The Microsoft Research Colloquium at Microsoft Research New England focuses on research in the foundational aspects of computer science, mathematics, economics, anthropology, and sociology. With an interdisciplinary flavor, this colloquium series features some of the foremost researchers in their fields talking about their research, breakthroughs, and advances.
The agenda typically consists of approximately 50 minutes of prepared presentation and brief Q&A, followed immediately by a brief reception to meet the speaker and address detailed questions. We welcome members of the local academic community to attend.
Upcoming Speakers
Time Incentives in Public Procurement: Evidence from California and Minnesota
Greg Lewis, Harvard
Wednesday, May 22
4:00 PM – 5:00 PM
Description
Most procurement contracts incentivize timely delivery, either through the auction mechanism or the contract terms. We evaluate both of these approaches in the context of highway procurement, using data from California and Minnesota. We show that firms respond strongly to incentives: for example, in California, when contractors compete for contracts on the basis of both price and delivery date, contracts are completed 30-40% faster. We simulate counterfactual outcomes under different incentive schemes, and discuss the practical implications of our research for the design of procurement contracts.
Biography
Greg Lewis is associate professor of economics at Harvard University, and faculty research fellow at the National Bureau of Economic Research. His main research interests lie in industrial organization and market design, with a particular focus on auction theory and estimation. Recently, his time has been spent developing dynamic models of auction markets, suggesting methods for price discrimination in online display advertising, examining learning by firms in the British electricity market and analyzing how contracts terms interact with moral hazard in highway procurement. He received his bachelor’s degree in economics and statistics from the University of the Witwatersrand in South Africa, and his MA and PhD both from the University of Michigan.
Hannah Wallach, UMass Amherst
Wednesday, July 3
Past Speakers
Sum-Product Networks: Powerful Models with Tractable Inference
Pedro Domingos, U Washington
Wednesday, May 8
4:00 PM – 5:00 PM
Description
Big data makes it possible in principle to learn very rich probabilistic models, but inference in them is prohibitively expensive. Since inference is typically a subroutine of learning, in practice learning such models is very hard. Sum-product networks (SPNs) are a new model class that squares this circle by providing maximum flexibility while guaranteeing tractability. In contrast to Bayesian networks and Markov random fields, SPNs can remain tractable even in the absence of conditional independence. SPNs are defined recursively: an SPN is either a univariate distribution, a product of SPNs over disjoint variables, or a weighted sum of SPNs over the same variables. It's easy to show that the partition function, all marginals and all conditional MAP states of an SPN can be computed in time linear in its size. SPNs have most tractable distributions as special cases, including hierarchical mixture models, thin junction trees, and nonrecursive probabilistic context-free grammars. I will present generative and discriminative algorithms for learning SPN weights, and an algorithm for learning SPN structure. SPNs have achieved impressive results in a wide variety of domains, including object recognition, image completion, collaborative filtering, and click prediction. Our algorithms can easily learn SPNs with many layers of latent variables, making them arguably the most powerful type of deep learning to date. (Joint work with Rob Gens and Hoifung Poon.)
Biography
Pedro Domingos received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon. He received an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. He spent two years as an assistant professor at IST, before joining the faculty of the University of Washington in 1999. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas. He is a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was program co-chair of KDD-2003 and SRL-2009, and served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. He is a AAAI Fellow, and received a Sloan Fellowship, an NSF CAREER Award, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions.
Compressed Sensing and Natural Image Statistics
Yair Weiss, Hebrew U
Wednesday, April 24
4:00 PM – 5:00 PM
Description
Compressed sensing (CS) refers to a branch of applied mathematics which is based on the surprising result whereby signals that are exactly “k-sparse” (i.e. can be represented by at most k nonzero coefficients in some basis) can be exactly reconstructed using a small number of random measurements. Since natural images tend to be sparse in the wavelet basis, one of the motivating examples of CS has always been to reconstruct high resolution images from a small number of random measurements. Unfortunately, there are some significant deviations between the way that natural images behave and the assumptions of the dramatic theorems, and in fact random projections perform quite poorly when applied to real images. I will describe an alternative theory, which we call “Informative Sensing”, that seeks a small number of projections that are maximally informative given a known distribution over signals. I will show experimental results demonstrating that the informative projections indeed outperform random projections, but that the savings relative to more standard imaging methods are altogether rather modest.
Joint work with Hyun Sung Chang and Bill Freeman.
Biography
Yair Weiss is a Professor of Computer Science and Engineering at the Hebrew University of Jerusalem. He is currently on sabbatical at Microsoft Research New England.
