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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181030T170000
DTEND;TZID=America/New_York:20181030T180000
DTSTAMP:20260408T023549
CREATED:20181005T212240Z
LAST-MODIFIED:20181005T213127Z
UID:8362-1540918800-1540922400@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty\, who introduces the program and answers your questions. \nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar-2/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181029T160000
DTEND;TZID=America/New_York:20181029T170000
DTSTAMP:20260408T023549
CREATED:20180810T160254Z
LAST-MODIFIED:20190501T143409Z
UID:8166-1540828800-1540832400@idss-stage.mit.edu
SUMMARY:Computing with Assemblies
DESCRIPTION:Computation in the brain has been modeled productively at many scales\, ranging from molecules to dendrites\, neurons\, and synapses\, all the way to the whole brain models useful in cognitive science. I will discuss recent work on an intermediate layer\, involving assemblies of neurons — that is to say\, sets of neurons firing together in a repetitive pattern whenever we think of a particular memory\, concept or idea. Assemblies have been conjectured six decades ago by Hebb\, and have been over the past decade noticed in both the animal and the human brain. Further\, experiments\, simulations\, and theoretical analysis suggest that assemblies can be copied from one brain area to another\, and associated with other assemblies to encode affinity. We propose a broader “calculus” of assemblies\, including operations such as “reciprocal-project” and “merge”\, comprising a powerful computational model. One interesting hypothesis is that assembly operations may underlie some of the most advanced functions of the brain\, such as reasoning\, planning\, language\, math. Work with Santosh Vempala\, Wolfgang Maass\, and Michael Collins. \n\n\nBio: Christos H. Papadimitriou is the Donovan Family professor of computer science at Columbia University. Before joining Columbia in 2017\, he taught at UC Berkeley for 22 years\, and before that at Harvard\, MIT\, NTU Athens\, Stanford\, and UCSD. He has written five textbooks and many articles on algorithms and complexity\, and their applications to optimization\, databases\, control\, AI\, robotics\, economics and game theory\, the Internet\, evolution\, and more recently the study of the brain. He holds a Ph.D. from Princeton as well as eight honorary doctorates\, and he has won the Knuth prize\, the Goedel prize\, and the von Neumann Medal. He is a member of the National Academy of Sciences of the US\, the American Academy of Arts and Sciences\, and the National Academy of Engineering; in 2013 the president of Greece named him Commander of the Order of the Phoenix. He has also written three novels: “Turing”\, “Logicomix” and his latest “Independence”. \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/lids-seminar-series-4
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181026T110000
DTEND;TZID=America/New_York:20181026T120000
DTSTAMP:20260408T023549
CREATED:20180621T192221Z
LAST-MODIFIED:20180626T142057Z
UID:7914-1540551600-1540555200@idss-stage.mit.edu
SUMMARY:Alan Frieze
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-16/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181025T090000
DTEND;TZID=America/New_York:20181025T170000
DTSTAMP:20260408T023549
CREATED:20180717T192722Z
LAST-MODIFIED:20180906T134254Z
UID:8046-1540458000-1540486800@idss-stage.mit.edu
SUMMARY:IDSS Retail Conference 2018
DESCRIPTION:The retail sector is undergoing fundamental changes as a result of data analytics and computer science. The IDSS Retail Conference 2018 will bring together experts from academia and industry to discuss a variety of topics addressing “Data Driven Disruption” in the Retail Sector.
