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DTSTART;TZID=America/New_York:20181012T110000
DTEND;TZID=America/New_York:20181012T120000
DTSTAMP:20260408T005831
CREATED:20180621T191525Z
LAST-MODIFIED:20181004T134355Z
UID:7910-1539342000-1539345600@idss-stage.mit.edu
SUMMARY:Locally private estimation\, learning\, inference\, and optimality
DESCRIPTION:Abstract: In this talk\, we investigate statistical learning and estimation under local privacy constraints\, where data providers do not trust the collector of the data and so privatize their data before it is even collected. We identify fundamental tradeoffs between statistical utility and privacy in such local models of privacy\, providing instance-specific bounds for private estimation and learning problems by developing local minimax risks. In contrast to approaches based on worst-case (minimax) error\, which are conservative\, this allows us to evaluate the difficulty of individual problem instances and delineate the possibilities for adaptation in private estimation and inference. As part of this\, we identify an alternative to the Fisher information for private estimation\, giving a more nuanced understanding of the challenges of adaptivity and optimality. We also provide optimal procedures for private inference\, highlighting the importance of a more careful development of optimal tradeoffs between estimation and privacy. One consequence of our results is to identify settings where standard local privacy restrictions may be too strong for practice; time permitting\, I will then discuss a few new directions that maintain limited amounts of privacy while simultaneously allowing the development of high-performance statistical and learning procedures.\nBased on joint work with Feng Ruan.\n\nBiography: John Duchi is an assistant professor of Statistics and Electrical Engineering and (by courtesy) Computer Science at Stanford University\, with graduate degrees from UC Berkeley and undergraduate degrees from Stanford. His work focuses on large scale optimization problems arising out of statistical and machine learning problems\, robustness and uncertain data problems\, and information theoretic aspects of statistical learning. He has won a number of awards and fellowships\, including best paper awards at the Neural Information Processing Systems conference\, the International Conference on Machine Learning\, an NSF CAREER award\, a Sloan Fellowship in Mathematics\, the Okawa Foundation Award\, and the Association for Computing Machinery (ACM) Doctoral Dissertation Award (honorable mention).
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-14/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181011T160000
DTEND;TZID=America/New_York:20181011T170000
DTSTAMP:20260408T005831
CREATED:20181010T144504Z
LAST-MODIFIED:20181010T145040Z
UID:8389-1539273600-1539277200@idss-stage.mit.edu
SUMMARY:Local Geometric Analysis and Applications
DESCRIPTION:Abstract: Local geometric analysis is a method to define a coordinate system in a small neighborhood in the space of distributions over a given alphabet. It is a powerful technique since the notions of distance\, projection\, and inner product defined this way are useful in the optimization problems involving distributions\, such as regressions. It has been used in many places in the literature such as correlation analysis\, correspondence analysis. In this talk\, we will go through some of the basic setups and properties\, and discuss a few applications in information theory\, dimension reduction and softmax regression. \n About this Seminar: This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory\, inference\, causality\, estimation\, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers\, and with the exception of the two lectures on randomness and information\, there is no planned coherence or dependency among them. Ad hoc follow-on meetings about any of the topics presented are highly encouraged.
URL:https://idss-stage.mit.edu/calendar/local-geometric-analysis-and-applications/
LOCATION:32-D677\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181008T093000
DTEND;TZID=America/New_York:20181008T163000
DTSTAMP:20260408T005831
CREATED:20181002T125917Z
LAST-MODIFIED:20181002T130105Z
UID:8310-1538991000-1539016200@idss-stage.mit.edu
SUMMARY:HUBweek Policy Hackathon
DESCRIPTION:
URL:https://www.mitpolicyhackathon.org/hubweek/
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181005T110000
DTEND;TZID=America/New_York:20181005T120000
DTSTAMP:20260408T005831
CREATED:20180621T191105Z
LAST-MODIFIED:20181004T152601Z
UID:7908-1538737200-1538740800@idss-stage.mit.edu
SUMMARY:Efficient Algorithms for the Graph Matching Problem in Correlated Random Graphs
DESCRIPTION:Abstract:  The Graph Matching problem is a robust version of the Graph Isomorphism problem: given two not-necessarily-isomorphic graphs\, the goal is to find a permutation of the vertices which maximizes the number of common edges. We study a popular average-case variant; we deviate from the common heuristic strategy and give the first quasi-polynomial time algorithm\, where previously only sub-exponential time algorithms were known.\nBased on joint work with Boaz Barak\, Chi-Ning Chou\, Zhixian Lei\, and Yueqi Sheng.\n\n\nBiography: Tselil Schramm is a postdoc in theoretical computer science at Harvard and MIT\, hosted by Boaz Barak\, Jon Kelner\, Ankur Moitra\, and Pablo Parrilo. She obtained her PhD in computer science from U.C. Berkeley under the advisement of Prasad Raghavendra and Satish Rao. Her research interests include inference and average-case problems\, optimization via convex programs (especially the sum-of-squares hierarchy)\, spectral algorithms\, spectral graph theory\, and more.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-13/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180928T110000
DTEND;TZID=America/New_York:20180928T120000
DTSTAMP:20260408T005831
CREATED:20180621T194142Z
LAST-MODIFIED:20180919T020411Z
UID:7928-1538132400-1538136000@idss-stage.mit.edu
SUMMARY:Jingbo Liu
DESCRIPTION:Abstract  Concentration of measure refers to a collection of tools and results from analysis and probability theory that have been used in many areas of pure and applied mathematics. Arguably\, the first data science application of measure concentration (under the name ‘‘blowing-up lemma’’) is the proof of strong converses in multiuser information theory by Ahlswede\, G’acs and K”orner in 1976. Since then\, measure concentration has found applications in many other information theoretic problems\, most notably the converse (impossibility) results in information theory. Motivated by this\, information theorists (e.g. Marton) have also contributed to the mathematical foundations of measure concentration using their information-theoretic techniques. \nNow\, after all the past 40 years of such progress\, we found that\, amusingly\, measure concentration is not the right hammer for many of these information theoretic applications. We introduce a new machinery based on functional inequalities and reverse hypercontractivity which yields strict improvements in terms of sharpness of the bounds\, generality