The Disruptive Power Of Three-Dimensional Printing
Deven Desai, Thomas Jefferson School of Law
Thursday, May 2 *note the alternate date*
4:00 PM – 5:00 PM
Description
The Industrial Revolution was founded on economies of scale, but the next transformation in manufacturing may come from individual households. An additive (or 3D) printer is a desktop machine that can make customized physical objects from software and simple raw materials. This device promises to dramatically reduce the cost of making and distributing tangible goods, but it could also sharply increase patent infringement. Indeed, 3D printers present a challenge to patent law that is analogous to the disruption of copyright by MP3 files. This talk explores the implications of 3D printing for patents.
Biography
Deven Desai is a law professor at the Thomas Jefferson School of Law and recently completed serving as Academic Research Counsel at Google, Inc. As a law professor, he teaches trademark, intellectual property theory, business associations, and information privacy law. He is a graduate of the University of California, Berkeley and Yale Law School. He has also spent year as a Visiting Fellow at Princeton University’s Center for Information Technology Policy. Professor Desai’s scholarship examines how business interests and economic theories shape privacy and intellectual property law and where those arguments explain productivity or where they fail to capture society’s interest in the free flow of information and development. His articles include Speech Citizenry and the Market: A Corporate Public Figure Doctrine 98 Minnesota Law Review __ (2013) (forthcoming); Bounded by Brands: An Information Network Approach to Brands, U.C. Davis Law Review (2013) (forthcoming); Beyond Location: Data Security in the 21st Century, Communications of the ACM (January, 2013); Response: An Information Approach to Trademarks, 100 Georgetown Law Journal 2119 (2012); From Trademarks to Brands, 46 Florida Law Review 981 (2012); The Life and Death of Copyright, 2011 Wisconsin Law Review 219 (2011); Brands, Competition, and the Law, 2010 Brigham Young Law Review 1425 (2010) (with Spencer Waller); Privacy? Property?: Reflections on the Implications of a Post-Human World 18 Kansas J. of Law & Public Policy (2009); Property, Persona, and Preservation, 81 Temple Law Review 67 (2008); and Confronting the Genericism Conundrum, 28 Cardozo Law Review 789 (2007) (Sandra L. Rierson, co-author).
So You Think Quantum Computing Is Bunk?
Scott Aaronson, MIT
Wednesday, April 10, 2013
4:00 PM – 5:00 PM
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Description
In this talk, I'll take an unusual tack in explaining quantum computing to a broad audience. I'll start by assuming, for the sake of argument, that scalable quantum computing is "too crazy to work": i.e., that it must be impossible for some fundamental physical reason. I'll then investigate the sorts of radical additions or changes to current physics that we seem forced to contemplate in order to justify such an assumption. I'll point out the many cases where such changes seem ruled out by existing experiments, or by no-go theorems such as the Bell Inequality. I'll also mention two recent no-go theorems for so-called "epistemic" hidden-variable theories: one due to Pusey, Barrett, and Rudolph, the other to Bouland, Chua, Lowther, and myself. Finally, I'll discuss my 2004 notion of a "Sure/Shor separator," as well as the BosonSampling proposal [A.-Arkhipov 2011] and its recent experimental realizations---which suggest one possible route to falsifying the Extended Church-Turing Thesis more directly than by building a universal quantum computer.
Biography
Scott Aaronson is the TIBCO Career Development Associate Professor of Electrical Engineering and Computer Science at MIT. His research focuses on the capabilities and limits of quantum computers, and computational complexity theory more generally. His book, "Quantum Computing Since Democritus," was recently published by Cambridge University Press; he's also written about quantum computing for Scientific American and the New York Times. He's received the National Science Foundation's Alan T. Waterman Award, as well as MIT's Junior Bose Award for Excellence in Teaching.
Platforms, practices, politics: Towards an open history of social media
Jean Burgess, Queensland U of Technology
Wednesday, March 27, 2013
4:00 PM – 5:00 PM
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Description
Social media has been with us as a mainstream phenomenon for barely a decade now. That period has seen multiple, distinct paradigm shifts in the business models, uses, and discourses surrounding social media, as well as in approaches to conducting research on and through particular social media platforms. In this paper I draw on recent attempts within media, communication and cultural studies to go beyond static, single-platform snapshots and to develop more synthesized, general accounts of how social media has evolved since the early 2000s. I show how we might identify patterns of change across platforms and over time, and discuss the practical and conceptual challenges of opening up these short but dynamic histories of the proprietary web.
Biography
Jean Burgess is an Associate Professor of Digital Media Studies and Deputy Director of the ARC Centre of Excellence for Creative Industries & Innovation (CCI) at Queensland University of Technology, Australia. Her research focuses on the uses, politics and methodological implications of social and mobile media platforms.
