BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IDSS - ECPv4.6//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IDSS
X-ORIGINAL-URL:https://idss.mit.edu
X-WR-CALDESC:Events for IDSS
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190222T110000
DTEND;TZID=America/New_York:20190222T120000
DTSTAMP:20190218T100805
CREATED:20190204T175935Z
LAST-MODIFIED:20190213T164650Z
UID:8812-1550833200-1550836800@idss.mit.edu
SUMMARY:Capacity lower bound for the Ising perceptron
DESCRIPTION: Abstract: \nThe perceptron is a toy model of a simple neural network that stores a collection of given patterns. Its analysis reduces to a simple problem in high-dimensional geometry\, namely\, understanding the intersection of the cube (or sphere) with a collection of random half-spaces. Despite the simplicity of this model\, its high-dimensional asymptotics are not well understood. I will describe what is known and present recent results. \nThis is joint work with Jian Ding. \n Biography: \nNike Sun is a faculty member in the MIT mathematics department. \n
URL:https://stat.mit.edu/calendar/capacity-lower-bound-ising-perceptron-nikesun/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190301T110000
DTEND;TZID=America/New_York:20190301T120000
DTSTAMP:20190218T100805
CREATED:20190204T180630Z
LAST-MODIFIED:20190204T181211Z
UID:8814-1551438000-1551441600@idss.mit.edu
SUMMARY:Why Aren’t Network Statistics Accompanied By Uncertainty Statements?
DESCRIPTION: Abstract: \nOver 500K scientific articles have been published since 1999 with the word “network” in the title. And the vast majority of these report network summary statistics of one type or another. However\, these numbers are rarely accompanied by any quantification of uncertainty. Yet any error inherent in the measurements underlying the construction of the network\, or in the network construction procedure itself\, necessarily must propagate to any summary statistics reported. Perhaps surprisingly\, there is little in the way of formal statistical methodology for this problem. I summarize results from our recent work\, for the case of estimating the density of low-order subgraphs. Under a simple model of network error\, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed. We then develop method-of-moment estimators of subgraph density and error rates for the case where a minimal number of network replicates are available (i.e.\, just 2 or 3). These estimators are shown to be asymptotically normal as the number of vertices increases to infinity. We also provide confidence intervals for quantifying the uncertainty in these estimates\, implemented through a novel bootstrap algorithm. We illustrate the use of our estimators in the context of gene coexpression networks — the correction for measurement error is found to have potentially substantial impact on standard summary statistics. This is joint work with Qiwei Yao and Jinyuan Chang. \n Biography: \nEric Kolaczyk is a Professor of Statistics and Director of the Program in Statistics in the Department of Mathematics & Statistics at Boston University. He is also a university Data Science Faculty Fellow\, and affiliated with the Division of Systems Engineering and the Programs in Bioinformatics and in Computational Neuroscience. His current research interests revolve mainly around the statistical analysis of network-indexed data\, including both theory/methods development and collaborative research. He has published several books on the topic of network analysis. As an associate editor\, he has served on the boards of JASA and JRSS-B in statistics\, IEEE IP and TNSE in engineering\, and SIMODS in mathematics. Currently he is the co-chair of the NAS Roundtable on Data Science Education. He is an elected fellow of the AAAS\, ASA\, and IMS\, an elected senior member of the IEEE\, and an elected member of the ISI. \n
URL:https://stat.mit.edu/calendar/arent-network-statistics-accompanied-uncertainty-statements-erickolaczyk/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190304T080000
DTEND;TZID=America/New_York:20190304T170000
DTSTAMP:20190218T100805
CREATED:20180717T190526Z
LAST-MODIFIED:20181204T161507Z
UID:8040-1551686400-1551718800@idss.mit.edu
SUMMARY:Women in Data Science (WiDS) – Cambridge\, MA
DESCRIPTION:This one-day technical conference will bring together local academic leaders\, industrial professionals and students to hear about the latest data science-related research in a number of domains\, to learn how leading-edge companies are leveraging data science for success\, and to connect with potential mentors\, collaborators\, and others in the field. \n
