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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171103T110000
DTEND;TZID=America/New_York:20171103T120000
DTSTAMP:20260407T143733
CREATED:20171120T200525Z
LAST-MODIFIED:20171120T200525Z
UID:7014-1509706800-1509710400@idss-stage.mit.edu
SUMMARY:Statistics\, Computation and Learning with Graph Neural Networks
DESCRIPTION:Abstract: \nDeep Learning\, thanks mostly to Convolutional architectures\, has recently transformed computer vision and speech recognition. Their ability to encode geometric stability priors\, while offering enough expressive power\, is at the core of their success. In such settings\, geometric stability is expressed in terms of local deformations\, and it is enforced thanks to localized convolutional operators that separate the estimation into scales. \nMany problems across applied sciences\, from particle physics to recommender systems\, are formulated in terms of signals defined over non-Euclidean geometries\, and also come with strong geometric stability priors. In this talk\, I will present techniques that exploit geometric stability in general geometries with appropriate graph neural network architectures. We will show that these techniques can all be framed in terms of local graph generators such as the graph Laplacian. We will present some stability certificates\, as well as applications to computer graphics\, particle physics and graph estimation problems. In particular\, we will describe how graph neural networks can be used to reach statistical detection thresholds in community detection on random graph families\, and attack hard combinatorial optimization problems\, such as the Quadratic Assignment Problem. \nBiography: \nJoan Bruna graduated from Universitat Politecnica de Catalunya (Barcelona\, Spain) in both Mathematics and Electrical Engineering. He obtained an M.Sc. in applied mathematics from ENS Cachan (France). He then became a research engineer in an image processing startup\, developing real-time video processing algorithms. He obtained his PhD in Applied Mathematics at Ecole Polytechnique (France). He was a postdoctoral researcher at the Courant Institute\, NYU\, New York\, and a fellow at Facebook AI Research. In 2015\, he became Assistant Professor at UC Berkeley\, Statistics Department\, and starting Fall 2016 he joined the Courant Institute (NYU\, New York) as Assistant Professor in Computer Science\, Data Science and Mathematics (affiliated). His research interests include invariant signal representations\, high-dimensional statistics and stochastic processes\, deep learning and its applications to signal processing.
URL:https://idss-stage.mit.edu/calendar/statistics-computation-and-learning-with-graph-neural-networks/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171101T110000
DTEND;TZID=America/New_York:20171101T120000
DTSTAMP:20260407T143733
CREATED:20171120T181051Z
LAST-MODIFIED:20171120T192008Z
UID:7007-1509534000-1509537600@idss-stage.mit.edu
SUMMARY:Unbiased Markov chain Monte Carlo with couplings
DESCRIPTION:Abstract: Markov chain Monte Carlo methods provide consistent approximations of integrals as the number of iterations goes to infinity. However\, these estimators are generally biased after any fixed number of iterations\, which complicates both parallel computation. In this talk I will explain how to remove this burn-in  bias by using couplings of Markov chains and a telescopic sum argument\, inspired by Glynn & Rhee (2014). The resulting unbiased estimators can be computed independently in parallel\, and averaged. I will present coupling constructions for Metropolis-Hastings\, Gibbs and Hamiltonian Monte Carlo. The proposed methodology will be illustrated on various examples. If time permits\, I will describe how the proposed estimators can approximate the “cut” distribution that arises in Bayesian inference for misspecified models made of sub-models. \nThis is joint work with John O’Leary\, Yves F. Atchade and Jeremy Heng\,\navailable at arxiv.org/abs/1708.03625 and arxiv.org/abs/1709.00404. \nBiography: Pierre Jacob is an Assistant Professor of Statistics at Harvard University since 2015. Pierre was before a postdoctoral research fellow at the University of Oxford and the National University of Singapore. His Ph.D. was from Université Paris-Dauphine on computational methods for Bayesian inference. His current research is on algorithms amenable to parallel computing for Bayesian inference and model comparison\, with a focus on time series models. \nPierre E. Jacob\nAssistant Professor of Statistics\, Harvard University\npersonal website: sites.google.com/site/pierrejacob/\nblog: statisfaction.wordpress.com/
