- talks - lists.cs.princeton.edu

R Dannenfelser MSE talk - time change
by Melissa M. Lawson 24 Apr '14

24 Apr '14

Ruth's talk has been changed to a 2:45 start time from 3PM. --Melissa

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Fwd: MSE talk abstract
by Melissa M. Lawson 24 Apr '14

24 Apr '14

Christian Eubank will present his MSE thesis talk on Tuesday April 29 at 11AM in Room 301. The members of his committee are: Arvind Narayanan, advisor and Ed Felten, reader. Everyone is invited to attend his talk. His abstract follows below. ----- Original Message ----- Web privacy measurement experiments have heavily influenced privacy debates by shedding light into practices such as third party online tracking and price discrimination. However, these research experiments have typically been one-off projects, with different groups encountering similar methodological and engineering challenges without forming an institutional knowledge base. As an illustrative example of this trend of repeating effort through self-contained experiments, we present the results of one study comparing mobile and desktop tracking and discuss how challenges from this study and other works in the literature shaped the formation of general design principles intended to improve the efficiency of such experiments. We present a robust and modular web measurement platform that enables scalable and repeatable experiments while avoiding many common pitfalls observed by the research community. We describe case studies performed on this framework, including the detection of unique cookie identifiers, the detection of third-party synchronization of these IDs and an examination into the personalization of the content on news sites based on user history. Finally, we present an online web privacy measurement census that visualizes cookie ID information flows over time using data periodically collected and updated by our infrastructure.

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7:30pm Mon Apr 28 on buying energy for the princeton campus Friend Center, 109 - Thomas A. Nyquist
by Jennifer Rexford 22 Apr '14

22 Apr '14

> From: "Barbara J. Fruhling" <bfruhlin(a)Princeton.EDU> > Date: April 22, 2014 at 12:43:36 PM EDT > To: ee-seminar(a)Princeton.EDU > Subject: EE Seminar - April 28, 2014, 7:30 pm, Friend Center, 109 - Thomas A. Nyquist > Reply-To: "Barbara J. Fruhling" <bfruhlin(a)Princeton.EDU> > > > > > > Speaker: Thomas A. Nyquist > Princeton University Executive Director, Engineering and Campus Energy, Engineering and Construction > Title: Buying energy for the Princeton campus > Lecture series on Contemporary Challenges in Electric Power > Date: Monday, April 28, 2014 > Time: 7:30 p.m. > Room: Friend Center, 109 > Host: Prof. Sigurd Wagner > > Outline: > > 1. How we used to purchase power and our plant operations when we operated using the old PSEG tariff. > > 2. The new PJM electric market and how we changed our operations to adapt to the new economic structure. > > 3. Why we changed the design of our new chilled water plant in response to the new PJM rates. > > 4. Explain new Federal Energy Regulatory Commission orders; our response to them; and the economic benefits. > > 5. Finally, the CO2 benefits of cogeneration, and potential operational changes to reduce CO2 emissions. >

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PRISM/PCCM Seminar Series, Wednesday, April 23, Prof. Steven M. Girvin, Yale University, 12:00 noon in Bowen Hall Auditorium 222
by Nicole E. Wagenblast 21 Apr '14

21 Apr '14

Please join us Wednesday, April 23, for the next seminar of the PRISM/PCCM Spring ’14 Seminar Series - featuring Prof. Steven M. Girvin. A complete list of seminar speakers is available at: http:www.princeton.edu/prism Speaker: Prof. Steven M. Girvin Yale University Sloane Physics Laboratory Department of Physics Topic : “ Quantum Measurements and Back-Action (Spooky and Otherwise) ” Host : Prof. Hakan E. Tureci, Department of Electrical Engineering Place : Bowen Hall Auditorium Rm. 222 Time : 12:00 PM - 1:00 PM There will be a light lunch for Seminar attendees provided beforehand at 11:30 AM in the Bowen Hall Atrium. Abstract : The topic of measurements and measurement back-action in quantum mechanics was endlessly confusing to the founders of quantum mechanics who were forced to argue in terms of gedanken experiments. Now we must face this confusion because real experiments are happening. This talk will present an introductory tutorial on how real measurements work and how they affect the state of the quantum system under observation. The basic concepts will be illustrated with the Stern-Gerlach experiment and then extended to include dispersive coupling of atoms or qubits to resonant cavities. For more information on PRISM/PCCM seminars, please contact: Sheila Gunning _____________________ (Ms.) Sheila Gunning Administrative Assistant to the Director, James C. Sturm Princeton Institute for the Science and Technology of Materials (PRISM) Princeton University, 321 Bowen Hall 70 Prospect Avenue, Princeton, NJ 08540 Telephone 609.258.1575, Facsimile 609.258.1177 Email: sheila(a)princeton.edu www.prism.princeton.edu

