- talks - lists.cs.princeton.edu

Colloquium Speaker Sergey Yekhanin Wed Oct 23 4:30pm
by Nicole E. Wagenblast 22 Oct '13

22 Oct '13

Local erasure coding for data storage Sergey Yekhanin , Microsoft Research Wednesday, October 23- 4:30pm Computer Science 105 Historically, most large distributed storage systems (e.g., Hotmail) have been using replication to provide reliability against machine failures. Today however as the amount of stored data reaches multiple Exabytes keeping few copies of data around is becoming prohibitively expensive. Therefore more and more systems are adopting erasure coding in place of replication. Local Reconstruction Codes (LRCs) are a new class of erasure correcting codes designed specifically for applications in data storage. Built upon the rich mathematical theory of locally decodable codes developed in the theory community, LRCs provide high level of reliability and allow data fragments to be reconstructed quickly in typical failure scenarios. LRCs have been recently deployed by Windows Azure Storage and are going to ship in Windows 8.1 and Windows Server 2012. In this talk we will discuss motivation behind local reconstruction codes and cover the main technical challenges and tradeoffs in the design of these codes. (Based on joint papers with Brad Calder, Michael Forbes, Parikshit Gopalan, Cheng Huang, Bob Jenkins, Jin Li, Aaron Ogus, Huseyin Simitci, and Yikang Xu.)

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R Ge FPO
by Melissa M. Lawson 18 Oct '13

18 Oct '13

Rong Ge will present his FPO on Thursday October 24 at 10:30AM in Room 402. The members of his committee are: Sanjeev Arora, advisor; Moses Charikar and Sham Kakade (MSR New England), readers; Mark Braverman and Zeev Dvir,nonreaders. A copyof his thesis, "Provable Algorithms for Machine Learning Problems", is on display in Room 310. Everyone is invited to attend his talk.

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Distinguished Colloquium Speaker Wed Oct 16 4:30pm Friend 006
by Nicole E. Wagenblast 16 Oct '13

16 Oct '13

On the Computational and Statistical Interface and "Big Data" Michael I. Jordan , University of California, Berkeley Wednesday, October 16th, 4:30pm Friend Center, 006 The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the statistical and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked. Indeed, if data are a data analyst's principal resource, why should more data be burdensome in some sense? Shouldn't it be possible to exploit the increasing inferential strength of data at scale to keep computational complexity at bay? I present three research vignettes that pursue this theme, the first involving the deployment of resampling methods such as the bootstrap on parallel and distributed computing platforms, the second involving large-scale matrix completion, and the third introducing a methodology of "algorithmic weakening," whereby hierarchies of convex relaxations are used to control statistical risk as data accrue. Joint work with Venkat Chandrasekaran, Ariel Kleiner, Lester Mackey, Purna Sarkar, and Ameet Talwalkar. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in statistical genetics, signal processing, natural language processing and distributed computing systems. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics, and has received the ACM/AAAI Allen Newell Award. He is a Fellow of the AAAI, ACM, ASA, CSS, IMS, IEEE and SIAM.

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Colloquium Speaker Kevin Fu Thurs Oct 10 4:30pm Friend Center 006
by Nicole E. Wagenblast 10 Oct '13

10 Oct '13

Medical Device Cyber Security: The First 164 Years Kevin Fu , University of Michigan Thursday, October 10- 4:30pm Friend Center 006 The U.S. Institute of Medicine commissioned my 2011 report on the role of trustworthy software in the context of the "510(k)" U.S. medical device regulations. More recently, the U.S. Food and Drug Administration has released draft guidance on cybersecurity for medical device manufacturing. This talk will provide a glimpse into the risks, benefits, and regulatory issues for medical device cybersecurity and innovation of trustworthy medical device software. Today, it would be difficult to find medical device technology that does not critically depend on computer software. The technology enables patients to lead more normal and healthy lives. However, medical devices that rely on software (e.g., drug infusion pumps, linear accelerators) continue to injure or kill patients in preventable ways---despite the lessons learned from the tragic radiation incidents of the Therac-25 era. The lack of trustworthy medical device software leads to shortfalls in properties such as safety, effectiveness, dependability, reliability, usability, security, and privacy. Come learn a bit about the science, technology, and policy that shapes medical device software. Kevin Fu is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan where he directs the Archimedes Center for Medical Device Security and the SPQR group. His research investigates how to achieve trustworthy computing on embedded devices with application to health care, commerce, and communication. His most recent contributions appear in computer science and medical conferences and journals such as USENIX Security and IEEE Security and Privacy. Kevin received his PhD in EECS from the MIT. Fu received a Sloan Research Fellowship, NSF CAREER award, Fed100 Award, and best paper awards from various academic silos of computing. The research is featured in critical articles by the NYT, WSJ, and NPR. Kevin was named MIT Technology Review TR35 Innovator of the Year for work on medical device security. Kevin has testified in Congress on health matters and has written commissioned work for the Institute of Medicine of the National Academies. He served as a visiting scientist at the Food & Drug Administration, the Beth Israel Deaconess Medical Center of Harvard Medical School, Microsoft Research, and MIT CSAIL. Previous employers include Bellcore, Cisco Systems, HP Labs, and Holland Community Hospital. He is a member of the ACM Committee on Computers and Public Policy and the NIST Information Security and Privacy Advisory Board. Kevin also holds a certificate of achievement in artisanal bread making from the French Culinary Institute.