URL:https://idssretail2018.mit.edu/
LOCATION:Microsoft New England Research and Development Center\, One Memorial Drive\, Cambridge\, 02139\, United States
CATEGORIES:Conferences and Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181022T160000
DTEND;TZID=America/New_York:20181022T170000
DTSTAMP:20260408T023549
CREATED:20180810T160117Z
LAST-MODIFIED:20190501T143713Z
UID:8164-1540224000-1540227600@idss-stage.mit.edu
SUMMARY:Distributed Statistical Estimation of High-Dimensional Distributions and Parameters under Communication Constraints
DESCRIPTION:Modern data sets are often distributed across multiple machines and processors\, and bandwidth and energy limitations in networks and within multiprocessor systems often impose significant bottlenecks on the performance of algorithms. Motivated by this trend\, we consider the problem of estimating high-dimensional distributions and parameters in a distributed network\, where each node in the network observes an independent sample from the underlying distribution and can communicate it to a central processor by writing at most k bits on a public blackboard. We obtain matching upper and lower bounds for the minimax risk of estimating the underlying distribution or parameter under various common statistical models. Our results show that the impact of the communication constraint can be qualitatively different depending on the tail behavior of the score function associated with each model. The key ingredient in our proof is a geometric characterization of Fisher information from quantized samples. \nJoint work with Leighton Barnes\, Yanjun Han\, and Tsachy Weissman. \nBio: Ayfer Ozgur received her Ph.D. degree in 2009 from the Information Processing Group at EPFL\, Switzerland. In 2010 and 2011\, she was a post-doctoral scholar at the same institution. She is an Assistant Professor in the Electrical Engineering Department at Stanford University since 2012. Her research interests include distributed communication and learning\, wireless systems\, and information theory. Dr. Ozgur received the EPFL Best Ph.D. Thesis Award in 2010\, an NSF CAREER award in 2013\, the Okawa Foundation Research Grant and the IEEE Communication Theory Technical Committee (CTTC) Early Achievement Award in 2018. \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/lids-seminar-series-3
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181019T110000
DTEND;TZID=America/New_York:20181019T120000
DTSTAMP:20260408T023549
CREATED:20180621T191820Z
LAST-MODIFIED:20181010T202034Z
UID:7912-1539946800-1539950400@idss-stage.mit.edu
SUMMARY:Algorithmic thresholds for tensor principle component analysis
DESCRIPTION:Abstract:  Consider the problem of recovering a rank 1 tensor of order k that has been subject to Gaussian noise. The log-likelihood for this problem is highly non-convex. It is information theoretically possible to recover the tensor with a finite number of samples via maximum likelihood estimation\, however\, it is expected that one needs a polynomially diverging number of samples to efficiently recover it. What is the cause of this large statistical–to–algorithmic gap? To study this question\, we investigate the thresholds for efficient recovery for a simple family of algorithms\, Langevin dynamics and gradient descent. We view this problem as a member of a broader class of problems which correspond to recovering a signal from a non-linear observation that has been perturbed by isotropic Gaussian noise. We propose a mechanism for success/failure of recovery of such algorithms in terms of the strength of the signal on the high entropy region of the initialization. Joint work with G. Ben Arous (NYU) and R. Gheissari (NYU). \n Biography:  Aukosh Jagannath is a Benjamin Pierce Fellow and NSF Postdoctoral fellow at Harvard University with undergraduate and graduate degree from NYU. He works in probability at the interface of statistical physics\, data science\, combinatorics\, and statistics.
URL:https://idss-stage.mit.edu/calendar/stochastics-and-statistics-seminar-15/
CATEGORIES:Stochastics and Statistics Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181017T120000
DTEND;TZID=America/New_York:20181017T130000
DTSTAMP:20260408T023550
CREATED:20181005T211137Z
LAST-MODIFIED:20181005T213025Z
UID:8360-1539777600-1539781200@idss-stage.mit.edu
SUMMARY:SES PhD Admissions Webinar
DESCRIPTION:Learn about admission to the Social and Engineering Systems Doctoral Program. Webinars are led by a member of the IDSS faculty\, who introduces the program and answers your questions. \nPlease register in advance.
URL:https://idss-stage.mit.edu/calendar/ses-phd-admissions-webinar/
LOCATION:online
CATEGORIES:Online events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181016T160000
DTEND;TZID=America/New_York:20181016T170000
DTSTAMP:20260408T023550
CREATED:20180712T160153Z
LAST-MODIFIED:20181024T145321Z
UID:7973-1539705600-1539709200@idss-stage.mit.edu
SUMMARY:Can machine learning survive the artificial intelligence revolution?