The Virtual Lab
Duncan Watts, Microsoft Research New York City
Wednesday, December 5, 2012
4:00 PM – 5:00 PM
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Description
Crowdsourcing sites like Amazon's Mechanical Turk are increasingly being used by researchers to construct "virtual labs" in which they can conduct behavioral experiments. In this talk, I describe some recent experiments that showcase the advantages of virtual over traditional physical labs, as well as some of the limitations. I then discuss how this relatively new experimental capability may unfold in the near future, along with some implications for social and behavioral science.
Biography
Duncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. From 2000-2007, he was a professor of Sociology at Columbia University, and then prior to joining Microsoft, a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group . He has also served on the external faculty of the Santa Fe Institute and is currently a visiting fellow at Columbia University and at Nuffield College, Oxford.
His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and the Harvard Business Review. He is also the author of three books, including Six Degrees: The Science of a Connected Age (W.W. Norton, 2003) and Everything is Obvious*: Once You Know The Answer (Crown Business, 2011).
He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.
The Applicant Auction for Top-Level Domains: Using an auction to efficiently resolve conflicts among applicants
Peter Cramton, University of Maryland
Wednesday, November 28, 2012
4:00 PM – 5:00 PM
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Description
The prospect of using auctions to resolve conflicts among parties competing for the same top-level internet domains is described. In such an auction the winner’s payment is divided among the losers, whereas if the conflict is not resolved then ICANN will conduct an auction and retain the winner’s payment. For first-price and second-price sealed-bid auctions, we characterize equilibrium bidding strategies and provide examples, assuming bidders’ valuations are distributed independently and are either symmetrically or asymmetrically distributed. The qualitative properties of equilibria reveal novel features; for example, in a second-price auction a bidder might bid more than her valuation in order to drive up the winner’s payment. Even so, examples indicate that in symmetric cases a bidder’s expected profit is the same in the two auction formats. We then test in the experimental lab two auction formats that extent the setting from a single domain to the actual setting with many domains. The first format is a sequential first-price sealed-bid auction; the second format is a simultaneous ascending clock auction. The framing and subjects were chosen to closely match the actual setting. Subjects were PhD students at the University of Maryland in Economics, Computer Science, and Computer Engineering, with training in game theory and auction theory. Each subject played the role of an actual company (e.g., Google) and bid for domains (e.g., .book) consistent with the company’s applications. Subjects were given instructions explaining the auction and the equilibrium theory for the single-item case in relevant examples. Both formats achieved auction efficiencies of 98% in the lab. This high level of efficiency is especially remarkable in the case with asymmetric distributions—the format performed better than the simple single-item equilibrium despite the presence of budget constraints in the lab. This experiment together with previous results on the robustness of ascending auctions in general and simultaneous ascending clock auctions in particular suggest that the simultaneous ascending clock auction will perform best in this setting.
See “Applicant Auctions for Internet Top-Level Domains: Resolving Conflicts Efficiently” (with Ulrich Gall, Pacharasut Sujarittanonta, and Robert Wilson), Working Paper, University of Maryland, 11 November 2012. [Presentation]
Biography
Peter Cramton is Professor of Economics at the University of Maryland. Since 1983, he has conducted research on auction theory and practice. This research appears in the leading economics journals. The main focus is the design of auctions for many related items. Applications include spectrum auctions, electricity auctions, and treasury auctions. On the practical side, he is Chairman of Market Design Inc., an economics consultancy founded in 1995, focusing on the design of auction markets. He also is Founder and Chairman of Cramton Associates LLC, which since 1993 has provided expert advice on auctions and market design. Since 2001, he has played a lead role in the design and implementation of electricity auctions in France and Belgium, gas auctions in Germany, and the world’s first auction for greenhouse gas emissions held in the UK in 2002. He has advised numerous governments on market design and has advised dozens of bidders in high-stake auction markets. Since 1997, he has advised ISO New England on electricity market design and was a lead designer of New England’s forward capacity auction. He led the design of electricity and gas markets in Colombia, including the Firm Energy Market, the Forward Energy Market, and the Long-term Gas Market. Since June 2006, he played a leading role in the design and development of Ofcom’s spectrum auctions in the UK. He has advised the UK, the US, and Australia on greenhouse gas auction design. He led the development of the FAA’s airport slot auctions for the New York City airports. He received his B.S. in Engineering from Cornell University and his Ph.D. in Business from Stanford University.
Miku: Virtual Idol as Media Platform
Ian Condry, MIT
Wednesday, November 14, 2012
4:00 PM – 5:00 PM
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Description
Miku Hatsune is Japan's number one virtual idol. Her songs are sold online, she is one of the most requested karaoke downloads, she promotes Toyota in TV commercials, she performs concerts with live bands -- and she doesn't exist. Miku is a voice in music synthesizer software, and her community of users have created something new in the world of popular culture: a crowd-sourced celebrity. Based on fieldwork in Japan and the US, this talk will explore the dynamics of the social in media and the value of collaborative creativity.