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A Chaney general exam
by Melissa M. Lawson 08 Oct '13

08 Oct '13

Allison Chaney will present her research seminar/general exam on Tuesday October 15 at 2PM in Room 301 (note room). The members of her committee are: David Blei (advisor), Rebecca Fiebrink, and Andrea LaPaugh. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. ----- Original Message ----- Abstract Each of us are faced with the problem of selecting which books to read and movies to watch. Traditionally, we ask our trusted friends for recommendations, but algorithmic recommendation models make those choices even easier, saving us time and effort by steering us towards media we are more likely to enjoy. The downside to most probabilistic recommendations is that for some people, part of the appeal of reading or consuming other media is in creating shared experiences with friends. I present a model that incorporates social network information into recommendation models, reintroducing the social aspect to recommendation; this approach also has the potential to improve overall recommendations. This model discovers the latent trust that exists between users in a network and allows us to consider which users are more trustworthy than others, providing us insight into the social network's dynamics. Papers 1. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation . Journal of Machine Learning Research , 3:993–1022, January 2003. http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf 2. C. Wang and D. Blei. Collaborative topic modeling for recommending scientific articles. Knowledge Discovery and Data Mining , 2011. http://www.cs.cmu.edu/~chongw/papers/WangBlei2011.pdf 3. P. Gopalan, Scalable Recommendation with Poisson Factorization (forthcoming, supplied on request) 4. S. Purushotham, Y. Liu, and C.-C. J. Kuo. Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems . International Conference on Machine Learning, 2012. http://icml.cc/2012/papers/407.pdf 5. M. Hao, Y. Haixuan, M. R. Lyu , I. King. SoRec: Social Recommendation Using Probabilistic Matrix Factorization . Proceedings of the 17th ACM conference on Information and knowledge management , pg. 931–940, October 2008. http://appsrv.cse.cuhk.edu.hk/~hma/Paper_CIKM08_SoRec_Hao.pdf 6. H. Shan and A. Banerjee, Generalized Probabilistic Matrix Factorizations for Collaborative Filtering http://www-users.cs.umn.edu/~shan/icdm10_gpmf.pdf 7. Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems . Computer , 42:30–37, August 2009. http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf 8. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating Collaborative Filtering Recommender Systems . ACM Transactions on Information Systems , 2004. http://dl.acm.org/citation.cfm?doid=963770.963772 9. M. Hoffman, D. Blei, J. Paisley, and C. Wang. Stochastic variational inference . Journal of Machine Learning Research , 2013. http://www.cs.princeton.edu/~blei/papers/HoffmanBleiWangPaisley2013.pdf Selected Sections of Textbooks 1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach , Prentice Hall Series in Artificial Intelligence, 2003: Chapters: 3.1-3.5, 4.1, 5, 8.1-8.3, 13.1-13.5, 14, 15, 18, 21 2. Bishop, C. M., Pattern Recognition and Machine Learning , Springer , 2006 : Chapters 1, 2, 3.1-3.3, 4.1-4.3, 8, 9, 10, 11.1-11.3, 12.1-12.2, 13, 14.1, 14.3 3. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysi s, Taylor & Francis, 1995: chapters 5, 6, 11 4. Spall, J. C., Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control , Wiley, 2003: Ch 2 5. J. Lazar, J. Feng, and H. Hochheiser. Research Methods in Human-Computer Interaction. John Wiley & Sons, 2010: Chapters 2,3,10-12 6. R ecommender Systems Handbook, Springer , 2010. Chapter 8: Evaluating Recommendation Systems by G. Shani and A. Gunawardana.

