- talks - lists.cs.princeton.edu

4:30pm talk today on evolution of the Internet (doors open 4pm, reception afterwards)
by Jennifer Rexford 12 Mar '14

12 Mar '14

Begin forwarded message: > Resent-From: <jrex(a)Princeton.EDU> > From: "Heather L. Evans" <heathere(a)Princeton.EDU> > Subject: Seminar Announcement - Fathers of the Internet Reflecting on the Internet Evolution- Today at 4:30 pm > Date: March 12, 2014 9:03:57 AM EDT > To: "ee-seminar(a)princeton.edu" <ee-seminar(a)Princeton.EDU> > > > > Speakers: > Vinton Cerf, Chief Internet Evangelist, Google & Robert Kahn, CEO of Corporation for National Research Initiatives > Title: > Fathers of the Internet Reflecting on the Evolution of the Internet > Day: > Wednesday, March 12, 2014 > > > Time: > 4:30 pm > Room: > Friend Center Auditorium 101 > Host: > Mung Chiang > > > > > > > > > > Vinton Cerf (Chief Internet Evangelist at Google) and Robert Kahn (CEO of Corporation for National Research Initiatives), whose pioneering work on the Internet has been widely recognized by numerous awards, including the US Presidential Medal of Freedom, the US National Medal of Technology, and the ACM Turing Award, will together give a public duet lecture on “Perspectives on the Internet and its Evolution > > This is an open lecture to the general public. No fee or ticket required. Seating available on a first-come-first serve basis. Seating will begin at 4pm. Reception will follow. > > Additional information available at http://edgelab-events.princeton.edu/. > > > Heather Evans > Assistant to the Chair > Department of Electrical Engineering > Princeton University > Engineering Quadrangle, B-210 > Olden Street > Princeton, NJ 08544 > (p): 609-258-7282 > (f): 609-258-0119 > heathere(a)princeton.edu > >

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Colloquium speaker: Richard Socher Tues, March 11, 4:30pm
by Nicole E. Wagenblast 11 Mar '14

11 Mar '14

Recursive Deep Learning for Modeling Compositional Meaning in Language Richard Socher ( Stanford University ) Tuesday, March 11, 4:30pm Computer science 105 Great progress has been made in natural language processing thanks to many different algorithms, each often specific to one application. Most learning algorithms force language into simplified representations such as bag-of-words or fixed-sized windows or require human-designed features. I will introduce three models based on recursive neural networks that can learn linguistically plausible representations of language. These methods jointly learn compositional features and grammatical sentence structure for parsing or phrase level sentiment predictions. They can also be used to represent the visual meaning of a sentence which can be used to find images based on query sentences or to describe images with a more complex description than single object names. Besides the state-of-the-art performance, the models capture interesting phenomena in language such as compositionality. For instance, people easily see that the "with" phrase in "eating spaghetti with a spoon" specifies a way of eating whereas in "eating spaghetti with some pesto" it specifies the dish. I show that my model solves these prepositional attachment problems well thanks to its distributed representations. In sentiment analysis, a new tensor-based recursive model learns different types of high level negation and how they can change the meaning of longer phrases with many positive words. They also learn that when contrastive conjunctions such as "but" are used the sentiment of the phrases following them usually dominates. Richard Socher is a PhD student at Stanford working with Chris Manning and Andrew Ng. His research interests are machine learning for NLP and vision. He is interested in developing new deep learning models that learn useful features, capture compositional structure in multiple modalities and perform well across different tasks. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 "Magic Grant" from the Brown Institute for Media Innovation.

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Colloquium Speaker Mohammad Alizadeh Monday March 10, 4:30pm
by Nicole E. Wagenblast 10 Mar '14