Biography
Ian Condry is a cultural anthropologist and associate professor of Comparative Media Studies at MIT. His forthcoming book The Soul of Anime: Collaborative Creativity and Japan's Media Success Story (January 2013, Duke University Press) focuses on Japan's anime creators including participant-observation in studios, fan conventions and toy companies. His first book, Hip-Hop Japan: Rap and the Paths of Cultural Globalization (2006) is based on fieldwork in Tokyo nightclubs and recording studios. More info: http://iancondry.com
Music intelligence & the "Taste Profile" - What computers think of you and your music taste
Brian Whitman, The Echo Nest
Wednesday, October 31, 2012
4:00 PM – 5:00 PM
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Description
Over 200 million people now trust an algorithm they’ve never met to listen to and discover music. But music needs a bit more care than collaborative filtering or automated editorial approaches can give, and before we let Facebook automatically make mixtapes for our crushes, we should step back and see what the potential of music analysis is and how we can give it more respect.
For the past 10 years I’ve been working on automatic music analysis, first academically and now as the co-founder and CTO of the Echo Nest, a company you’ve never heard of but powers most music discovery experiences you have on the internet today, from Spotify to Clear Channel to MTV. I’ll show how the interaction between listeners and music is being modeled today, where it is amazing and where it falls flat, and how connections are being made between your music taste and your identity.
Biography
Brian is recognized as a leading scientist in the area of music and text retrieval and natural language processing. He received his doctorate from MIT's Media Lab in 2005 and co-founded The Echo Nest to provide music recommendation, search, playlisting, fingerprinting and personalization technology based on his research to much of the online music industry. As the CTO of the Echo Nest, Brian leads new product development and focuses on future taste profile and music analytic products.
Real applications of non-real numbers
Roger Myerson, The University of Chicago
*Special Date & Time*
Thursday, October 18, 2012
1:30 PM – 2:30 PM
No recording available
Description
This paper considers a simple model of credit cycles driven by moral hazard in financial intermediation. Investment advisors or bankers must earn moral-hazard rents, but the cost of these rents can be efficiently spread over a banker's entire career, by promising large back-loaded rewards if the banker achieves a record of consistently successful investments. The dynamic interactions among different generations of bankers can create equilibrium credit cycles with repeated booms and recessions. We find conditions when taxing workers to subsidize bankers can increase investment and employment enough to make the workers better off.
The paper is at http://home.uchicago.edu/~rmyerson/research/index.html
Biography
Roger Myerson is the Glen A. Lloyd Distinguished Service Professor of Economics at the University of Chicago. He has made seminal contributions to the fields of economics and political science. In game theory, he introduced refinements of Nash's equilibrium concept, and he developed techniques to characterize the effects of communication when individuals have different information. His analysis of incentive constraints in economic communication introduced some of the fundamental ideas in mechanism design theory, including the revelation principle and the revenue-equivalence theorem in auctions and bargaining. Professor Myerson has also applied game-theoretic tools to political science, analyzing how political incentives can be affected by different electoral systems and constitutional structures.
Myerson is the author of Game Theory: Analysis of Conflict (1991) and Probability Models for Economic Decisions (2005). He also has published numerous articles in Econometrica, the Journal of Economic Theory, Games and Decisions, and the International Journal of Game Theory, for which he served as an editorial board member for 10 years.
Professor Myerson has a PhD from Harvard University and taught for 25 years in the Kellogg School of Management at Northwestern University before coming to the University of Chicago in 2001. He is a member of the American Academy of Arts and Sciences and of the National Academy of Sciences. In 2007, he was awarded the 2007 Nobel Memorial Prize in Economic Sciences in recognition of his contributions to mechanism design theory.
Real applications of non-real numbers
Alex Lubotzky, Hebrew U of Jerusalum
Wednesday, October 10. 2012
4:00 PM – 5:00 PM
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Description
The system of real numbers are defined mathematically as a "completion' of the rational numbers. But this is not the only way to do it! In fact there are infinitely many others completions- the so called "p-adic numbers". These numbers were defined for pure mathematical reasons and have been a subject of research for a century. But in the last 3 decades they have found 'real world' applications in computer science, construction of networks, algorithms etc. We will try to tell the story in a way which hopefully will make sense also to non-mathematicians.