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A Bell FPO
by Melissa M. Lawson 03 Oct '13

03 Oct '13

Ana Bell will present her FPO on Friday October 11 at 9AM in Room 402. The members of her committee are: Olga Troyanskaya, advisor; Mona Singh and Andrea LaPaugh, readers; Tom Funkhouser and Kai Li, nonreaders. A copy of her thesis, "Integrating Genomic Data to Build Networks for Proteins and Small Molecules", is on display in Room 310. Everyone is invited to attend her talk.

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Colloquium Speaker Kevin Fu, Thurs Oct 10, 4:30pm, Friend 006
by Nicole E. Wagenblast 02 Oct '13

02 Oct '13

Medical Device Cyber Security: The First 164 Years Kevin Fu , University of Michigan Thursday, October 10, 4:30pm Friend Center, 006 The U.S. Institute of Medicine commissioned my 2011 report on the role of trustworthy software in the context of the "510(k)" U.S. medical device regulations. More recently, the U.S. Food and Drug Administration has released draft guidance on cybersecurity for medical device manufacturing. This talk will provide a glimpse into the risks, benefits, and regulatory issues for medical device cybersecurity and innovation of trustworthy medical device software Today, it would be difficult to find medical device technology that does not critically depend on computer software. The technology enables patients to lead more normal and healthy lives. However, medical devices that rely on software (e.g., drug infusion pumps, linear accelerators) continue to injure or kill patients in preventable ways---despite the lessons learned from the tragic radiation incidents of the Therac-25 era. The lack of trustworthy medical device software leads to shortfalls in properties such as safety, effectiveness, dependability, reliability, usability, security, and privacy. Come learn a bit about the science, technology, and policy that shapes medical device software. Kevin Fu is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan where he directs the Archimedes Center for Medical Device Security and the SPQR group. His research investigates how to achieve trustworthy computing on embedded devices with application to health care, commerce, and communication. His most recent contributions appear in computer science and medical conferences and journals such as USENIX Security and IEEE Security and Privacy. Kevin received his PhD in EECS from the MIT. Fu received a Sloan Research Fellowship, NSF CAREER award, Fed100 Award, and best paper awards from various academic silos of computing. The research is featured in critical articles by the NYT, WSJ, and NPR. Kevin was named MIT Technology Review TR35 Innovator of the Year for work on medical device security. Kevin has testified in Congress on health matters and has written commissioned work for the Institute of Medicine of the National Academies. He served as a visiting scientist at the Food & Drug Administration, the Beth Israel Deaconess Medical Center of Harvard Medical School, Microsoft Research, and MIT CSAIL. Previous employers include Bellcore, Cisco Systems, HP Labs, and Holland Community Hospital. He is a member of the ACM Committee on Computers and Public Policy and the NIST Information Security and Privacy Advisory Board. Kevin also holds a certificate of achievement in artisanal bread making from the French Culinary Institute.

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Colloquium Speaker Rajiv Gandhi Wed Oct 2nd 4:30pm
by Nicole E. Wagenblast 01 Oct '13

01 Oct '13

>From Potential to Promise - Developing Scholars, one Eureka moment at a time Rajiv Gandhi , Rutgers University, Camden Wednesday, October 2, 4:30pm Computer Science 105 In this talk, I will tell the story of our work with some truly remarkable undergraduate students at Rutgers-Camden, who despite many odds have achieved success that is unprecedented for the Camden campus. I will discuss the various challenges that we faced and some ideas that have worked very well (and some that have not) for us. We have been applying some of these ideas in our work with high school students and students at other institutions. This talk should be of interest to all faculty and students. Dr. Rajiv Gandhi is an Associate Professor of Computer Science at the Rutgers University-Camden. He received his Ph.D. in Computer Science from the University of Maryland, College Park in 2003. He worked as a software engineer at Qualcomm from 1994-96. His research interests lie in the broad area of theoretical computer science. Specifically, he is interested in approximation and randomized algorithms, distributed algorithms, and graph theory. He has published papers in these areas in leading journals and conferences. He has been the recipient of several teaching excellence awards -- at Rutgers-Camden and at other universities. He was also the recipient of the Chancellor's award for Civic Engagement at Rutgers-Camden in 2013. He was a Fulbright Fellow from Jan-June 2011, during which he worked with students in Mumbai. Since 2009, he has also been working with high school students as part of the Program in Discrete Mathematics and Computer Science.