URL:http://www.widscambridge.org
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190305T160000
DTEND;TZID=America/New_York:20190305T170000
DTSTAMP:20190218T100805
CREATED:20190129T144952Z
LAST-MODIFIED:20190213T170038Z
UID:8791-1551801600-1551805200@idss.mit.edu
SUMMARY:IDSS Distinguished Speaker Seminar - Sanjeev Arora
DESCRIPTION:Detailed information will be posted soon. \nAbout the Speaker: Sanjeev Arora is the Charles C. Fitzmorris Professor in Computer Science. He joined Princeton in 1994 after earning his doctorate from the University of California\, Berkeley. He was a visiting professor at the Weizmann Institute in 2007\, a visiting researcher at Microsoft in 2006-07\, and a visiting associate professor at Berkeley during 2001-02. Professor Arora’s honors include the D.R. Fulkerson Prize in Discrete Mathematics (awarded by the American Mathematical Society and Math Optimization Society) in 2012\, the ACM-Infosys Foundation Award in the Computing Sciences in the same year\, the Best paper award from IEEE Foundations of Computer Science in 2010\, and the EATCS-SIGACT Gödel Prize (cowinner)\, also in 2010. He was appointed a Simons Foundation investigator in 2012\, and was elected an ACM fellow in 2009. Professor Arora was the founding director and lead PI at the Center for Computational Intractability in 2008\, a project funded partly by an NSF Expeditions in Computing grant. \n
URL:https://idss.mit.edu/calendar/idss-distinguished-speaker-seminar-march/
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190308T110000
DTEND;TZID=America/New_York:20190308T120000
DTSTAMP:20190218T100805
CREATED:20190204T181505Z
LAST-MODIFIED:20190204T181505Z
UID:8816-1552042800-1552046400@idss.mit.edu
SUMMARY:Univariate total variation denoising\, trend filtering and multivariate Hardy-Krause variation denoising
DESCRIPTION: Abstract: \nTotal variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted function estimation. The results are based on joint work with Sabyasachi Chatterjee\, Billy Fang\, Donovan Lieu and Bodhisattva Sen. \n Biography: \nAditya Guntuboyina is currently an Associate Professor at the Department of Statistics\, UC Berkeley. He has been at Berkeley since 2012 after finishing his PhD in Statistics from Yale University and a postdoctoral position at the Wharton Statistics Department in the University of Pennsylvania. His research interests include nonparametric and high-dimensional statistics\, shape constrained statistical estimation\, empirical processes and statistical information theory. His research is currently supported by an NSF CAREER award. \n
URL:https://stat.mit.edu/calendar/univariate-total-variation-denoising-trend-filtering-multivariate-hardy-krause-variation-denoising-adityaguntuboyina/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190322T110000
DTEND;TZID=America/New_York:20190322T120000
DTSTAMP:20190218T100805
CREATED:20190204T195726Z
LAST-MODIFIED:20190206T173542Z
UID:8818-1553252400-1553256000@idss.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series
DESCRIPTION:
URL:https://stat.mit.edu/calendar/tbd-eliransubag/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190402T160000
DTEND;TZID=America/New_York:20190402T170000
DTSTAMP:20190218T100805
CREATED:20190129T145834Z
LAST-MODIFIED:20190214T133142Z
UID:8793-1554220800-1554224400@idss.mit.edu
SUMMARY:A Particulate Solution: Data Science in the Fight to Stop Air Pollution and Climate Change | IDSS Distinguished Speaker Seminar
DESCRIPTION:Abstract:\nWhat if I told you I had evidence of a serious threat to American national security – a terrorist attack in which a jumbo jet will be hijacked and crashed every 12 days. Thousands will continue to die unless we act now. This is the question before us today – but the threat doesn’t come from terrorists. The threat comes from climate change and air pollution. \nWe have developed an artificial neural network model that uses on-the-ground air-monitoring data and satellite-based measurements to estimate daily pollution levels across the continental U.S.