Biography
Alex Lubotzky is the Weil Professor of Mathematics at the Hebrew University of Jerusalem and an adjunct prof. of math at Yale University. He got his PhD. from Bar-Ilan University in 1980. Following an army service he joined the Hebrew University in 1983.His main area of research is group theory which he likes to combine with other areas like geometry, number theory, combinatorics and computer science. One of his best known works is the construction of Ramanujan graphs (which are optimal expanders) jointly with Phillips and Sarnak. This opened a world of connections between graph theory and representation theory. Lubotzky is an Honorary Foreign Member of the American Academy of Arts and Science and in 2006 he received an honorary degree from the University of Chicago for his contributions to modern mathematics.
Back to topDiffusion of Microfinance
Matt Jackson, Stanford
Wednesday, October 3. 2012
4:00 PM – 5:00 PM
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Description
We examine how participation in a microfinance program diffuses through social networks, using detailed demographic, social network, and participation data from 43 villages in South India. We exploit exogenous variation in the importance (in a network sense) of the people who were first informed about the program, the "injection points" .Microfinance participation is significantly higher when the injection points have higher eigenvector centrality. We also estimate structural models of diffusion that allow us to (i) determine the relative roles of basic information transmission versus other forms of peer influence, and (ii) distinguish information passing by participants and nonparticipants. We find that participants are significantly more likely to pass informationon to friends and acquaintances than informed non-participants. However, information passing by non-participants is still substantial and significant, accounting for roughly one-third of informedness and participation. We also find that, once we have properly conditioned on an individual being informed, her decision to participate is not significantly affected by the participation of her acquaintances.
Biography
Matthew O. Jackson is the Eberle Professor of Economics at Stanford University and an external faculty member of the Santa Fe Institute and a fellow of CIFAR. Jackson's research interests include game theory, microeconomic theory, and the study of social and economic networks, including diffusion, learning, and network formation. He was at Northwestern and Caltech before joining Stanford, and has a PhD from Stanford and BA from Princeton. Jackson is a Fellow of the Econometric Society and the American Academy of Arts and Sciences, and former Guggenheim Fellow.
Back to topBack to topDuolingo: Learn a Language for Free While Helping to Translate the Web
Luis von Ahn, Carnegie Mellon University
Wednesday, September 19. 2012
4:00 PM – 5:00 PM
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Description
I want to translate the Web into every major language: every webpage, every video, and, yes, even Justin Bieber's tweets. With its content split up into hundreds of languages -- and with over 50% of it in English -- most of the Web is inaccessible to most people in the world. This problem is pressing, now more than ever, with millions of people from China, Russia, Latin America and other quickly developing regions entering the Web. In this talk, I introduce my new project, called Duolingo, which aims at breaking this language barrier, and thus making the Web truly "world wide."
We have all seen how systems such as Google Translate are improving every day at translating the gist of things written in other languages. Unfortunately, they are not yet accurate enough for my purpose: Even when what they spit out is intelligible, it's so badly written that I can't read more than a few lines before getting a headache.
With Duolingo, our goal is to encourage people, like you and me, to translate the Web into their native languages.
Biography
Luis von Ahn is the A. Nico Habermann Associate Professor of Computer Science at Carnegie Mellon University. He is working to develop a new area of computer science that he calls Human Computation, which aims to build systems that combine the intelligence of humans and computers to solve large-scale problems that neither can solve alone. An example of his work is reCAPTCHA, in which over one billion people -- 15% of humanity -- have helped digitize books and newspapers. Among his many honors are a MacArthur Fellowship, a Packard Fellowship, a Sloan Research Fellowship, a Microsoft New Faculty Fellowship, the ACM Grace Hopper Award, and CMU's Herbert A. Simon Award for Teaching Excellence and Alan J. Perlis Teaching Award. He has been named one of the "50 Best Brains in Science" by Discover Magazine, one of the 50 most influential people in technology by silicon.com, and one of the "Brilliant 10 Scientists" by Popular Science Magazine.
