSC)\, the conference will feature presentations from established academic leaders\, industry innovators\, and rising stars in the field. Discussions will cover a wide range of theory and application\, representing the latest research and breakthroughs in statistics and data science. \nSDSC is an MIT-wide focal point for advancing academic programs and research activities in statistics and data science. It was formed in 2015 as part of the MIT Institute for for Data\, Systems\, and Society (IDSS).
URL:https://idss-stage.mit.edu/calendar/sdscon-2017-statistics-and-data-science-center-conference/
LOCATION:34-401 (Grier Room)\, The Stata Center (34-401)\, 50 Vassar Street\, Cambridge\, 02139\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170419T160000
DTEND;TZID=America/New_York:20170419T160000
DTSTAMP:20260407T124559
CREATED:20190627T212126Z
LAST-MODIFIED:20190627T212126Z
UID:10091-1492617600-1492617600@idss-stage.mit.edu
SUMMARY:The Landscape of Some Statistical Problems
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/the-landscape-of-some-statistical-problems-2/
LOCATION:E18-304\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170414T110000
DTEND;TZID=America/New_York:20170414T110000
DTSTAMP:20260407T124559
CREATED:20190627T212126Z
LAST-MODIFIED:20190627T212126Z
UID:10092-1492167600-1492167600@idss-stage.mit.edu
SUMMARY:Active learning with seed examples and search queries
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/active-learning-with-seed-examples-and-search-queries-2/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170413T160000
DTEND;TZID=America/New_York:20170413T160000
DTSTAMP:20260407T124559
CREATED:20190627T212127Z
LAST-MODIFIED:20190627T212127Z
UID:10093-1492099200-1492099200@idss-stage.mit.edu
SUMMARY:Data-Driven Models in Power Systems
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/data-driven-models-in-power-systems-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170411T160000
DTEND;TZID=America/New_York:20170411T160000
DTSTAMP:20260407T124559
CREATED:20190627T212127Z
LAST-MODIFIED:20190627T212127Z
UID:10094-1491926400-1491926400@idss-stage.mit.edu
SUMMARY:Geometries of Word Embeddings
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/geometries-of-word-embeddings-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170410T093000
DTEND;TZID=America/New_York:20170410T093000
DTSTAMP:20260407T124559
CREATED:20190627T212127Z
LAST-MODIFIED:20190627T212127Z
UID:10095-1491816600-1491816600@idss-stage.mit.edu
SUMMARY:From Theory to Methodology and Data Analysis: A Study of Model (Mis)specification
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/from-theory-to-methodology-and-data-analysis-a-study-of-model-misspecification-2/
LOCATION:46-3002\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170407T110000
DTEND;TZID=America/New_York:20170407T110000
DTSTAMP:20260407T124559
CREATED:20190627T212127Z
LAST-MODIFIED:20190627T212127Z
UID:10096-1491562800-1491562800@idss-stage.mit.edu
SUMMARY:Sample-optimal inference\, computational thresholds\, and the methods of moments 
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/sample-optimal-inference-computational-thresholds-and-the-methods-of-moments-2/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170406T160000
DTEND;TZID=America/New_York:20170406T160000
DTSTAMP:20260407T124559
CREATED:20190627T212128Z
LAST-MODIFIED:20190627T212128Z
UID:10097-1491494400-1491494400@idss-stage.mit.edu
SUMMARY:A System Level Approach to Controller Synthesis
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/a-system-level-approach-to-controller-synthesis-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170404T160000
DTEND;TZID=America/New_York:20170404T160000
DTSTAMP:20260407T124559
CREATED:20190627T212132Z
LAST-MODIFIED:20190627T212132Z
UID:10098-1491321600-1491321600@idss-stage.mit.edu
SUMMARY:Capacity via Symmetry: Extensions and Practical Consequences
DESCRIPTION:The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://idss-stage.mit.edu/calendar/capacity-via-symmetry-extensions-and-practical-consequences-2/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170324T110000
DTEND;TZID=America/New_York:20170324T110000
DTSTAMP:20260407T124559
CREATED:20190627T212132Z
LAST-MODIFIED:20190627T212132Z
UID:10099-1490353200-1490353200@idss-stage.mit.edu
SUMMARY:Jagers-Nerman stable age distribution theory\, change point detection and power of two choices in evolving networks
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/jagers-nerman-stable-age-distribution-theory-change-point-detection-and-power-of-two-choices-in-evolving-networks-2/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170322T160000
DTEND;TZID=America/New_York:20170322T160000
DTSTAMP:20260407T124559
CREATED:20190627T212133Z
LAST-MODIFIED:20190627T212133Z
UID:10100-1490198400-1490198400@idss-stage.mit.edu
SUMMARY:Inference and Control in Routing Games
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/inference-and-control-in-routing-games-2/
LOCATION:1-131\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170317T110000
DTEND;TZID=America/New_York:20170317T110000
DTSTAMP:20260407T124559
CREATED:20190627T212133Z
LAST-MODIFIED:20190627T212133Z
UID:10101-1489748400-1489748400@idss-stage.mit.edu
SUMMARY:Probabilistic factorizations of big tables and networks
DESCRIPTION:The Stochastics and Statistics Seminar is a weekly meeting organized by the Statistics and Data Science Center (SDSC). It consists of a series of one-hour presentations by worldwide leaders making cutting edge contributions to methodological and theoretical advances in data science. These fields include probability\, statistics\, optimization\, and applied mathematics. The seminar also regularly features experts in applications domains such as biology or engineering. This intellectual diversity has contributed to the organic assembly of a dynamic and diverse audience articulated around a core group composed of faculty\, postdocs and graduate students from different department and affiliated with IDSS. Every week\, this audience is supplemented by a large number—often more than doubled—of attendees from all of MIT reflecting the interdisciplinary nature of the stochastics and statistics seminar. 
URL:https://idss-stage.mit.edu/calendar/probabilistic-factorizations-of-big-tables-and-networks-2/
LOCATION:32-141\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170313T150000
DTEND;TZID=America/New_York:20170313T163000
DTSTAMP:20260407T124559
CREATED:20190904T175423Z
LAST-MODIFIED:20190904T175423Z
UID:10617-1489417200-1489422600@idss-stage.mit.edu
SUMMARY:Learning from People
DESCRIPTION:Abstract:\nLearning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk\, I describe progress on these challenges in the context of two canonical settings\, namely those of ranking and classification. In addressing the first challenge\, I introduce a class of “permutation-based” models that are considerably richer than classical models\, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models\, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge\, I present a class of “multiplicative” incentive mechanisms\, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice\, yielding several-fold improvements over prior art. \nBio:\nNihar B. Shah is a PhD candidate in the EECS department at the University of California\, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16\, the Berkeley Fellowship 2011-13\, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012\, and the SVC Aiya Medal from the Indian Institute of Science for the best master’s thesis in the department. His research interests include statistics and machine learning\, with a current focus on applications to learning from people.
URL:https://idss-stage.mit.edu/calendar/learning-from-people/
LOCATION:34-401 (Grier Room)\, The Stata Center (34-401)\, 50 Vassar Street\, Cambridge\, 02139\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
END:VCALENDAR