of the source/channel distributions\, and simplicity of the proofs. Examples covered in the talk include: 1. optimal second-order converses to distributed source-type problems (hypothesis testing\, common randomness generation\, and source coding); 2. sharpening the recent relay channel converse bounds by Wu and Ozgur with much simpler proofs. \nThe work benefited from collaborations with Thomas Courtade\, Paul Cuff\, Ayfer Ozgur\, Ramon van Handel\, and Sergio Verd’u \n Biography:  jingbo Liu received the B.E. degree from Tsinghua University\, Beijing\, China in 2012\, and the M.A. and Ph.D. degrees from Princeton University\, Princeton\, NJ\, USA\, in 2014 and 2018\, all in electrical engineering. His research interests include signal processing\, information theory\, coding theory\, high dimensional statistics\, and the related fields. His undergraduate thesis received the best undergraduate thesis award at Tsinghua University (2012). He gave a semi-plenary presentation at the 2015 IEEE Int. Symposium on Information Theory\, Hong-Kong\, China. He was a recipient of the Princeton University Wallace Memorial Honorific Fellowship in 2016.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-12/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180925T160000
DTEND;TZID=America/New_York:20180925T170000
DTSTAMP:20260408T005831
CREATED:20180726T171445Z
LAST-MODIFIED:20180904T145459Z
UID:8078-1537891200-1537894800@idss-stage.mit.edu
SUMMARY:Text as Data in Social Science: Discovery\, Measurement and Causal Inference
DESCRIPTION:Social scientists are increasingly turning to computer-assisted text analysis as a way of understanding the digital footprints left by communities and individuals.  Much of the technology that powers these approaches is borrowed from the fields of computer science and statistics; yet\, social scientists have substantially different goals.  We focus on the development of methods that support three core tasks: discovery\, measurement and causal inference with text.  We introduce the Structural Topic Model (STM)\, a bayesian generative model of text which is built for social science inference.  Using this model as a running example\, we will discuss the challenges of discovery\, measurement and causal inference and how to adapt our tools to approach each task.  The tasks will be illustrated with multiple examples across many different domains.  The talk will end with future directions for this fast-moving\, inter-disciplinary field. [Includes joint work with Molly Roberts\, Justin Grimmer\, Dustin Tingley\, Edo Airoldi\, Richard Nielsen and others.]\nAbout the speaker: Brandon Stewart is an Assistant Professor in the Department of Sociology and is also affiliated with the Department of Politics and the Office of Population Research. He develops new quantitative statistical methods for applications across the social sciences. Methodologically his focus is in tools which facilitate automated text analysis and model complex heterogeneity in regression. Many recent applications of these methods have centered on using large corpora of text to better understand propaganda in contemporary China. His research has been published in journals such as American Journal of Political Science\, Political Analysis and the Proceedings of the Association of Computational Linguistics. His work has won the Edward R Chase Dissertation Prize\, the Gosnell Prize for Excellence in Political Methodology\, and the Political Analysis Editor’s Choice Award.
URL:https://idss-stage.mit.edu/calendar/idss-seminar-brandon-stewart/
LOCATION:32-141\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180924T160000
DTEND;TZID=America/New_York:20180924T170000
DTSTAMP:20260408T005831
CREATED:20180810T155456Z
LAST-MODIFIED:20190501T143856Z
UID:8160-1537804800-1537808400@idss-stage.mit.edu
SUMMARY:The Power of Multiple Samples in Generative Adversarial Networks
DESCRIPTION:We bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953\, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs\, where a trained neural network (called a critic) determines whether a given sample is from the real data or the generated (fake) data. By jointly training the generator and the critic\, the hope is that eventually the trained generator will generate realistic samples. One of the major challenges in GAN is known as “mode collapse”; the lack of diversity in the samples generated by thus trained generators. We propose a new training framework\, where the critic is fed with multiple samples jointly (which we call packing)\, as opposed to each sample separately as done in standard GAN training. With this simple but fundamental departure from standard GANs\, experimental results show that the diversity of the generated samples improve significantly. We analyze this practical gain by first providing a formal mathematical definition of mode collapse and making a fundamental connection between the idea of packing and the intensity of mode collapse. Precisely\, we show that the packed critic naturally penalizes mode collapse\, thus encouraging generators with less mode collapse. The analyses critically rely on operational interpretation of hypothesis testing and corresponding data processing inequalities\, which lead to sharp analyses with simple proofs. For this talk\, I will assume no prior background on GANs. \nThis is joint work with Zinan Lin (CMU)\, Ahsish Khetan (Amazon AI)\, and Giulia Fanti (CMU). \nBio: Sewoong Oh is an Associate Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD\, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. His research interest is in theoretical machine learning\, including spectral methods\, ranking\, crowdsourcing\, estimation of information measures\, differential privacy\, and generative adversarial networks. He was co-awarded the best paper award at the SIGMETRICS in 2015\, NSF CAREER award in 2016 and GOOGLE Faculty Research Award. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-series-1
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180921T110000
DTEND;TZID=America/New_York:20180921T120000
DTSTAMP:20260408T005831
CREATED:20180621T184900Z
LAST-MODIFIED:20180626T141436Z
UID:7904-1537527600-1537531200@idss-stage.mit.edu
SUMMARY:Boaz Nadler
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-11/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180920T160000
DTEND;TZID=America/New_York:20180920T170000
DTSTAMP:20260408T005831
CREATED:20180910T161945Z
LAST-MODIFIED:20180919T022223Z
UID:8249-1537459200-1537462800@idss-stage.mit.edu
SUMMARY:Topics in Information and Inference Seminar
DESCRIPTION:Title: Strong data processing inequalities and information percolation\n\n Abstract: The data-processing inequality\, that is\, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$\, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. A channel-dependent improvement is called the strong data-processing inequality (or SDPI). In this talk we will: a) review SDPIs; b) show how point-to-point SDPIs can be combined into an SDPI for a network; c) show recent applications to problems of statistical inference on graphs (spiked Wigner model\, community detection etc.)