URL:https://idss-stage.mit.edu/calendar/unbiased-markov-chain-monte-carlo-with-couplings/
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=America/New_York:20171031T160000
DTEND;TZID=America/New_York:20171031T170000
DTSTAMP:20260407T143733
CREATED:20171002T154828Z
LAST-MODIFIED:20190501T144833Z
UID:6535-1509465600-1509469200@idss-stage.mit.edu
SUMMARY:Structure\, Randomness and Universality
DESCRIPTION:What is the minimum possible number of vertices of a graph that contains every k-vertex graph as an induced subgraph? What is the minimum possible number of edges in a graph that contains every k-vertex graph with maximum degree 3 as a subgraph? These questions and related one were initiated by Rado in the 60s\, and received a considerable amount of attention over the years\, partly motivated by algorithmic applications. The study of the subject combines probabilistic arguments and explicit\, structured constructions. I will survey the topic focusing on a recent asymptotic solution of the first question\, where an asymptotic formula\, improving earlier estimates by several researchers\, is obtained by combining combinatorial and probabilistic arguments with group theoretic tools. \nBio: Noga Alon is a Baumritter Professor of Mathematics and Computer Science in Tel Aviv University\, Israel. He received his Ph. D. in Mathematics at the Hebrew University of Jerusalem in 1983 and had visiting positions in various research institutes including MIT\, the Institute for Advanced Study in Princeton\, IBM Almaden Research Center\, Bell Laboratories\, Bellcore and Microsoft Research. He joined Tel Aviv University in 1985\, served as the head of the School of Mathematical Sciences in 1999-2000\, and supervised about 20 PhD students. Since 2009 he is also a member of Microsoft Research\, Israel. He serves on the editorial boards of more than a dozen international technical journals and has given invited lectures in many conferences\, including plenary addresses in the 1996 European Congress of Mathematics and in the 2002 International Congress of Mathematician. He published more than five hundred research papers and one book. \n\n\nHis research interests are mainly in Combinatorics\, Graph Theory and their applications in Theoretical Computer Science. His main contributions include the study of expander graphs and their applications\, the investigation of derandomization techniques\, the foundation of streaming algorithms\, the development and applications of algebraic and probabilistic methods in Discrete Mathematics and the study of problems in Information Theory\, Combinatorial Geometry and Combinatorial Number Theory. \nHe is an ACM Fellow and an AMS Fellow\, a member of the Israel Academy of Sciences and Humanities since 1997 and of the Academia Europaea since 2008\, and received the Erdös prize in 1989\, the Feher prize in 1991\, the Polya Prize in 2000\, the Bruno Memorial Award in 2001\, the Landau Prize in 2005\, the Gödel Prize in 2005\, the Israel Prize in 2008\, the EMET Prize in 2011\, the Dijkstra Prize in 2016\, an Honorary Doctorate from ETH Zurich in 2013 and from the University of Waterloo in 2015. \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/joint-seminar-csail-theory-computation-toc
LOCATION:32-G449 (Kiva)\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171027T110000
DTEND;TZID=America/New_York:20171027T120000
DTSTAMP:20260407T143733
CREATED:20171002T194208Z
LAST-MODIFIED:20171120T180403Z
UID:6559-1509102000-1509105600@idss-stage.mit.edu
SUMMARY:Stochastics and Statistics Seminar - Amit Daniely (Google)
DESCRIPTION:Abstract:  \nCan learning theory\, as we know it today\, form a theoretical basis for neural networks. I will try to discuss this question in light of two new results — one positive and one negative. \nBased on joint work with Roy Frostig\, Vineet Gupta and Yoram Singer\, and with Vitaly Feldman \nBiography: \nAmit Daniely is an Assistant Professor at the Hebrew University in Jerusalem\, and a research scientist at Google Research\, Tel-Aviv. Prior to that\, he was a research scientist at Google Research\, Mountain-View. Even prior to that\, he was a Ph.D. student at the Hebrew University of Jerusalem\, Israel\, supervised by Nati Linial and Shai Shalev-Shwartz. His main research interest is Machine Learning Theory.