\, breaking the country up into 1-square-kilometer zones. We have paired that information with health data contained in Medicare claims records from the last 12 years\, and for 97% of the population ages 65 or older. We have developed statistical methods and computational efficient algorithms for the analysis over 460 million health records. \nOur research shows that short and long term exposure to air pollution is killing thousands of senior citizens each year. This data science platform is telling us that federal limits on the nation’s most widespread air pollutants are not stringent enough. \nThis type of data is the sign of a new era for the role of data science in public health\, and also for the associated methodological challenges. For example\, with enormous amounts of data\, the threat of unmeasured confounding bias is amplified\, and causality is even harder to assess with observational studies. These and other challenges will be discussed. \nReferences:\nDi Q\, Wang Y\, Zanobetti A\, Wang Y\, Koutrakis P\, Dominici F\, Schwartz J. (2017). Air Pollution and Mortality in the Medicare Population. New England Journal of Medicine\, 376:2513-2522\, June 29\, 2017\, DOI: 10.1056/NEJMoa1702747\nDi Q\, Dai L\, Wang Y\, Zanobetti A\, Dominici F\, Schwartz J. (2017) A Nationwide Case-crossover Study on Air Pollution and Mortality in the United States\, 2000-2012\, Journal of American Medical Association\, AMA. 2017;318(24):2446-2456. doi:10.1001/jama.2017.17923 \nAbout the Speaker:\nFrancesca Dominici is Professor of Biostatistics at the Harvard T.H.Chan School of Public Health and co-Director of the Harvard Data Science Initiative. \nHer research focuses on the development of statistical methods for the analysis of large and complex data; she leads several interdisciplinary groups of scientists with the ultimate goal of addressing important questions in environmental health science\, climate change\, comparative effectiveness research in cancer\, and health policy. Currently\, Dominici’s team uses satellite data and multiple data sources to estimate exposure to air pollution in rural areas in the US\, in India\, and in other developing countries. Her studies have directly and routinely impacted air quality policy and led to more stringent ambient air quality standards in the United States. \n \nDominici was recognized on the Thomson Reuters 2015 Highly Cited Researchers list\, ranking in the top 1 percent of scientists cited in her field. In 2017\, she was named one of the top 10 Italian women scientists with the largest impact in biomedical sciences across the world. In addition to her research interests and administrative leadership roles\, Dominici has demonstrated a career-long commitment to promoting diversity in academia. For her contributions\, she has earned the Jane L. Norwood Award for Outstanding Achievement by a Woman in the Statistical Sciences and the Florence Nightingale David Award. Dominici currently chairs the University Committee for the Advancement of Women Faculty at the Harvard T.H. Chan School of Public Health. Prior to Harvard\, she was on the faculty of the Johns Hopkins Bloomberg School of Public Health\, where she also co-chaired the University Committee on the Status of Women. Dominici has degrees from University La Sapienza and University of Padua. \n \nPress coverage links\nNPR: http://www.npr.org/sections/health-shots/2017/06/28/534594373/u-s-air-pollution-still-kills-thousands-every-year-study-concludes\nLos Angeles Times: http://www.latimes.com/science/sciencenow/la-sci-sn-air-pollution-death-20170628-story.html\nNew York Times: https://www.nytimes.com/2017/06/28/well/even-safe-pollution-levels-can-be-deadly.html?_r=0\nPodcast: https://www.hsph.harvard.edu/news/multimedia-article/harvard-chan-this-week-in-health-archive/ \n
URL:https://idss.mit.edu/calendar/idss-distinguished-speaker-seminar-francesca-dominici/
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190405T090000
DTEND;TZID=America/New_York:20190405T170000
DTSTAMP:20190218T100805
CREATED:20180717T190056Z
LAST-MODIFIED:20181204T161855Z
UID:8034-1554454800-1554483600@idss.mit.edu
SUMMARY:SDSCon2019
DESCRIPTION:SDSCon 2019 is the third annual celebration of the statistics and data science community at MIT and beyond\, organized by MIT’s Statistics and Data Science Center (SDSC). \n
URL:http://sdsc2019.mit.edu
LOCATION:20 Ames Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190412T110000
DTEND;TZID=America/New_York:20190412T120000
DTSTAMP:20190218T100805
CREATED:20190204T202500Z
LAST-MODIFIED:20190206T173126Z
UID:8820-1555066800-1555070400@idss.mit.edu
SUMMARY:Exponential line-crossing inequalities