URL:https://idss-stage.mit.edu/calendar/topics-in-information-and-inference-seminar/
LOCATION:32-D677\, United States
CATEGORIES:IDSS Special Seminars
ORGANIZER;CN="":MAILTO:jeannille.hiciano@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180917T160000
DTEND;TZID=America/New_York:20180917T170000
DTSTAMP:20260408T005831
CREATED:20180810T155258Z
LAST-MODIFIED:20190501T143950Z
UID:8158-1537200000-1537203600@idss-stage.mit.edu
SUMMARY:Regret of Queueing Bandits
DESCRIPTION:We consider a variant of the multiarmed bandit (MAB) problem where jobs or tasks queue for service\, and service rates of different servers (agents) may be unknown. Such (queueing+learning) problems are motivated by a vast range of service systems\, including supply and demand in online platforms (e.g.\, Uber\, Lyft\, Airbnb\, Upwork\, etc.)\, order flow in financial markets (e.g.\, limit order books)\, communication systems\, and supply chains. \nWe study algorithms that minimize queue-regret: the expected difference between the queue-lengths (backlogs) obtained by the algorithm\, and those obtained by a genie-aided matching algorithm that knows exact service rates. A naive view of this problem would suggest that queue-regret could grow logarithmically: since queue-regret cannot be larger than classical regret\, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time. Our work shows surprisingly more complex behavior — specifically\, the optimal queue-regret decreases with time and scales as O(1/t). We next consider holding-cost regret in multi-class (multiple types of tasks) multi-server (servers/agents have task-type dependent service rate) systems. Holding costs correspond to a system where a linear cost (with respect to time spent in the queue) is incurred for each incomplete task. We consider learning-based variants of the c-mu rule – a classic and well-studied scheduling policy that is used when server/agent service rates are known. We develop algorithms that result in constant expected holding-cost regret (independent of time). The key insight that allows such a regret bound is that service systems we consider exhibit explore-free learning\, where no penalty is (eventually) incurred for exploring and learning server/agent rates. We finally discuss the implications of our results on building platforms for matching tasks to servers/agents. Base on joint work with Subhashini Krishnasamy\, Rajat Sen\, Ari Arapostathis and Ramesh Johari. \nBio: Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. He is with The University of Texas at Austin\, where he is currently the Ashley H. Priddy Centennial Professor in Engineering\, the Director of the Wireless Networking and Communications Group (WNCG)\, and a Professor in the Department of Electrical and Computer Engineering. He received the NSF CAREER award in 2004 and was elected as an IEEE Fellow in 2014. His research interests lie at the intersection of algorithms for resource allocation\, statistical learning and networks\, with applications to wireless communication networks and online platforms. \n____________________________________ \nThe LIDS Seminar Series features distinguished speakers who provide an overview of a research area\, as well as exciting recent progress in that area. Intended for a broad audience\, seminar topics span the areas of communications\, computation\, control\, learning\, networks\, probability and statistics\, optimization\, and signal processing. 
URL:https://lids.mit.edu/news-and-events/events/lids-seminar-series-0
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180914T110000
DTEND;TZID=America/New_York:20180914T120000
DTSTAMP:20260408T005831
CREATED:20180621T184405Z
LAST-MODIFIED:20180914T154952Z
UID:7902-1536922800-1536926400@idss-stage.mit.edu
SUMMARY:An Information-Geometric View of Learning in High Dimensions
DESCRIPTION:Abstract: We consider the problem of data feature selection prior to inference task specification\, which is central to high-dimensional learning. Introducing natural notions of universality for such problems\, we show a local equivalence among them. Our analysis is naturally expressed via information geometry\, and represents a conceptually and practically useful learning methodology. The development reveals the key roles of the singular value decomposition\, Hirschfeld-Gebelein-Renyi maximal correlation\, canonical correlation and principle component analyses\, Tishby’s information bottleneck\, Wyner’s common information\, Ky Fan k-norms\, and Brieman and Friedman’s alternating conditional expectation algorithm. As we’ll discuss\, this framework provides a basis for understanding and optimizing aspects of learning systems\, including neural network architectures\, matrix factorization methods for collaborative filtering\, rank-constrained multivariate linear regression\, and semi-supervised learning\, among others.\nJoint work with Shao-Lun Huang\, Anuran Makur\, and Lizhong Zheng\n\n Biography: Gregory W. Wornell received the B.A.Sc. degree (with honors) from the University of British Columbia\, Canada\, and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology\, all in Electrical Engineering and Computer Science\, in 1985\, 1987 and 1991\, respectively.\nHis research interests and publications span the areas of signal processing\, information theory\, statistical inference\, digital communication\, and information security\, and include architectures for sensing\, learning\, computing\, communication\, and storage; systems for computational imaging\, vision\, and perception; aspects of computational biology and neuroscience; and the design of wireless networks. He has been involved in the Information Theory and Signal Processing societies of the IEEE in a variety of capacities\, and maintains a number of close industrial relationships and activities. He has won a number of awards for both his research and teaching\, including the IEEE Leon K. Kirchmayer Graduate Teaching Award\, and is a Fellow of the IEEE.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-10/
LOCATION:32-155\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180911T160000
DTEND;TZID=America/New_York:20180911T170000
DTSTAMP:20260408T005831
CREATED:20180731T142245Z
LAST-MODIFIED:20180731T142245Z
UID:8102-1536681600-1536685200@idss-stage.mit.edu
SUMMARY:Science for Policy 2.0