10 Mar '14

Mohammad Alizadeh ( Stanford University ) Monday, March 10, 4:30pm Computer Science 105 Packet Transport Mechanisms for Data Center Networks We are witnessing a fundamental shift in the computing landscape in recent years as much of the world's application workloads move to massive "cloud" data centers. These data centers enable the services we rely on daily, such as web search, social networking, and Internet commerce. They also require huge investments to deploy and operate. This has spurred significant interest in the industry and the research community in innovation for data centers, and in particular, for data center networks. A crucial feature of a data center network is its transport mechanism: the method by which data is transferred from one server to another. Ideally, this should occur at the highest possible rate and with the lowest possible latency. But there is an inherent tension between these requirements, especially with today 's state-of-the-art Transmission Control Protocol (TCP) in data centers. In this talk, I will present three data center transport designs with increasing levels of sophistication. First, I will describe Data Center TCP (DCTCP), a new congestion control algorithm for data centers that is now shipping with Windows Server 2012. DCTCP uses a simple modification to the TCP algorithm to achieve full throughput while reducing queueing latency in the switches by ~10x compared to TCP. I will present a control-theoretic analysis of DCTCP's control loop. I will then discuss HULL, an architecture that builds on DCTCP to deliver near-zero fabric latency: only propagation and switching latency, no queueing. Finally, I will describe pFabric, a minimalistic design that leverages application-level hints and very simple switch mechanisms to achieve near theoretically optimal scheduling of flows in the data center fabric. Mohammad Alizadeh is a Researcher at Insieme Networks (recently acquired by Cisco Systems). He received his Ph.D. in Electrical Engineering from Stanford University where he was advised by Balaji Prabhakar. Before that, he completed his undergraduate degree in Electrical Engineering at Sharif University of Technology. His research interests are broadly in networked systems, data center networking, and cloud computing. His dissertation work focused on designing high performance data center transport mechanisms. His research has garnered significant industry interest: the DCTCP algorithm has been implemented in Windows Server 2012; the QCN congestion control algorithm has been standardized as the IEEE 802.1Qau standard; and most recently, the CONGA adaptive load balancing mechanism has been implemented in Cisco's new flagship Application Centric Infrastructure products. Mohammad is a recipient of a Stanford Electrical Engineering Departmental Fellowship, the Caroline and Fabian Pease Stanford Graduate Fellowship, and the Numerical Technologies Inc. Prize and Fellowship.

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C Park FPO
by Melissa M. Lawson 04 Mar '14

04 Mar '14

Christopher Park will present his FPO on Monday March 10 at 1PM in Room 302 (note room!) . The members of his committee are: Olga Troyanskaya, advisor; Kai Li and Mona Singh, readers; Moses Charikar and Rob Schapire, nonreaders. A copy of his thesis, "Computational Functional Genomics for Directing Biological Discoveries", is on display in Room 310. Everyone is invited to attend his talk.

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Colloquium Speaker Barbara Engelhardt Monday, March 3, 4:30pm
by Nicole E. Wagenblast 03 Mar '14

03 Mar '14

Statistical models to study how a genetic variant impacts an organism Barbara Engelhardt ( Duke University ) Monday, March 3, 4:30pm Computer Science 105 Consider sequencing the genome of a newborn, and selecting targeted therapeutics early in life to reduce her lifetime risk of addiction, obesity, type II diabetes, or pancreatic cancer. While genome-wide association studies (GWAS) have unquestionably been successful in identifying reproducible genomic risk factors for complex human diseases, the promise of developing therapeutics to reduce the heritable portion of disease risk is far from fulfillment. The essential technological developments to fulfill this promise, however, are mainly in statistics and computation rather than in genomic experimental methods. I describe three genomic studies from my recent work. First, I identified a genetic variant that behaves differently depending on whether or not an individual takes cholesterol-reducing drugs. Second, I found that genetic variants that are associated with different cell traits are co-localized with a large variety of different regulatory mechanisms more often than expected. Third, I developed a model to uncover genetic variants that affect many traits simultaneously, where the trait measurements have substantial technical noise. Throughout, I emphasize statistical and computational challenges, and innovations necessary to fulfill this promise of genomic studies.

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Colloquium Speaker Anima Anandkumar Thurs Feb 27, 4:30pm
by Nicole E. Wagenblast 27 Feb '14

27 Feb '14

Guaranteed Learning of Latent Variable Models: Overlapping Community Models and Overcomplete Representations Anima Anandkumar , University of California Irvine Thursday, February 27, 4:30pm Computer Science 105 Incorporating latent or hidden variables is a crucial aspect of statistical modeling. I will present a statistical and a computational framework for guaranteed learning of a wide range of latent variable models. I will focus on two instances, viz., community detection and overcomplete representations. The goal of community detection is to discover hidden communities from graph data. I will present a tensor decomposition approach for learning probabilistic mixed membership models. The tensor approach is guaranteed to correctly recover the mixed membership communities with tight guarantees. We have deployed it on many real-world networks, e.g. Facebook, Yelp and DBLP. It is easily parallelizable, and is orders of magnitude faster than the state-of-art stochastic variational approach. I will then discuss recent results on learning overcomplete latent representations, where the latent dimensionality can far exceed the observed dimensionality. I will present two frameworks, viz., sparse coding and sparse topic modeling. Identifiability and efficient learning are established under some natural conditions such as incoherent dictionaries or persistent topics. Anima Anandkumar is a faculty at the EECS Dept. at U.C.Irvine since August 2010. Her research interests are in the area of large-scale machine learning and high-dimensional statistics. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She has been a visiting faculty at Microsoft Research New England in 2012 and a postdoctoral researcher at the Stochastic Systems Group at MIT between 2009-2010. She is the recipient of the Microsoft Faculty Fellowship, ARO Young Investigator Award, NSF CAREER Award, IBM Fran Allen PhD fellowship, thesis award from ACM SIGMETRICS society, paper awards from the ACM SIGMETRICS and IEEE Signal Processing societies, and 2014 Sloan Fellowship.