DESCRIPTION: Abstract: \nThis talk will present a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities in this way. We will illustrate this point by presenting a single assumption and a single theorem that together strengthen many tail bounds for martingales\, including classical inequalities (1960-80) by Bernstein\, Bennett\, Hoeffding\, and Freedman; contemporary inequalities (1980-2000) by Shorack and Wellner\, Pinelis\, Blackwell\, van de Geer\, and de la Pena; and several modern inequalities (post-2000) by Khan\, Tropp\, Bercu and Touati\, Delyon\, and others. In each of these cases\, we give the strongest and most general statements to date\, quantifying the time-uniform concentration of scalar\, matrix\, and Banach-space-valued martingales\, under a variety of nonparametric assumptions in discrete and continuous time. In doing so\, we bridge the gap between existing line-crossing inequalities\, the sequential probability ratio test\, the Cramer-Chernoff method\, self-normalized processes\, and other parts of the literature. Time permitting\, I will briefly discuss applications to sequential covariance matrix estimation\, adaptive clinical trials and multi-armed bandits via the notion of “confidence sequences”. \n(joint work with Steve Howard\, Jas Sekhon and Jon McAuliffe\, preprint https://arxiv.org/abs/1808.03204) \n Biography: \nAaditya Ramdas is an assistant professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. Previously\, he was a postdoctoral researcher in Statistics and EECS at UC Berkeley from 2015-18\, mentored by Michael Jordan and Martin Wainwright. He finished his PhD at CMU in Statistics and Machine Learning\, advised by Larry Wasserman and Aarti Singh\, winning the Best Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay. A lot of his research focuses on modern aspects of reproducibility in science and technology — involving statistical testing and false discovery rate control in static and dynamic settings. He also works on some problems in sequential decision-making and online uncertainty quantification \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/tbd-aadityaramdas/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190419T110000
DTEND;TZID=America/New_York:20190419T120000
DTSTAMP:20190218T100805
CREATED:20190204T202923Z
LAST-MODIFIED:20190206T173441Z
UID:8822-1555671600-1555675200@idss.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series
DESCRIPTION:
URL:https://stat.mit.edu/calendar/tbd-aaronroth/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190426T110000
DTEND;TZID=America/New_York:20190426T120000
DTSTAMP:20190218T100805
CREATED:20190204T203212Z
LAST-MODIFIED:20190204T203212Z
UID:8824-1556276400-1556280000@idss.mit.edu
SUMMARY:TBD
DESCRIPTION:
URL:https://stat.mit.edu/calendar/tbd-chaogao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190503T110000
DTEND;TZID=America/New_York:20190503T120000
DTSTAMP:20190218T100805
CREATED:20190204T203624Z
LAST-MODIFIED:20190206T173354Z
UID:8827-1556881200-1556884800@idss.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series
DESCRIPTION:
URL:https://stat.mit.edu/calendar/tbd-tracyke/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190507T160000
DTEND;TZID=America/New_York:20190507T170000
DTSTAMP:20190218T100805
CREATED:20190129T150047Z
LAST-MODIFIED:20190129T150047Z
UID:8800-1557244800-1557248400@idss.mit.edu
SUMMARY:IDSS Distinguished Speaker Seminar - May
DESCRIPTION:Detailed information will be posted soon. \n
URL:https://idss.mit.edu/calendar/idss-distinguished-speaker-seminar-may/
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190510T080000
DTEND;TZID=America/New_York:20190510T170000
DTSTAMP:20190218T100805
CREATED:20190204T204606Z
LAST-MODIFIED:20190206T172824Z
UID:8832-1557475200-1557507600@idss.mit.edu
SUMMARY:Counting and sampling at low temperatures
DESCRIPTION: Abstract: \nWe consider the problem of efficient sampling from the hard-core and Potts models from statistical physics. On certain families of graphs\, phase transitions in the underlying physics model are linked to changes in the performance of some sampling algorithms\, including Markov chains. We develop new sampling and counting algorithms that exploit the phase transition phenomenon and work efficiently on lattices (and bipartite expander graphs) at sufficiently low temperatures in the phase coexistence regime. Our algorithms are based on Pirogov-Sinai theory and the cluster expansion\, classical tools from statistical physics. Joint work with Tyler Helmuth and Guus Regts. \n Biography: \nWill Perkins is an assistant professor in the Department of Mathematics\, Statistics\, and Computer Science at the University of Illinois at Chicago. His research interests are in probability\, combinatorics\, and algorithms. He received his PhD in 2011 from New York University\, then was a postdoc at Georgia Tech and faculty at the University of Birmingham before moving to UIC in 2018. \nMIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar. \n
URL:https://stat.mit.edu/calendar/tbd-willperkins/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
END:VCALENDAR