DESCRIPTION:We live in an increasingly polarized present\, looking to a complex and uncertain future while basing our legislative decisions on systems of the past. We need the processes and structures that underpin our political decision-making to be aligned with the complexities of the 21st century. Such changes cannot be undertaken by a technocratic elite\, potentially disenfranchising citizens further from their governing institutions. Rather\, political institutions must seek to improve transparency\, openness\, and accountability. The great divide between science and policy must be bridged\, not through advisers and external counsel but through involvement in a co-creation process that would from the outset\, allow interested parties\, experts and policymakers to work together to gain a shared understanding of a specific issue\, clarity of the objectives of regulatory action as well as alternative regulatory measures. Yet we know that knowledge is not the only driver of political decision-making\, emotion\, self-interest\, power relations and values all play their role in decision-making and political discourse. Through co-creation\, interested parties\, experts\, and policymakers could potentially compare and weigh the risks\, costs\, and benefits and their distribution against self-declared biases.\nAs the European Commission’s in-house science service providing independent scientific advice and support to EU policy\, the Joint Research Centre is at the forefront of such research and seeking innovative opportunities to implement such measures. \nAbout the speaker: \nVladimír Šucha is Director-General of the Joint Research Centre\, the European Commission’s science and knowledge service. He was Deputy Director-General of the JRC between 2012 and 2013. Prior to that\, he spent 6 years in the position of director for culture and media in the Directorate-General for Education and Culture of the European Commission. Before joining the European Commission\, he held various positions in the area of European and international affairs. Between 2005 and 2006\, he was director of the Slovak Research and Development Agency\, national body responsible for funding research. He was principal advisor for European affairs to the minister of education of the Slovak Republic (2004-2005). He worked at the Slovak Representation to the EU in Brussels as research\, education and culture counselor (2000-2004). In parallel\, he has followed a long-term academic and research career\, being a full professor in Slovakia and visiting professor/scientist at different academic institutions in many countries. He published more than 100 scientific papers in peer reviewed journals.
URL:https://idss-stage.mit.edu/calendar/science-for-policy-2-0/
LOCATION:32-141\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180910T160000
DTEND;TZID=America/New_York:20180910T170000
DTSTAMP:20260408T005831
CREATED:20180905T161016Z
LAST-MODIFIED:20190501T144029Z
UID:8230-1536595200-1536598800@idss-stage.mit.edu
SUMMARY:Streaming Analytics for the Smart Grid
DESCRIPTION:How to conduct real-time analytics of streaming measurement data in the power grid? This talk offers a dynamic systems approach to utilizing data of different time scale for improved monitoring of the grid cyber and physical security. The first example of the talk presents how to leverage synchrophasor data dimensionality reduction and Robust Principal Component Analysis for early anomaly detection\, visualization\, and localization. The second example presents an online framework to detect cyber-attacks on automatic generation control (AGC). A cyber-attack detection algorithm is designed based on the approach of Dynamic Watermarking. The detection algorithm provides a theoretical guarantee of detection of cyber-attacks launched by sophisticated attackers possessing extensive knowledge of the physical and statistical models of targeted power systems. The underlying theme of the work suggests the importance of integrating data with dynamic context-aware models in the smart grid. \nBio: Dr. Le Xie is a Professor and Eugene Webb Faculty Fellow in the Department of Electrical and Computer Engineering at Texas A&M University. He received B.E. in Electrical Engineering from Tsinghua University\, S.M. in Engineering Sciences from Harvard\, and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon in 2009. His industry experience includes ISO-New England and Edison Mission Energy Marketing and Trading. His research interest includes modeling and control in data-rich large-scale systems\, grid integration of clean energy resources\, and electricity markets. \nDr. Xie received the U.S. National Science Foundation CAREER Award\, and DOE Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award. He was awarded the 2017 IEEE PES Outstanding Young Engineer Award. He was the recipient of Texas A&M Dean of Engineering Excellence Award\, ECE Outstanding Professor Award\, and TEES Select Young Fellow. He is an Editor of IEEE Transactions on Smart Grid\, and the founding chair of IEEE PES Subcommittee on Big Data & Analytics for Grid Operations. He and his students received the Best Paper awards at North American Power Symposium and IEEE SmartGridComm. He recently chaired the 2018 NSF Workshop on Real-time Learning and Decision Making in Dynamical Systems. \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/streaming-analytics-smart-grid
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20180910
DTEND;VALUE=DATE:20180911
DTSTAMP:20260408T005831
CREATED:20180717T192146Z
LAST-MODIFIED:20180717T192358Z
UID:8042-1536537600-1536623999@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data-Driven Decisions
DESCRIPTION:
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+3T2018/about?utm_medium=website&utm_source=idss&utm_campaign=ds-fl18-sept&utm_content=event-calendar
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180907T110000
DTEND;TZID=America/New_York:20180907T120000
DTSTAMP:20260408T005831
CREATED:20180621T183631Z
LAST-MODIFIED:20180626T140733Z
UID:7874-1536318000-1536321600@idss-stage.mit.edu
SUMMARY:Dejan Slepcev
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-9/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180821T140000
DTEND;TZID=America/New_York:20180821T150000
DTSTAMP:20260408T005831
CREATED:20180821T144701Z
LAST-MODIFIED:20180821T144701Z
UID:8203-1534860000-1534863600@idss-stage.mit.edu
SUMMARY:Resource-efficient ML in 2 KB RAM for the Internet of Things