How users evaluate things and each other in social media
Jure Leskovec, Stanford University
Wednesday, September 5. 2012
4:00 PM – 5:00 PM
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Description
In a variety of domains, mechanisms for evaluation allow one user to say whether he or she trusts another user, or likes the content they produced, or wants to confer special levels of authority or responsibility on them. We investigate a number of fundamental ways in which user and item characteristics affect the evaluations in online settings. For example, evaluations are not unidimensional but include multiple aspects that all together contribute to user’s overall rating. We investigate methods for modeling attitudes and attributes from online reviews that help us better understand user’s individual preferences. We also examine how to create a composite description of evaluations that accurately reflects some type of cumulative opinion of a community. Natural applications of these investigations include predicting the evaluation outcomes based on user characteristics and to estimate the chance of a favorable overall evaluation from a group knowing only the attributes of the group's members, but not their expressed opinions
Biography
Jure Leskovec is assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining large social and information networks.Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including best paper awards at KDD (2005, 2007, 2010), WSDM (2011), ICDM (2011) and ASCE J. of Water Resources Planning and Management (2009), ACM KDD dissertation award (2009), Microsoft Research Faculty Fellowship (2011), Alfred P. Sloan Fellowship (2012) and NSF Early Career Development (CAREER) Award (2011). He received his bachelor's degree in computer science from University of Ljubljana, Slovenia, Ph.D. in machine learning from the Carnegie Mellon University and postdoctoral training from Cornell University. You can follow him on Twitter @jure
Playing “Hide and Seek” - The hidden genome
Michal Linial, The Hebrew University of Jerusalem, Israel
Wednesday, August 30. 2012
4:00 PM – 5:00 PM
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Description
The overwhelming increase in sequencing methodology resulted in the accumulation of millions of DNA sequences. These sequences are collected from thousands of genomes that (ideally) sample the ‘tree of life’. I will briefly discuss the ‘minimal set of instructions’ by which a linear sequence is transformed into a functional protein. What happen when the statistical noise is too high, thus classical procedures to predict protein sequences fail? I will focus on the challenge of identifying short proteins that remain buried in the genomic data. For illustration, I will take you for a ‘treasure hunt’ for short proteins.
Many short proteins share fuzzy features that are common to most animal venom. I will discuss the limitation in using classical tools that are based on string comparison, or pattern finding to identify short proteins. For this task, statistical machine learning methods were useful in identifying hidden bioactive sequences in several genomes. Evidently, such sequences are attractive candidates for novel therapy. The test case of short proteins illustrates the importance of a cycle that starts by a biological hypothesis, then uses a computational formulation and finalizes by an experimental validation. Finally, I will discuss our genomes with respect to our ‘partners’ (viruses, bacteria). Once the interaction of these genomes is considered, the source for the dynamic nature of human evolution becomes evident. Related publications:
• Rappoport N, Karsenty S, Stern A, Linial N, Linial M. (2012) Nucl. Acids Res. 40:D313-D320
• Rappoport N, Linial M. (2012) PLoS Comput Biol. 8:e1002364.
• Naamati G, Askenazi M, Linial M. (2010) Bioinformatics 26:i482-i488.
• Naamati G, Askenazi M, Linial M (2009) Nucl. Acids Res. 37:W363-368.
• Kaplan N, Morpurgo N, Linial M. (2007) J Mol Biol. 369:553-566.
Biography
Michal Linial is a Professor of Biochemistry, The Hebrew University, Jerusalem, Israel and a Director of the SCCB, the Sudarsky Center for Computational Biology.ML had published over 150 scientific papers and abstracts on diverse topics in molecular biology, cellular biology, bioinformatics, neuroscience the integration of tools to improve knowledge extractions. M. Linial has an experimental and computational laboratory. M.L is the leader and the founder of the first established educational program in Israel for Computer Science and Life Science (from 1999) for Undergraduate-Graduate studies. Her expertise in the synapse let to the study of protein families, protein-protein interactions with a global view on protein networks and their regulation. Molecular biology, cell biology and biochemical methods are applied in all research initiated in her laboratory. She and her laboratory are developing new computational and technological tools for large-scale cell biological research M. Linial and her colleagues apply MS based and genomics (DNA Chip) approaches for studying changes in neuronal development, and disease oriented research. She published over 180 scientific papers including book chapters and numerous reviews.The solid informatics approaches are used for large database storage and constant updating of several systems in view of classification, validation and functional predictions. M.L. and her students has been an active participant in NIH structural genomics initiatives and she participated in Structural Genomics effort Task for target selections. She and her colleagues have created several global classification systems that are used by the biomedical and biology communities. Most notably are the ProtoNet, EVEREST, ProTarget and PANDORA, mirror, ClanTox and more. All those developed web systems are provided as an open source for investigators.