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Colloquium Speaker Xi Wang, Wed Feb 26, 4:30pm
by Nicole E. Wagenblast 26 Feb '14

26 Feb '14

A new class of bugs: How compiler optimizations harm our systems through undefined behavior Xi Wang , Massachusetts Institute of Technology Wednesday, February 26, 4:30pm Computer Science 105 Software bugs introduce security vulnerabilities into our computer systems. To understand and mitigate an increasing number of bugs, practitioners categorize them into classes, such as buffer overflow or SQL injection, and handle each class separately. This talk introduces a new class of bugs called unstable code: code that is unexpectedly discarded by compiler optimizations due to undefined behavior in the program. I will discuss its prevalence and security impact in systems, and present a systematic approach for reasoning about unstable code, as well as a static checker called Stack that implements this approach to precisely identify unstable code in real systems. Applying Stack to widely used software has uncovered 160 new bugs that have been confirmed and fixed by developers. It has also been adopted by several companies to scan their codebases. Xi Wang is a PhD candidate in Computer Science at MIT, advised by M. Frans Kaashoek and Nickolai Zeldovich. His research interests are in building secure and reliable systems. He was awarded a Best Paper Award at SOSP 2013, a Best Student Paper Award at EuroSys 2008, and an MIT Jacobs Presidential Fellowship in 2008.

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Colloquium Speaker Rishabh Singh Tues Feb 25, 4:30pm
by Nicole E. Wagenblast 25 Feb '14

25 Feb '14

Program Synthesis for the Masses Rishabh Singh ( Massachusetts Institute of Technology ) Tuesday, February 25, 4:30pm Computer Science 105 New computing platforms have greatly increased the demand for programmers, but learning to program remains a big challenge. Program synthesis has the potential to revolutionize programming by making it more accessible. My work has focused on two goals: making programming more intuitive through the use of new interfaces, and using automated feedback to help students learn programming. In this talk, I will present my work on three systems that work towards these goals. The FlashFill system helps end-users perform repetitive data transformations over strings, numbers, and tables using input-output examples. FlashFill was shipped as part of Excel 2013 and was quoted as one of the top features by many press reviews. The Storyboard Programming system helps students write data-structure manipulations using textbook-like visual examples and bridges the gap between high-level insights and low-level code. Finally, the Autograder system provides automated feedback to students on introductory programming assignments, and was successfully run on tens of thousands of programming exercises from edX. I will describe how ideas from advances in constraint-solving, machine learning, and formal verification enabled the new forms of interaction required by these systems. Rishabh Singh is a PhD candidate in the Computer Science and Artificial Intelligence Laboratory at MIT. His research interests are broadly in formal methods and programming languages. His PhD work focuses on developing program synthesis techniques for making programming accessible to end-users and students. He is a Microsoft Research PhD fellow and winner of MIT's William A. Martin Outstanding Master's thesis Award. He obtained his BTech in Computer Science and Engineering from IIT Kharagpur in 2008, where he was awarded the Institute Silver Medal and Bigyan Sinha Memorial Award. He was also awarded to be Prime Minister's National Guest at Republic Day Parade, New Delhi in 2005.

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Princeton Public Lectures: Google's Eric Schmidt on campus 2/26/2014
by Nicole E. Wagenblast 21 Feb '14

21 Feb '14

Flyer attached.

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4:30pm Wed Mar 12 talk by Cerf and Kahn on the evolution of the Internet
by Jennifer Rexford 20 Feb '14

20 Feb '14

Speakers: Vinton Cerf, Chief Internet Evangelist, Google Robert Kahn, CEO of Corporation for National Research Initiatives Title: Fathers of the Internet Reflecting on the Evolution of the Internet Day: 4:30pm Wed March 12 Room: Friend Center Auditorium 101 Host: Mung Chiang Vinton Cerf (Chief Internet Evangelist at Google) and Robert Kahn (CEO of Corporation for National Research Initiatives), whose pioneering work on the Internet has been widely recognized by numerous awards, including the US Presidential Medal of Freedom, the US National Medal of Technology, and the ACM Turing Award, will together give a public duet lecture on “Perspectives on the Internet and its Evolution." This is an open lecture to the general public. No fee or ticket required. Seating available on a first-come-first serve basis. Seating will begin at 4pm. Reception will follow. Additional information available at http://edgelab-events.princeton.edu/

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