DESCRIPTION:Abstract: We propose an alternative paradigm for the Internet of Things (IoT) where machine learning algorithms run locally on severely resource-constrained edge and endpoint devices without necessarily needing cloud connectivity. This enables many scenarios beyond the pale of the traditional paradigm including low-latency brain implants\, precision agriculture on disconnected farms\, privacy-preserving smart spectacles\, etc. \nTowards this end\, we develop novel tree and kNN based algorithm\, called Bonsai and ProtoNN\, for efficient prediction on IoT devices — such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support\, 2 KB RAM and 32 KB read-only flash memory. Experimental results on multiple benchmark datasets demonstrate that Bonsai and ProtoNN can make predictions in milliseconds even on slow microcontrollers\, can fit in KB of memory\, have lower battery consumption than all other algorithms while achieving prediction accuracies that can be as much as 30% higher than state-of-the-art methods for resource-efficient machine learning. \nTime permitting\, I will also discuss our recent results about deploying RNNs on similar sized tiny devices. \nJoint work with Manik Varma\, Harsha Simhadri\, Arun Suggala\, Ankit Goyal\, Chirag Gupta\, Don Dennis\, Aditya Kusupati\, Shishir Patil\, Ashish Kumar. \nBiography: I am a member of the Machine Learning and Optimization and the Algorithms and Data Sciences Group at Microsoft Research\, Bangalore\, India. My research interests are in machine learning\, non-convex optimization\, high-dimensional statistics\, and optimization algorithms in general. I am also interested in applications of machine learning to privacy\, computer vision\, text mining and natural language processing.\nEarlier\, I completed my PhD at the University of Texas at Austin under Prof. Inderjit S. Dhillon.
URL:https://idss-stage.mit.edu/calendar/resource-efficient-ml-in-2-kb-ram-for-the-internet-of-things/
LOCATION:E18-304\, United States
CATEGORIES:IDSS Special Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180525T110000
DTEND;TZID=America/New_York:20180525T120000
DTSTAMP:20260408T005831
CREATED:20180510T154321Z
LAST-MODIFIED:20180801T190714Z
UID:7554-1527246000-1527249600@idss-stage.mit.edu
SUMMARY:Fitting a putative manifold to noisy data
DESCRIPTION:Abstract: We give a solution to the following question from manifold learning.\nSuppose data belonging to a high dimensional Euclidean space is drawn independently\, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M\, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce a manifold $M_o$ whose Hausdorff distance to M is small and whose reach (normal injectivity radius) is not much smaller than the reach of M?\nThis is joint work with Charles Fefferman\, Sergei Ivanov\, Yaroslav Kurylev\, and Matti Lassas.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-8/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180515T150000
DTEND;TZID=America/New_York:20180515T160000
DTSTAMP:20260408T005831
CREATED:20180223T173133Z
LAST-MODIFIED:20180515T135702Z
UID:7445-1526396400-1526400000@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series - Vivek Borkar
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/https-lids-mit-edu-news-and-events-events-distributed-algorithms-tsitsiklis-and-beyond/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180511T110000
DTEND;TZID=America/New_York:20180511T120000
DTSTAMP:20260408T005831
CREATED:20171215T163823Z
LAST-MODIFIED:20180801T190611Z
UID:7150-1526036400-1526040000@idss-stage.mit.edu
SUMMARY:Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-3/
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:20180508T150000
DTEND;TZID=America/New_York:20180508T160000
DTSTAMP:20260408T005831
CREATED:20180223T173042Z
LAST-MODIFIED:20180223T173042Z
UID:7443-1525791600-1525795200@idss-stage.mit.edu
SUMMARY:A Rationally Designed Biomolecular Integral Feedback Control System for Robust Gene Regulation
DESCRIPTION:Abstract \nHumans have been influencing the DNA of plants and animals for thousands of years through selective breeding. Yet it is only over the last 3 decades or so that we have gained the ability to manipulate the DNA itself and directly alter its sequences through the modern tools of genetic engineering. This has revolutionized biotechnology and ushered in the era of synthetic biology. Among the possible applications enabled by synthetic biology is the design and engineering of feedback control systems that act at the molecular scale in real-time to steer the dynamic behavior of living cells. Here I will present our theoretical framework for the design and synthesis of such control systems\, and will discuss the main challenges in their practical implementation. I will then present the first designer gene network that attains integral feedback in a living cell and demonstrate its tenability and disturbance rejection properties. A growth control application shows the inherent capacity of this integral feedback control system to deliver robustness\, and highlights its potential use as a universal controller for regulation of biological variables in arbitrary networks. Finally\, I will discuss the potential impact of biomolecular control systems in industrial biotechnology and medical therapy and bring attention to the opportunities that exist for control theorists to advance this young area of research. \nBiography \nMustafa Khammash is the Professor of Control Theory and Systems Biology at the Department of Biosystems Science and Engineering at ETH Zurich\, Switzerland. He works in the areas of control theory\, systems biology\, and synthetic biology. His lab develops theoretical\, computational\, and experimental methods aimed at understanding the role of dynamics\, feedback\, and randomness in biology. He is currently developing new theoretical and experimental approaches for the design of biomolecular control systems and for their realization in living cells. \nProf. Khammash received his B.S. degree from Texas A&M University in 1986 and his Ph.D. from Rice University in 1990\, both in electrical engineering. In 1990\, he joined the engineering faculty of Iowa State University\, where he created the Dynamics and Control Program and led the control group until 2002. He then joined the engineering faculty at the University of California\, Santa Barbara (UCSB)\, where he was Director of the Center for Control\, Dynamical Systems and Computation (CCDC) until 2011 when he joined ETH Zurich. He is a Fellow of the IEEE\, IFAC\, and the Japan Society for the Promotion of Science (JSPS).