Back to topBack to topDynamic Games with Asymmetric Information: A Framework for Empirical Work
Ariel Pakes, Harvard University
Wednesday, August 29. 2012
4:00 PM – 5:00 PM
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Description
We develop a framework for the analysis of dynamic games that can be applied to the analysis of firm which compete in a market whose characteristics evolve over time as a probabilistic function of the actions of the firms competing in that market. Firm's chose their actions to maximize their perceptions of the discounted value of the returns that will accrue to them as a result of those actions. These returns depend on both their own states and their competitor's states. The firms know their own states, but only observe imprecise signals on the states of their competitors. Our goal is to provide a framework capable of analyzing the impact of policy or environmental changes in such a setting. Bayesian perfect Nash equilibria for environments that are rich enough to adequately approximate behavior have computational and informational demands that both; (i) make them impossible for applied researchers to use, and (ii) unlikely to be the best approximation to agent's actual behavior. So we introduce an alternative notion of equilibria which is less demanding of both agents and researchers, while still implying agents "optimize" in a meaningful sense of that word. We show that: (i) there is an artificial intelligence algorithm that makes it relatively easy to compute (at least some of) the resultant equilibria, and (ii) it is relatively easy to use the properties of that equilibria to estimate any unknown parameters of the game. We use the analysis of a de-regulated electric utility market as an example. Two firms each own several generators and bid "supply functions" into the market in every period (a quantity supplied as an increasing function of price). An independent system operator (an ISO) sums the supply curves horizontally and intersects the result with demand to determine the period's price and the quantities to be produced by each firm. The firm's cost of supplying electricity on each of its generators is increasing in the current quantity produced and stochastically increasing in the quantities produced since the last time the firm did maintenance on that generator. Firms do not know the current cost of their competitor's generators but realize that the returns they will earn from the bid on each of its generator will increase the less the quantity supplied by other generators (their own, as well as those of its competitors). This provides incentives for firms to simply shut down some generators without doing maintenance, and to implicitly co-ordinate shutdowns across firms. Consumers pay the price through the resultant increase in the price of electricity. Joint work with Chaim Fershtman
Biography
Ariel Pakes is the Steven McArthur Heller Professor of Economics in the Department of Economics at Harvard University, where he teaches courses in Industrial Organization and in Econometrics. Before coming to Harvard in 1999, he was the Charles and Dorothea Dilley Professor of Economics at Yale University (1997-99). He has held other tenured positions at Yale (1988-97), the University of Wisconsin (1986-88), and the University of Jerusalem (1985-86). Pakes received his doctorate degree from Harvard University in 1980, and he stayed at Harvard as a Lecturer until he took up a position in Jerusalem in 1981. Pakes received the award for the best graduate student advisor at Yale in 1996. Pakes was elected fellow of the American Academy of Arts and Sciences in 2002. He received the Frisch Medal of the Econometric Society in 1986, was elected as a fellow of that society in 1988, and gave the Fisher-Schultz lecture at the World Congress of that society in 2005. He was the Distinguished Fellow of the Industrial Organization Society in 2007. He has been on the editorial boards of the RAND Journal of Economics, Econometrica, Economic Letters and the Journal of Economic Dynamics and Control. He is also a research associate of the NBER, and has been member of the AEA Committee on Government Statistics, the chair of the AEA Census Advisory Panel, and co-editor of a Proceedings of the National Academy of Science issue on "Science, Technology and the Economy". Professor Pakes' research has been in Industrial Organization (I.O.), the Economics of Technological Change and in Econometric Theory. He and his co-authors have focused on developing techniques which allow us to analyze market responses to policy and environmental changes. This includes; econometric work on how to estimate demand and cost systems and then use the estimated parameters to analyze equilibrium responses in different institutional settings, empirical work which uses these techniques to analyze market outcomes in different industries, and theoretical work developing frameworks for the applied analysis of dynamic oligopolies (with and without collusive possibilities, and with and without asymmetric information).
Gender, Competitiveness and Career Choices
Muriel Niederle, Stanford University
Wednesday, August 22. 2012
4:00 PM – 5:00 PM
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Description
Gender differences in competitiveness are often discussed as potential explanation for gender differences in labor market outcomes. We correlate an incentivized measure of competitiveness with the first important career choice of secondary school students in the Netherlands. At the age of 15, these students have to pick one out of four study profiles, which vary in how prestigious they are. While boys and girls have very similar levels of academic ability, boys are substantially more likely than girls to choose more prestigious profiles. We find that 25% of this gender difference can be attributed to gender differences in competitiveness. This lends support to the extrapolation of laboratory findings on competitiveness to labor market settings. Joint work with Hessel Oosterbeek and Thomas Buser.
Biography
Muriel is a Professor of Economics at Stanford. In her own words: I am an experimental economist, and as such, have some experiments that fall outside my main areas of gender or market design. Most recently, I got interested in k-level models. The first strand of literature I am working on can be broadly thought of as market design. While that includes studying markets that have been designed (such as the National Residency Matching Market), I am also interested redesigning markets, or adding features such as signaling to help markets such as the economics job market work better. Most recently, I have been getting involved in working with the San Francisco Unified School District to help redesign their school choice system. In market design, I have used theory, experiments, as well as data collected by others. My second strand of work is work on gender differences. So far, I have only experimental papers in that are, showing that women may not be as competitive as men, especially when they have to compete against men.