URL:https://idss-stage.mit.edu/calendar/stochastic-and-statistics-seminar-amit-daniely-google/
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=America/New_York:20171024T160000
DTEND;TZID=America/New_York:20171024T170000
DTSTAMP:20260407T143733
CREATED:20171002T154138Z
LAST-MODIFIED:20190501T145009Z
UID:6530-1508860800-1508864400@idss-stage.mit.edu
SUMMARY:Regularized Nonlinear Acceleration
DESCRIPTION:We describe a convergence acceleration technique for generic optimization problems. Our scheme computes estimates of the optimum from a nonlinear average of the iterates produced by any optimization method. The weights in this average are computed via a simple linear system\, whose solution can be updated online. This acceleration scheme runs in parallel to the base algorithm\, providing improved estimates of the solution on the fly\, while the original optimization method is running. Numerical experiments are detailed on classical classification problems. \nBio: After dual PhDs from Ecole Polytechnique and Stanford University in optimisation and finance\, followed by a postdoc at U.C. Berkeley\, Alexandre d’Aspremont joined the faculty at Princeton University as an assistant then associate professor with joint appointments at the ORFE department and the Bendheim Center for Finance. He returned to Europe in 2011 thanks to a grant from the European Research Council and is now a research director at CNRS\, attached to Ecole Normale Supérieure in Paris. His research focuses on convex optimization and applications to machine learning\, statistics and finance. \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/alexandre-tsybakov-ensae-paristech
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;VALUE=DATE:20171023
DTEND;VALUE=DATE:20171111
DTSTAMP:20260407T143733
CREATED:20171025T184757Z
LAST-MODIFIED:20171107T230144Z
UID:6765-1508716800-1510358399@idss-stage.mit.edu
SUMMARY:Data Science Course Launches - open registration extended to November 10
DESCRIPTION:Every day\, your organization generates new data on your customers\, your processes\, and your industry. But could you be using this data more effectively? Developed by over ten MIT faculty members at the MIT Institute for Data\, Systems and Society (IDSS)\, this course is specially designed for professionals looking to learn the latest theories and strategies to harness data.
URL:https://idss-stage.mit.edu/calendar/data-science-course-launches-open-registration-extended/
ATTACH;FMTTYPE=image/png:https://idss-stage.mit.edu/wp-content/uploads/2017/10/Screen-Shot-2017-10-25-at-2.50.28-PM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171020T110000
DTEND;TZID=America/New_York:20171020T120000
DTSTAMP:20260407T143733
CREATED:20171002T193921Z
LAST-MODIFIED:20171006T202431Z
UID:6555-1508497200-1508500800@idss-stage.mit.edu
SUMMARY:Inference in dynamical systems and the geometry of learning group actions - Sayan Mukherjee (Duke)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/inference-in-dynamical-systems-and-the-geometry-of-learning-group-actions-sayan-mukherjee-duke/
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:20171019T163000
DTEND;TZID=UTC:20171019T173000
DTSTAMP:20260407T143733
CREATED:20170831T230110Z
LAST-MODIFIED:20171002T193958Z
UID:6078-1508430600-1508434200@idss-stage.mit.edu
SUMMARY:Special Stochastics and Statistics Seminar - John Cunningham (Columbia)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/special-stochastics-and-statistics-seminar-john-cunningham-columbia/
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=America/New_York:20171017T160000
DTEND;TZID=America/New_York:20171017T170000
DTSTAMP:20260407T143733
CREATED:20171002T153935Z
LAST-MODIFIED:20190501T145140Z
UID:6528-1508256000-1508259600@idss-stage.mit.edu
SUMMARY:The Maps Inside Your Head
DESCRIPTION:How do our brains make sense of a complex and unpredictable world? In this talk\, I will discuss an information theory approach to the neural topography of information processing in the brain. First I will review the brain’s architecture\, and how neural circuits map out the sensory and cognitive worlds. Then I will describe how highly complex sensory and cognitive tasks are carried out by the cooperative action of many specialized neurons and circuits\, each of which has a simple function. I will illustrate my remarks with one sensory example and one cognitive example. For the sensory example\, I will consider the sense of smell (“olfaction”)\, whereby humans and other animals distinguish vast arrays of odor mixtures using very limited neural resources. For the cognitive example\, I will consider the “sense of place”\, that is\, how animals mentally represent their physical location. Both examples demonstrate that brains have evolved neural circuits that exploit sophisticated principles of mathematics and information processing – principles that scientists have only recently discovered. \nBio: Vijay Balasubramanian is the Cathy and Marc Lasry Professor in the Physics Department at the University of Pennsylvania\, where he is also Director of the Computational Neuroscience Initiative. He received B.Sc. degrees in Physics and Computer Science\, and an M.Sc. in Computer Science\, from MIT. He earned a Ph.D. in Theoretical Physics at Princeton University\, and was a Junior Fellow of the Harvard Society of Fellows. \n\n\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/maps-inside-your-head
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:20171013T110000
DTEND;TZID=America/New_York:20171013T120000
DTSTAMP:20260407T143733
CREATED:20171002T182143Z
LAST-MODIFIED:20171006T201516Z
UID:6549-1507892400-1507896000@idss-stage.mit.edu
SUMMARY:Additivity of Information in Deep Generative Network:  The I-MMSE Transform Method - Galen Reeves (Duke University)
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/additivity-of-information-in-deep-generative-network-the-i-mmse-transform-method-galen-reeves-duke-university/
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:20171012T143000
DTEND;TZID=UTC:20171012T153000
DTSTAMP:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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:20260407T143733
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
END:VCALENDAR