URL:https://idss-stage.mit.edu/calendar/a-rationally-designed-biomolecular-integral-feedback-control-system-for-robust-gene-regulation/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20180507
DTEND;VALUE=DATE:20180508
DTSTAMP:20260408T005831
CREATED:20180405T175024Z
LAST-MODIFIED:20180501T185710Z
UID:7579-1525651200-1525737599@idss-stage.mit.edu
SUMMARY:Data Science and Big Data Analytics: Making Data Driven Decisions
DESCRIPTION:
URL:https://mitxpro.mit.edu/courses/course-v1:MITxPRO+DSx+2T2018/about?utm_medium=website&utm_source=idss&utm_campaign=ds-su18&utm_content=event-calendar
LOCATION:online
CATEGORIES:Online events
ATTACH;FMTTYPE=image/png:https://idss-stage.mit.edu/wp-content/uploads/2018/04/Screen-Shot-2018-04-05-at-1.41.29-PM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180504T110000
DTEND;TZID=America/New_York:20180504T120000
DTSTAMP:20260408T005831
CREATED:20171215T163500Z
LAST-MODIFIED:20180801T190448Z
UID:7148-1525431600-1525435200@idss-stage.mit.edu
SUMMARY:Size-Independent Sample Complexity of Neural Networks
DESCRIPTION:MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-2/
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:20180501T160000
DTEND;TZID=America/New_York:20180501T170000
DTSTAMP:20260408T005831
CREATED:20171228T155922Z
LAST-MODIFIED:20180427T191821Z
UID:7193-1525190400-1525194000@idss-stage.mit.edu
SUMMARY:IDSS Distinguished Seminar - Conflict in Networks:  The Rise and Fall of Empires
DESCRIPTION:Abstract  \nIn the study of war\, a recurring observation is that conflict between two opponents is shaped by third parties. The actions of these parties are in turn influenced by other proximate players. These considerations lead us to propose a model of conflict in a network. We study the influence of resources\, technology\, and the network of connections on the dynamics of war and the prospects of peace. \nBio \nSanjeev Goyal is Professor of Economics at the University of Cambridge and Fellow of Christ’s College\, Cambridge. His early research in the 1990’s laid the foundations of an economic approach to the study of networks by providing a framework for the study of the effects of social networks on human behaviour and by developing a model of how the costs and benefits of linking shape the formation of networks. In subsequent work\, he has explored applications of network ideas in a variety of fields including industrial organisation\, economic development\, international trade\, and conflict. In 2007\, Princeton University Press published his book\, Connections: An Intrduction to the Economics of Networks. Sanjeev Goyal is a Fellow of the British Academy and was the founding Director of the Cambridge-INET Institute
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-sanjeev-goyal-university-of-cambridge/
LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:IDSS Distinguished Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://idss-stage.mit.edu/wp-content/uploads/2017/10/IMG_1788.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180427T110000
DTEND;TZID=America/New_York:20180427T120000
DTSTAMP:20260408T005831
CREATED:20171215T163016Z
LAST-MODIFIED:20180426T181058Z
UID:7146-1524826800-1524830400@idss-stage.mit.edu
SUMMARY:Inference\, Computation\, and Visualization for Convex Clustering and Biclustering
DESCRIPTION:Abstract:  Hierarchical clustering enjoys wide popularity because of its fast computation\, ease of interpretation\, and appealing visualizations via the dendogram and cluster heatmap. Recently\, several have proposed and studied convex clustering and biclustering which\, similar in spirit to hierarchical clustering\, achieve cluster merges via convex fusion penalties. While these techniques enjoy superior statistical performance\, they suffer from slower computation and are not generally conducive to representation as a dendogram. In the first part of the talk\, we present new convex (bi)clustering methods and fast algorithms that inherit all of the advantages of hierarchical clustering. Specifically\, we develop a new fast approximation and variation of the convex (bi)clustering solution path that can be represented as a dendogram or cluster heatmap. Also\, as one tuning parameter indexes the sequence of convex (bi)clustering solutions\, we can use these to develop interactive and dynamic visualization strategies that allow one to watch data form groups as the tuning parameter varies. In the second part of this talk\, we consider how to conduct inference for convex clustering solutions that addresses questions like: Are there clusters in my data set? Or\, should two clusters be merged into one? To achieve this\, we develop a new geometric representation of Hotelling’s T^2-test that allows us to use the selective inference paradigm to test multivariate hypotheses for the first time. We can use this approach to test hypotheses and calculate confidence ellipsoids on the cluster means resulting from convex clustering. We apply these techniques to examples from text mining and cancer genomics. This is joint work with John Nagorski\, Michael Weylandt\, and Frederick Campbell. \nBiography:  Genevera Allen is an Associate Professor of Statistics\, Computer Science\, and Electrical and Computer Engineering at Rice University. She is also a member of the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital and Baylor College of Medicine where she holds a joint appointment. Dr. Allen received her PhD in statistics from Stanford University (2010)\, under the mentorship of Prof. Robert Tibshirani\, and her bachelors\, also in statistics\, from Rice University (2006).