- talks - lists.cs.princeton.edu

Colloquium Speaker Gabe Cohn Tuesday March 25, 4:30pm
by Nicole E. Wagenblast 25 Mar '14

25 Mar '14

Building Embedded Sensor Systems to Bring Ubicomp to Life Gabe Cohn ( University of Washington ) Tuesday, March 25, 4:30pm Computer Science 105 Although we have successfully created smaller, faster, and cheaper computer devices, several adoption barriers remain to realize the dream of Ubiquitous Computing (Ubicomp). By lowering these barriers, we can seamlessly embed human-computer interfaces into our home and work environments. My work focuses on building integrated hardware/software sensing systems for Ubicomp applications using my expertise in embedded systems, low-energy hardware design, and sensing, in addition to integrating communications, signal processing, and machine learning. In this talk, I will use my research to present three main techniques to lower the installation, maintenance, and scalability adoption barriers and bring Ubicomp to life. First, I will discuss my work on using the existing infrastructure in buildings to reduce the number of sensors required to enable many Ubicomp applications. Second, I will discuss how the conductive properties of the human body can be leveraged to enable novel human-computer interactions and health sensing opportunities. Finally, I will describe techniques for dramatically reducing the power consumption of embedded sensor systems for Ubicomp applications. By continually working on application-driven interdisciplinary research, we can lower the adoption barriers and enable many new high-impact application domains. Gabe Cohn is a Ph.D. candidate in Electrical Engineering in the Ubiquitous Computing (Ubicomp) Lab at the University of Washington, advised by Shwetak Patel. His research focuses on (1) designing and implementing ultra-low-power embedded sensing systems , (2) leveraging physical phenomena to enable new sensing modalities for human-computer interaction , and (3) developing sensor systems targeted at realizing immediate change in high-impact application domains . He was awarded the Microsoft Research Ph.D. Fellowship in 2012, the National Science Foundation Graduate Research Fellowship in 2010, and 6 Best Paper awards and nominations. He is the co-founder of SNUPI Technologies ( www.wallyhome.com ), a sensor and services company focused on home safety, security, and loss prevention. He received his B.S. with honors in Electrical Engineering from the California Institute of Technology in 2009, where he specialized in embedded systems, computer architectures, and digital VLSI.

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Colloquium Speaker Michael Carbin Monday, March 24, 4:30pm
by Nicole E. Wagenblast 24 Mar '14

24 Mar '14

Verified Approximate Computing Michael Carbin , Massachusetts Institute of Technology Monday, March 24, 4:30pm Computer Science 105 Many modern applications implement large-scale computations (e.g., machine learning, big data analytics, and financial analysis) in which there is a natural trade-off between the quality of the results that the computation produces and the performance and cost of executing the computation. Exploiting this fact, researchers have recently developed a variety of new mechanisms that automatically manipulate an application to enable it to execute at a variety of points in its underlying trade-off space. The resulting approximate application can navigate this trade-off space to meet its performance and cost requirements. I present a program verification and analysis system, Rely, for answering fundamental questions that arise when manipulating an application that implements an approximate computation. Examples of the questions that Rely is designed to answer include: What is the probability that the approximate application produces the same result as the original application? How much do the approximate application’s results differ from those of the original application? And is the approximate application safe and secure? Rely answers these questions with a novel analysis and verification method for reasoning about the safety and accuracy of approximate applications. Rely also provides a novel language and program analysis for verifying quantitative reliability: the probability that the new approximate application produces the same result as the original computation. Michael Carbin is a Ph.D. Candidate in Electrical Engineering and Computer Science at MIT. Michael started his research career as undergraduate student, working on BDD-based program analysis at Stanford University and on type-safe compile-time metaprogramming at Microsoft Research. His work at Stanford received an award for Best Computer Science Undergraduate Honors Thesis. As a graduate student, Michael has worked as a MIT Lemelson Presidential Fellow and a Microsoft Research Graduate Fellow on both the theory and practice of verified approximate computing and software self-healing. His recent research on verifying the reliability of programs that execute on unreliable hardware won a best paper award at OOPSLA 2013: Object-Oriented Programming, Systems, Languages & Analysis.

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11am Wed Mar 19 talk on optimal network traffic engineering
by Jennifer Rexford 17 Mar '14

17 Mar '14

Speaker: Kevin Tang, Cornell Title: Traffic Engineering: From Distributed Static Optimization to Centralized Transient Control Date/time: 11am-12pm on Wed March 19 Location: CS 402 Abstract: Traffic Engineering is of fundamental importance for network operation. Traditionally, it assumes static traffic input and then looks for distributed routing solutions to optimize certain objective. Along this line, in the first part of this talk, we will present HALO (Hop-by-hop Adaptive Link-state Optimal) routing. It is the first provable optimal link-state routing solution with hop-by-hop forwarding. Furthermore, our solution does not require traffic matrix as an explicit input and can adapt to changing traffic input. We prove our solution converges and solves the standard MCF (Multi-Commodity Flow) problem. Recently, there has been tremendous interest in SDN (Software-Defined Networking), which is an emerging network architecture that separates control and data planes. Conceivably, that means network managers can first solve Traffic Engineering optimization in a centralized manner and then translate the solution to configure network switches. Within this context, in the second part of this talk, we consider the problem of avoiding transient congestion while reconfiguring the routing paths. We formulate a flow-based and a switch-based model. For the flow-based model, we demonstrate that an optimal sequence of minimum update steps could always be found when the dependency graph is acyclic. For the switched model, we find a way to translate a feasible flow-based updating sequence to a feasible switch-based sequence subject to a tree topology constraint on the routing paths.

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Colloquium Speaker Ameet Talwalkar Thurs March 13, 4:30pm
by Nicole E. Wagenblast 13 Mar '14

13 Mar '14

Machine Learning in the Wild Ameet Talwalkar ( University of California, Berkeley ) Thursday, March 13, 4:30pm Computer Science 105 Modern datasets are rapidly growing in size and complexity, and this wealth of data holds the promise for many transformational applications. Machine learning is seemingly poised to deliver on this promise, having proposed and rigorously evaluated a wide range of data processing techniques over the past several decades. However, concerns over scalability and usability present major roadblocks to the wider adoption of these methods, and in this talk I will present work that addresses these concerns. In terms of scalability, my work relies on a careful application of divide-and-conquer methodology. In terms of usability, I focus on developing tools to diagnose the applicability of learning techniques and to autotune components of typical machine learning pipelines. I will discuss applications in the context of matrix factorization, estimator quality assessment and genomic variant calling. Ameet Talwalkar is a postdoctoral fellow in the Computer Science Division at UC Berkeley. He obtained a Ph.D. in Computer Science from the Courant Institute at New York University, and prior to that graduated summa cum laude from Yale University. His work addresses scalability and ease-of-use issues in the field of machine learning, as well as applications related to large-scale genomic sequencing analysis. He has won the Janet Fabri Prize for best doctoral dissertation and the Henning Biermann Award for exceptional service at NYU, received Yale's undergraduate prize in Computer Science, and is an NSF OCI postdoctoral scholar.

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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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