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Colloquium Speaker: Amit Sahai, Wed. Sept. 25 4:30pm
by Nicole E. Wagenblast 24 Sep '13

24 Sep '13

How to Encrypt Software Amit Sahai , UCLA Wednesday, September 25, 4:30pm Computer Science 105 The goal of secure program obfuscation is to make a program “unintelligible” while preserving its functionality. For decades, program obfuscation for general programs has remained an art, with all public general-purpose obfuscation methods known to be broken. In this talk, we will describe new developments this year that for the first time provide a mathematical approach to the problem of general-purpose program obfuscation, where extracting secrets from the obfuscated program requires solving mathematical problems that currently take hundreds of years to solve on the world's fastest computing systems. We will also discuss the implications of these developments. This talk is based on joint works with Sanjam Garg, Craig Gentry, Shai Halevi, Mariana Raykova, and Brent Waters. Professor Amit Sahai received his Ph.D. in Computer Science from MIT in 2000. From 2000 to 2004, he was on the faculty at Princeton University; in 2004 he joined UCLA, where he currently holds the position of Professor of Computer Science. His research interests are in security and cryptography, and theoretical computer science more broadly. He has published more than 100 original technical research papers at venues such as the ACM Symposium on Theory of Computing (STOC), CRYPTO, and the Journal of the ACM. He has given a number of invited talks at institutions such as MIT, Stanford, and Berkeley, including the 2004 Distinguished Cryptographer Lecture Series at NTT Labs, Japan. Professor Sahai is the recipient of numerous honors; he was named an Alfred P. Sloan Foundation Research Fellow in 2002, received an Okawa Research Grant Award in 2007, a Xerox Foundation Faculty Award in 2010, a Google Faculty Research Award in 2010, and the 2013 Pazy Memorial Award.

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Talk: Ashutosh Saxena - Monday 12:30 CS 402
by Nicole E. Wagenblast 23 Sep '13

23 Sep '13

Date: Monday, September 23, 2013 Time: 12:30-13:30 Room: CS 402 Title: How should a robot perceive the world? Speaker: Ashutosh Saxena Abstract: In order to perform assistive tasks, a robot should perceive a functional understanding of the environment. This comprises learning how the objects in the environment could be used (i.e., their affordances). In this talk, I will discuss what types of object representations could be useful. One challenge is to model the object's context with each other and with (hidden) humans. Our learning algorithm will be Infinite Latent CRFs (ILCRFs) that allow modeling the data with different plausible graph structures. Unlike CRFs, where the graph structure is fixed, ILCRFs learn distributions over possible graph structures in an unsupervised manner. We then show that our idea of modeling environments using object affordances and hidden humans is not only useful for robot manipulation tasks such as arranging a disorganized house, haptic manipulation, unloading items from a dishwasher, but also in significantly improving standard robotic tasks such as scene segmentation, 3D object detection, human activity detection and anticipation, and task and path planning. Bio: Ashutosh Saxena is an assistant professor in computer science department at Cornell University. His research interests include machine learning and robotics perception, especially in the domain of personal robotics. He received his MS in 2006 and Ph.D. in 2009 from Stanford University, and his B.Tech. in 2004 from Indian Institute of Technology (IIT) Kanpur. He is a recipient of National Talent Scholar award in India, Google Faculty award, Alfred P. Sloan Fellowship, Microsoft Faculty Fellowship, and NSF Career award. In the past, Ashutosh developed Make3D (http://make3d.cs.cornell.edu), an algorithm that converts a single photograph into a 3D model. Tens of thousands of users used this technology to convert their pictures to 3D. He has also developed algorithms that enable robots (such as STAIR, POLAR, see http://pr.cs.cornell.edu) to perform household chores such as unload items from a dishwasher, place items in a fridge, etc. His work has received substantial amount of attention in popular press, including the front-page of New York Times, BBC, ABC, New Scientist, Discovery Science, and Wired Magazine. He has won best paper awards in 3DRR, IEEE ACE and RSS, and was named a co-chair of the IEEE technical committee on robot learning. -- Best, Jianxiong Xiao Assistant Professor Department of Computer Science Princeton University http://vision.princeton.edu

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