\nDr. Allen’s research focuses on developing statistical methods to help scientists make sense of their ‘Big Data’ in applications such as high-throughput genomics and neuroimaging. Her work lies in the areas of modern multivariate analysis\, graphical models\, statistical machine learning\, and data integration or data fusion. She is the recipient of several honors including a National Science Foundation CAREER award\, the International Biometric Society’s Young Statistician Showcase award\, and the George R. Brown School of Engineering’s Research and Teaching Excellence Award at Rice University. In 2013 and 2014\, she represented the American Statistical Association (ASA) at the Coalition for National Science Funding on Capitol Hill and has had her research highlighted on the House floor in a speech by Congressman McNerney (D-CA). In 2014\, Dr. Allen was named to the “Forbes ’30 under 30′: Science and Healthcare” list. Dr. Allen currently serves as an Associated Editor for Biometrics\, the Secretary / Treasurer for the ASA Section on Statistical Computing\, and the Program Chair for the ASA Section on Statistical Learning and Data Science.\nOutside of work\, Dr. Allen is a patron of the Houston Symphony and Houston Grand Opera and is involved with several arts organizations throughout Houston. She also enjoys traveling\, Texas craft beers\, and playing viola.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar/
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:20180424T150000
DTEND;TZID=America/New_York:20180424T160000
DTSTAMP:20260408T005831
CREATED:20180223T172912Z
LAST-MODIFIED:20180419T194543Z
UID:7441-1524582000-1524585600@idss-stage.mit.edu
SUMMARY:LIDS Seminar Series: Jose M. F. Moura
DESCRIPTION:
URL:https://idss-stage.mit.edu/calendar/lids-seminar-series-jose-m-f-moura/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20180420
DTEND;VALUE=DATE:20180421
DTSTAMP:20260408T005831
CREATED:20171205T162353Z
LAST-MODIFIED:20180501T144423Z
UID:7069-1524182400-1524268799@idss-stage.mit.edu
SUMMARY:SDSCon 2018
DESCRIPTION:SDSCon 2018 is the second annual 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). \nFor more information please visit https://sdsc2018.mit.edu
URL:https://idss-stage.mit.edu/calendar/sdscon-2018/
LOCATION:Bartos Theater\, Media Lab\, 20 Ames Street\, Cambridge\, MA\, 02139\, United States
CATEGORIES:Conferences and Workshops
ATTACH;FMTTYPE=image/png:https://idss-stage.mit.edu/wp-content/uploads/2017/12/Screen-Shot-2017-12-05-at-11.05.36-AM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180418T140000
DTEND;TZID=America/New_York:20180418T150000
DTSTAMP:20260408T005831
CREATED:20180223T172740Z
LAST-MODIFIED:20180223T172740Z
UID:7439-1524060000-1524063600@idss-stage.mit.edu
SUMMARY:Community-based and Peer-to-peer Electricity Markets
DESCRIPTION:Abstract \nThe deployment of distributed renewable generation capacities\, new ICT capabilities\, as well as a more proactive role of consumers\, are all motivating rethinking electricity markets in a more distributed and consumer-centric fashion. After motivating the design of various forms of consumer-centric electricity markets\, we will focus on two alternative constructs (which could actually be unified) consisting in community-based and peer-to-peer electricity markets. The mathematical framework for these markets will be described\, with focus on negotiation and clearing algorithms in a distributed and decentralized setup. Opportunities and challenges related to these markets\, both mathematical and related to real-world applications\, will be discussed. Especially\, we will look at fairness aspects\, product differentiation\, as well as the design of network charges to account for ‘actual’ usage of a network. \nBiography \nPierre Pinson is a Professor at the Centre for Electric Power and Energy (CEE) of the Technical University of Denmark (DTU\, Dept. of Electrical Engineering)\, also heading a group focusing on Energy Analytics & Markets. He holds an M.Sc. In Applied Mathematics from INSA Toulouse and a Ph.D. In Energy Engineering from Ecole de Mines de Paris (France). He acts (or has acted) as an Editor for the IEEE Transactions on Power Systems\, the International Journal of Forecasting and Wind Energy. His main research interests are centered around the proposal and application of mathematical methods for electricity markets and power systems operations\, including forecasting. He has published extensively in some of the leading journals in Meteorology\, Power Systems Engineering\, Statistics and Operations Research. He has been a visiting researcher at the University of Oxford (Mathematical Institute) and the University of Washington in Seattle (Dpt. of Statistics)\, as well as a scientist at the European Center for Medium-range Weather Forecasts (ECMWF\, UK) and a visiting professor at Ecole Normale Superieure (Rennes\, France). In 2019 he will be a Simons Fellow at the University of Cambridge\, Isaac Newton Institute (“The mathematics of energy systems”). He is leading a number of initiatives aiming to profundly rethink electricity markets for future renewable-based power systems and with a more proactive role of consumers. This focus on consumer-centric and community-driven electricity markets translates into proposals for peer-to-peer energy exchange\, from mathematical framework to actual demonstration in Denmark.