Wireless Spectrum Sharing: Opportunities for Interdisciplinary Research
Anant Sahai, University of California, Berkeley
Wednesday, August 8, 2012
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Description
Under the current static system of frequency assignment, a great deal of spectrum remains underused. This seeming waste represents an opportunity for frequency-agile cognitive radios to improve performance. Understanding this opportunity forces us to take a closer look at the whole question of "regulatory overhead." Until recently, cognitive radios represented the "Medical Marijuana" of wireless research --- rhetoric on both sides characterized by distrust, wishful thinking, and vested interests, but the underlying "technology" in question was still very much illegal. Regulatory changes were required before research in this area could truly impact practice. Recent steps taken by the FCC in the TV Whitespaces demonstrate that the government is serious about change, and just last month, the President's Council of Advisers on Science and Technology (PCAST) released a report that advocated expanding this approach beyond the TV bands. However, the problem is that while we have a rough sense of what we want to achieve at a high level, as a community, we do not yet know what this regulatory change should entail at the detailed level and more troubling, even how we would recognize the right answer if we saw it.The full scope of the problem weaves together information theory, signal processing, economics, and law in a nontrivial way (and probably also cryptography and social networks). In this talk, I will give an introduction to the opportunity in the context of the TV Whitespaces. I'll use some simulations based on real FCC data and realistic propagation models to give a quantitative sense of the tradeoffs involved, and then show idealized models that enable a conceptual understanding of the "overhead" in the context of spectrum sensing. I will then elucidate what "light handed regulation" could mean in the cognitive radio context, giving a simple criminal-law inspired model to reveal something about the overhead and tradeoffs involved. I'll close with some interesting future research directions.
Biography
Anant Sahai (BS '94 UC Berkeley, MS '96 MIT, PhD '01 MIT) is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California at Berkeley, where he joined the faculty in 2002. He is a member of the Berkeley Wireless Research Center (BWRC) and the Wireless Foundations Center (WiFo). In 2001, he spent a year at the wireless startup Enuvis developing adaptive signal processing algorithms for extremely sensitive GPS receivers implemented using software defined radio. Prior to that, he was a graduate student at the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). His research interests are in wireless communication, decentralized control, and information theory. He is particularly interested in delay, feedback, and complexity from an information-theoretic perspective and in cognitive radio from a regulatory perspective.
Back to topThe Wonders of the Probabilistic Method
Nati Linial, Hebrew University
Wednesday, August 8, 2012
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Description
I will try to explain some key principles in modern mathematics which combine ideas from combinatorics and probability. In particular I will emphasize the surprising role that probability theory plays in the study of combinatorics. How it allows us to investigate complicated graphs and networks without having to reveal all the specific details of individual large graphs or networks. This talk is intended for a general audience. The necessary mathematical background is at the level of good high-school education.
Biography
Nati Linial is a professor of computer science at the Hebrew University of Jerusalem. In his own words: I got my undergraduate education in mathematics at the Technion. I did my PhD at the Hebrew University with a thesis in graph theory. Following a postdoctoral period at UCLA math I joined the faculty of the Hebrew University. My main areas of interest are combinatorics, theoretical computer science and bioinformatics. I had about 30 graduate students so far (currently I have 7 PhD students and one MSc student) I am married to Michal, a life-science professor at the Hebrew University. We have three children who are, respectively, an artist, a poet and a budding physicist. I like long-distance running, reading and classical music.
Lines, Shading, and the Perception of 3D Shape
Ted Adelson, Massachusetts Institute of Technology
Wednesday, August 1, 2012
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Description
Humans can easily see 3D shape from single 2D images, exploiting multiple kinds of information. There are several subfields (in both human vision and computer vision) devoted to the study of particular cues to 3D shape, such as shading, texture, and contours. However, the resulting algorithms remain specialized and fragile (in contrast with the flexibility and robustness of human vision). Recent work in graphics and psychophysics has demonstrated the importance of local orientation structure in conveying 3D shape. This information is fairly stable and reliable across rendering condition. We have developed an exemplar-based system (which we call Shape Collage) that learns to associate image patches with corresponding 3D shape patches.
We train it with synthetic images of “blobby” objects rendered in various ways, including solid texture, Phong shading, and line drawings. Given a new image, it finds the best candidate scene patches and assembles them into a coherent interpretation of the object shape. Our system is the first that can retrieve the shape of naturalistic objects from line drawings. The same system, without modification, works for shape-from-texture and can also do shape-from-shading without requiring Lambertian surfaces. Thus disparate types of image information can be processed by a single mechanism to extract 3D shape.
(Collaborative work with Forrester Cole, Phillip Isola, Fredo Durand, and William Freeman.)
Random Graph Models of Kidney Exchange
Al Roth, Harvard University
Wednesday, July 25. 2012
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Description
Kidney exchange involves creating a non-monetary marketplace through which incompatible donors and patients can take part in exchanges, so that each patient in the exchange receives a transplant from a compatible donor. I’ll recount the brief history of kidney exchange, explain some of the technical problems that have been overcome, and some which remain.
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