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R Dannenfelser MSE thesis talk
by Melissa M. Lawson 21 Apr '14

21 Apr '14

Ruth Dannenfelser will present her MSE thesis talk on Thursday April 24 at 3PM in room 301 (note room!). The members of her committee are Olga Troyanskaya, advisor and Kai Li, reader. Everyone is invited to attend her talk. Her abstract follows below. ----- Original Message ----- The immune system is a complex set of biological processes and structures that work together to protect the human body. A main component of this system are the lymphocytes, white blood cells, which are responsible for identifying and destroying invading cells, including tumors. In breast cancer it has been shown that the level of the immune infiltrate in the tumors is one of the key determinants of prognosis and survival. However, a more detailed view of the relative abundances of specific lymphocytes in these tumors and their functional role in breast cancer remains largely unexplored. Here we attempt to quantify the level of immune activity in patient tumors by examining the expression of four specific lymphocytes: B cells, cytotoxic T, T helper 1, and T helper 2 cells. We utilize a machine learning approach, in silico nano-dissection, to expand small lists of immune specific markers into larger cell type specific signatures. We then use these signatures to profile breast tumors across different clinical groupings revealing consistent variations in lymphocyte expression across subtypes, morphologies, and treatments.

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Fwd: Keith Devlin, April 21, public lecture
by Jennifer Rexford 17 Apr '14

17 Apr '14

> Please join the  > Princeton University  > Council on Science and Technology, Mathematics Department, & McGraw Center for Teaching  > and Learning > > FOR A PUBLIC LECTURE WITH > > Keith Devlin,  > Stanley Kelley, Jr. Visiting Professor for Distinguished Teaching, Department of Mathematics > > DISCUSSING  > Leonardo and Steve:  > The Young Genius Who Beat Apple to Market by  > 800 Years > EVENT DETAILS  > Date: April 21st  > Time: 6:00pm  > Place: Computer Science 104 > >

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Colloquium Speaker Nick Feamster, Thurs, April 17, 4:30pm
by Nicole E. Wagenblast 17 Apr '14

17 Apr '14

Bots, Bubbles, and Bottlenecks: Safeguarding the User’s Internet Experience Nick Feamster, Georgia Tech Thursday, April 17, 4:30pm Computer Science 105 A user's experience on the Internet rests in the hands of a large and increasingly diverse set of stakeholders. Internet service providers and content providers point fingers at one other about performance problems. Miscreants launch attacks against both other users and the Internet infrastructure itself. Content providers continually engage in practices to "personalize" what we see and when we see it. Many governments aim to control its citizens' access to information, while activists design circumvention tools. Safeguarding the user's Internet experience requires both gathering empirical network measurements to detect threats (typically in the absence of any "ground truth") and developing large-scale systems to mitigate them. In this talk, I will present three classes of safeguards against different threats to the user's Internet experience: (1) technologies to characterize and improve performance in the Internet's "last mile", including a worldwide deployment of home routers in around 200 home networks and ongoing studies with the Federal Communications Commission; (2) methods for lightweight and fast detection of message abuse, such as spam, that have since been widely adopted by industry; and (3) defenses against against information manipulation attacks, a new class of attacks against personalization algorithms. I will also discuss other such threats and how networking can draw from other disciplines to tackle them. Nick Feamster is an associate professor in the College of Computing at Georgia Tech. He received his Ph.D. in Computer science from MIT in 2005, and his S.B. and M.Eng. degrees in Electrical Engineering and Computer Science from MIT in 2000 and 2001, respectively. His research focuses on many aspects of computer networking and networked systems, with a focus on network operations, network security, and censorship-resistant communication systems. In December 2008, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) for his contributions to cybersecurity, notably spam filtering. His honors include the Technology Review 35 "Top Young Innovators Under 35" award, the ACM SIGCOMM Rising Star Award, a Sloan Research Fellowship, the NSF CAREER award, the IBM Faculty Fellowship, the IRTF Applied Networking Research Prize, and award papers at the SIGCOMM Internet Measurement Conference (measuring Web performance bottlenecks), SIGCOMM (network-level behavior of spammers), the NSDI conference (fault detection in router configuration), Usenix Security (circumventing web censorship using Infranet), and Usenix Security (web cookie analysis).