URL:https://idss-stage.mit.edu/calendar/community-based-and-peer-to-peer-electricity-markets/
LOCATION:MIT Building E18\, Room 304\, Ford Building (E18)\, 50 Ames Street\, Cambridge\, MA\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180413T110000
DTEND;TZID=America/New_York:20180413T120000
DTSTAMP:20260408T005831
CREATED:20171215T161019Z
LAST-MODIFIED:20180801T190032Z
UID:7143-1523617200-1523620800@idss-stage.mit.edu
SUMMARY:Testing degree corrections in Stochastic Block Models
DESCRIPTION:Abstract:  The community detection problem has attracted signicant attention in re- cent years\, and it has been studied extensively under the framework of a Stochas- tic Block Model (SBM). However\, it is well-known that SBMs fit real data very poorly\, and various extensions have been suggested to replicate characteristics of real data. The recovered community assignments are often sensitive to the model used\, and this naturally begs the following question:  Given a network with community structure\, how to decide whether to fit a vanilla SBM\, or a more complicated model?  In this talk\, we will formulate this problem as a classical goodness of fit question\, and try to provide some principled answers in this direction. \nThis is based on joint work with Rajarshi Mukherjee. \nBio:  Subhabrata Sen is Schramm Postdoctoral Fellow at Microsoft Re- search NE and MIT Mathematics. He graduated from the Stanford Statistics Department in 2017\, where he was advised by Amir Dembo and Andrea Mon- tanari. He was awarded the “Probability Dissertation Award” for his thesis on “Random graphs\, optimization\, and spin glasses”.  His research interests include hypothesis testing and non-parametric inference on one hand\, and combinatorial optimization and random graphs on the other.
URL:https://idss-stage.mit.edu/calendar/stochastic-and-statistics-seminar/
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:20180410T150000
DTEND;TZID=America/New_York:20180410T160000
DTSTAMP:20260408T005831
CREATED:20180223T172617Z
LAST-MODIFIED:20180223T172617Z
UID:7437-1523372400-1523376000@idss-stage.mit.edu
SUMMARY:Finding Online Extremists in Social Networks
DESCRIPTION:Abstract \nOnline extremists in social networks pose a new form of threat to the general public. These extremists range from cyber bullies who harass innocent users to terrorist organizations such as ISIS that use social networks to spread propaganda. Currently\, social networks suspend the accounts of such extremists in response to user complaints\, but these extremist users simply create new accounts and continue their activities. In this talk\, we present a new set of operational capabilities to help authorities mitigate the threat posed by online extremist groups in social networks. \nUsing data from several hundred thousand extremist accounts on Twitter\, we develop a behavioral model for these users\, in particular\, what their accounts look like and who they connect with. This model is used to identify new extremist accounts by predicting if they will be suspended for extremist activity. We also use this model to track existing extremist users as they create new accounts by identifying if two accounts belong to the same user. Finally\, we use this model as the basis for an efficient policy to search the social network for suspended users’ new accounts. Our search approach is based on a variant of the classic Polya’s urn setup. We find a simple characterization of the optimal search policy for this model under fairly general conditions. Our search policy and main theoretical results generalize easily to search problems in other fields. \nJoint work with Jytte Klausen and Christopher Marks. \nBiography \nTauhid is an Assistant Professor of Operations Management at the MIT Sloan School of Management. He received his BS\, MEng\, and Ph.D. degrees in electrical engineering and computer science from MIT. His research focuses on solving operational problems involving social network data using probabilistic models\, network algorithms\, and modern statistical methods. Some of the topics he studies in the social networks space include predicting the popularity of content\, finding online extremists\, and geo-locating users. His broader interests cover data-driven approaches to investing in startup companies\, non-traditional choice modeling\, algorithmic sports betting\, and biometric data. His work has been featured in the Wallstreet Journal\, Wired\, Mashable\, the LA Times\, and Time Magazine.
URL:https://idss-stage.mit.edu/calendar/finding-online-extremists-in-social-networks/
LOCATION:32-141\, United States
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180406T110000
DTEND;TZID=America/New_York:20180406T120000
DTSTAMP:20260408T005831
CREATED:20180311T182217Z
LAST-MODIFIED:20180311T183822Z
UID:7473-1523012400-1523016000@idss-stage.mit.edu
SUMMARY:Optimality of Spectral Methods for Ranking\, Community Detections and Beyond
DESCRIPTION:Abstract:  Spectral methods have been widely used for a large class of challenging problems\, ranging from top-K ranking via pairwise comparisons\, community detection\, factor analysis\, among others.\nAnalyses of these spectral methods require super-norm perturbation analysis of top eigenvectors. This allows us to UNIFORMLY approximate elements in eigenvectors by linear functions of the observed random matrix that can be analyzed further. We first establish such an infinity-norm pertubation bound for top eigenvectors and apply the idea to several challenging problems such as top-K ranking\, community detections\, Z_2-syncronization and matrix completion. We show that the spectral methods are indeed optimal for these problems. We illustrate these methods via simulations.\n(Based on joint work with Emmanuel Abbe\, Kaizheng Wang\, Yiqiao Zhong and that of Yixin Chen\, Cong Ma and Kaizheng Wang) \n Biography: Jianqing Fan is Frederick L. Moore Professor at Princeton University. After receiving his Ph.D. from the University of California at Berkeley\, he has been appointed as assistant\, associate\, and full professor at the University of North Carolina at Chapel Hill (1989-2003)\, professor at the University of California at Los Angeles (1997-2000)\, and professor at the Princeton University (2003–). He was the past president of the Institute of Mathematical Statistics and International Chinese Statistical Association. He is co-editing Journal of Econometrics and was the co-editor of The Annals of Statistics\, Probability Theory and Related Fields and Econometrics Journal. His published work on statistics\, economics\, finance\, and computational biology has been recognized by The 2000 COPSS Presidents’ Award\, The 2007 Morningside Gold Medal of Applied Mathematics\, Guggenheim Fellow\, P.L. Hsu Prize\, Royal Statistical Society Guy medal in silver\, and election to Academician of Academia Sinica and follow of American Associations for Advancement of Science.
URL:https://idss-stage.mit.edu/calendar/optimality-of-spectral-methods-for-ranking-community-detections-and-beyond/
LOCATION:E18-304\, United States
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
END:VCALENDAR