DESCRIPTION:  \nAbstract:\nData and algorithms are ubiquitous in all scientific\, industrial and personal domains. Data now come in multiple forms (text\, image\, video\, web\, sensors\, etc.)\, are massive\, and require more and more complex processing beyond their mere indexation or the computation of simple statistics\, such as recognizing objects in images or translating texts. For all of these tasks\, commonly referred to as artificial intelligence (AI)\, significant recent progress has allowed algorithms to reach performances that were deemed unreachable a few years ago and that make these algorithms useful to everyone.\nMany scientific fields contribute to AI\, but most of the visible progress come from machine learning and tightly connected fields such as computer vision and natural language processing. Indeed\, many of the recent advances are due to the availability of massive data to learn from\, large computing infrastructures and new machine learning models (in particular deep neural networks).\nBeyond the well publicized visibility of some advances\, machine learning has always been a field characterized by the constant exchanges between theory and practice\, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Is this still possible? \nIn this talk\, Francis Bach will present recent illustrating machine learning successes and propose some answers to the question above. \nFrancis Bach is a researcher at Inria\, leading since 2011 the machine learning team which is part of the Computer Science Department at Ecole Normale Supérieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005\, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris\, then he joined the computer vision project-team at Inria/Ecole Normale Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in graphical models\, sparse methods\, kernel-based learning\, large-scale convex optimization\, computer vision and signal processing. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council\, and received the Inria young researcher prize in 2012\, the ICML test-of-time award in 2014\, as well as the Lagrange prize in continuous optimization in 2018. In 2015\, he was program co-chair of the International Conference in Machine learning (ICML)\, and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research.
URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-francis-bach/
LOCATION:32-141\, United States
CATEGORIES:IDSS Distinguished Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181015T160000
DTEND;TZID=America/New_York:20181015T170000
DTSTAMP:20260408T023550
CREATED:20180810T155930Z
LAST-MODIFIED:20190501T143758Z
UID:8162-1539619200-1539622800@idss-stage.mit.edu
SUMMARY:Augmented Lagrangians and Decomposition in Convex and Nonconvex Programming
DESCRIPTION:Multiplier methods based on augmented Lagrangians are attractive in convex and nonconvex programming for their stabilizing and even convexifying properties. They have widely been seen\, however\, as incompatible with taking advantage of a block-separable structure. \nIn fact\, when articulated in the right way\, they can produce decomposition algorithms in which low-dimensional subproblems can be solved in parallel. Convergence in the nonconvex case is\, of course\, just local\, but is available under a broad analog of the strong second-order sufficient condition for local optimality that dominates much of computational methodology outside of convex optimization. This carries over also to extended nonlinear programming with its greater flexibility to handle composite terms. \nBio: Ralph Tyrrell (Terry) Rockafellar has long been associated with the University of Washington\, Seattle\, where he is Professor Emeritus of Mathematics\, but has also contributed in recent years as Adjunct Research Professor of Systems and Industrial Engineering at the University of Florida\, Gainesville\, and as Honorary Professor of the Department of Applied Mathematics at Hong Kong Polytechnic University. \nHis interests span from convex and variational analysis to problems of optimization and equilibrium\, especially nowadays applications in finance\, engineering\, and economics involving risk and reliability\, along with schemes of problem decomposition on convex and nonconvex programming. \nIn addition to being a winner of the Dantzig Prize given jointly by SIAM and the Mathematical Programming Society (1983)\, Prof. Rockafellar has gained international recognition for his work through honorary doctorates bestowed by universities in a number of countries. INFORMS awarded him and Roger Wets the 1997 Lancaster Prize for their book Variational Analysis\, and in 1999 he was further honored by INFORMS with John von Neumann Theory Prize for his fundamental contributions to the methodology of optimization. He has authored over 240 publications\, including one of the all-time most highly cited books in mathematics\, Convex Analysis. \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-2
LOCATION:32-155
CATEGORIES:LIDS Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20181015
DTEND;VALUE=DATE:20181016
DTSTAMP:20260408T023550
CREATED:20180802T010632Z
LAST-MODIFIED:20180802T010844Z
UID:8118-1539561600-1539647999@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+4T2018/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:20181012T110000
DTEND;TZID=America/New_York:20181012T120000
DTSTAMP:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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:20260408T023550
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
END:VCALENDAR