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Steven Salzberg special seminar today at 4:00 pm
by Nicole E. Wagenblast 15 Apr '14

15 Apr '14

Steven Salzberg, Johns Hopkins University School of Medicine, Computational Challenges of High Throughput Genome Sequence Analysis Apr 15, 2014, 4:00 p.m. Lewis Thomas Lab 003 Next-generation sequencing technology allows us to peer inside the cell in exquisite detail, revealing new insights into biology, evolution, and disease that would have been impossible to discover just a few years ago. The enormous volumes of data produced by NGS experiments present many computational challenges that we are working to address. In this talk, I will discuss some of our algorithmic solutions to two key alignment problems: (1) mapping sequences onto the human genome at very high speed, and (2) mapping and assembling transcripts from RNA-seq experiments. I will also discuss some of the problems that can arise during analysis of exome data, in which the gene-containing portions of the genome are sequenced in an effort to identify mutations responsible for disease. My group has developed algorithms to solve each of these problems, including the widely-used Bowtie program for fast DNA sequence alignment, the TopHat and Cufflinks programs for assembly of genes from transcriptome sequencing (RNA-seq) experiments, and the new DIAMUND program for detecting de novo mutations. This talk describes joint work with current and former lab members including Ben Langmead, Cole Trapnell, Daehwan Kim, Mihaela Pertea, and Geo Pertea. http://ccb.jhu.edu/people/salzberg/Salzberg/Salzberg_Lab_Home.html

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Colloquium Speaker Dafna Shahaf Tues April 15, 4:30pm
by Nicole E. Wagenblast 15 Apr '14

15 Apr '14

The Aha! Moment: From Data to Insight Dafna Shahaf, Stanford University Tuesday, April 15, 4:30pm Computer Science 105 The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally: The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising. I will formulate both problems mathematically and provide efficient, scalable methods for solving them. User studies on real-world datasets demonstrate that our methods help users acquire insight efficiently across multiple domains. Dafna Shahaf is a postdoctoral fellow at Stanford University. She received her Ph.D. from Carnegie Mellon University; prior to that, she earned an M.S. from the University of Illinois at Urbana-Champaign and a B.Sc. from Tel-Aviv university. Dafna 's research focuses on helping people make sense of massive amounts of data. She has won a best research paper award at KDD 2010, a Microsoft Research Fellowship, a Siebel Scholarship, and a Magic Grant for innovative ideas.

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P Gopalan preFPO
by Melissa M. Lawson 11 Apr '14

11 Apr '14

Prem Gopalan will present his preFPO on Friday April 18 at 3PM in Room 402. The members of his committee are: David Blei, advisor; Rob Schapire and Jake Hofman (MSR), readers; Michael Freedman and John Storey (MOL), nonreaders. Everyone is invited to attend his talk. His abstract follows below. ---------------- Title: Scalable inference of discrete outcomes: networks, genotype and user consumption Latent variable models are probabilistic models that can be used to extract hidden structure in real data. They are important in many fields such as genetics, social network analysis and collaborative filtering. Data analysis using these models is useful in making predictions, exploring the data and in making better models. Will inference algorithms be able to cope with the scale of modern data sets? If yes, what properties of the model, data and the algorithms help in achieving scalability? In this talk, I will present advances in statistical models and scalable inference algorithms for identifying overlapping communities in networks, ancestral populations in human genetic variations, and latent structure in user consumption data. These algorithms lie in the framework of variational inference, an approach to approximate posterior inference that has been adapted to a variety of probabilistic models. As a detailed example, I will present hierarchical Poisson matrix factorization models for recommendation, and a corresponding variational inference algorithm. The algorithm scales to more than 100 million observations on a single CPU and predicts better than prior methods. A simple extension to this model allows for cold-start recommendations. I will end with a novel Bayesian nonparametric variant of Poisson matrix factorization that eases the burden of searching for the best number of latent components. This talk includes ongoing work.

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