- talks - lists.cs.princeton.edu

Colloquium speaker: Ben Raphael, Today, 4:30pm
by Nicole E. Wagenblast 12 Jan '15

12 Jan '15

Colloquium Speaker Ben Raphael, Brown University Monday, January 12, 4:30pm Computer Science 105 Understanding the molecular alterations that cause cancer is one of the greatest challenges in biology and medicine. Recent advances in DNA sequencing technology now enable large-scale measurement of mutations in cancer genomes and provide an unprecedented opportunity to address this challenge. However, translating this information into deeper insight about the mutational process that drives cancer demands novel computational approaches. In this talk, I will describe algorithms that we have developed to address key problems in cancer genomics. First, I will discuss algorithms that deconvolve DNA sequencing data from a single tumor and determine subpopulations of cancer cells harboring different mutations. These algorithms exploit integrality constraints on copy number aberrations and phylogenetic tree constraints that relate subpopulations. Next, I will describe algorithms to identify combinations of mutations that perturb cellular signaling and regulatory networks. One algorithm employs a heat diffusion process to identify subnetworks of a genome-scale interaction network that are recurrently mutated across samples. A second algorithm finds combinations of mutations that optimize a measure of mutual exclusivity across samples. I will illustrate applications of these approaches to multiple cancer types in The Cancer Genome Atlas (TCGA), including a recent Pan-Cancer study of >3000 samples from 12 cancer types. Ben Raphael is an Associate Professor in the Department of Computer Science and Director of the Center for Computational Molecular Biology (CCMB) at Brown University. His research focuses on the design of algorithms for genome sequencing and interpretation. Recent interests include structural variation in human and cancer genomes, and network/pathway analysis of genetic variants. He received an S.B. in Mathematics from MIT, a Ph.D. in Mathematics from the University of California, San Diego (UCSD), and completed postdoctoral training in Bioinformatics and Computer Science at UCSD. He is the recipient of an NSF CAREER award, a Career Award from the Burroughs Wellcome Fund, and a Sloan Research Fellowship.

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Colloquium speaker: Ben Raphael, Monday January 12, 4:30pm
by Nicole E. Wagenblast 11 Jan '15

11 Jan '15

Colloquium Speaker Ben Raphael, Brown University Monday, January 12, 4:30pm Computer Science 105 Understanding the molecular alterations that cause cancer is one of the greatest challenges in biology and medicine. Recent advances in DNA sequencing technology now enable large-scale measurement of mutations in cancer genomes and provide an unprecedented opportunity to address this challenge. However, translating this information into deeper insight about the mutational process that drives cancer demands novel computational approaches. In this talk, I will describe algorithms that we have developed to address key problems in cancer genomics. First, I will discuss algorithms that deconvolve DNA sequencing data from a single tumor and determine subpopulations of cancer cells harboring different mutations. These algorithms exploit integrality constraints on copy number aberrations and phylogenetic tree constraints that relate subpopulations. Next, I will describe algorithms to identify combinations of mutations that perturb cellular signaling and regulatory networks. One algorithm employs a heat diffusion process to identify subnetworks of a genome-scale interaction network that are recurrently mutated across samples. A second algorithm finds combinations of mutations that optimize a measure of mutual exclusivity across samples. I will illustrate applications of these approaches to multiple cancer types in The Cancer Genome Atlas (TCGA), including a recent Pan-Cancer study of >3000 samples from 12 cancer types. Ben Raphael is an Associate Professor in the Department of Computer Science and Director of the Center for Computational Molecular Biology (CCMB) at Brown University. His research focuses on the design of algorithms for genome sequencing and interpretation. Recent interests include structural variation in human and cancer genomes, and network/pathway analysis of genetic variants. He received an S.B. in Mathematics from MIT, a Ph.D. in Mathematics from the University of California, San Diego (UCSD), and completed postdoctoral training in Bioinformatics and Computer Science at UCSD. He is the recipient of an NSF CAREER award, a Career Award from the Burroughs Wellcome Fund, and a Sloan Research Fellowship.

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11am-noon Thu Jan 15 talk on "Data Centers, Energy, and Online Optimization"
by Jennifer Rexford 09 Jan '15

09 Jan '15

Speaker: Adam Wierman, CalTech Title: Data Centers, Energy, and Online Optimization Date/time: 11am-noon Thursday January 15 Location: CS 105 (small auditorium) Abstract: This talk will tell two stories, one about designing sustainable data centers and one about the underlying algorithmic challenges, which fall into the context of online convex optimization. Story 1: The typical message surrounding data centers and energy is an extremely negative one: Data centers are energy hogs. This message is pervasive in both the popular press and academia, and it certainly rings true. However, the view of data centers as energy hogs is too simplistic. One goal of this talk is to highlight that, yes, data centers use a lot of energy, but data centers can also be a huge benefit in terms of integrating renewable energy into the grid and thus play a crucial role in improving the sustainability of our energy landscape. In particular, I will highlight a powerful alternative view: data centers as demand response opportunities. Story 2: Typically in online convex optimization it is enough to exhibit an algorithm with low (sub-linear) regret, which implies that the algorithm can match the performance of the best static solution in retrospect. However, what if one additionally wants to maintain performance that is nearly as good as the dynamic optimal, i.e., a good competitive ratio? In this talk, I'll highlight that it is impossible for an online algorithm to simultaneously achieve these goals. Luckily though, in practical settings (like data centers), noisy predictions about the future are often available, and I will show that, under a general model of prediction noise, even very limited predictions about the future are enough to overcome the impossibility result. Bio: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at the California Institute of Technology, where he is a founding member of the Rigorous Systems Research Group (RSRG) and maintains a popular blog called Rigor + Relevance. His research interests center around resource allocation and scheduling decisions in computer systems and services. He received the 2011 ACM SIGMETRICS Rising Star award, the 2014 IEEE Communications Society William R. Bennett Prize, and has been coauthor on papers that received of best paper awards at ACM SIGMETRICS, IEEE INFOCOM, IFIP Performance (twice), IEEE Green Computing Conference, IEEE Power & Energy Society General Meeting, and ACM GREENMETRICS.

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Colloquium speaker: Ben Raphael, Monday January 12, 4:30pm
by Nicole E. Wagenblast 06 Jan '15

06 Jan '15

Colloquium Speaker Ben Raphael, Brown University Monday, January 12, 4:30pm Computer Science 105 Understanding the molecular alterations that cause cancer is one of the greatest challenges in biology and medicine. Recent advances in DNA sequencing technology now enable large-scale measurement of mutations in cancer genomes and provide an unprecedented opportunity to address this challenge. However, translating this information into deeper insight about the mutational process that drives cancer demands novel computational approaches. In this talk, I will describe algorithms that we have developed to address key problems in cancer genomics. First, I will discuss algorithms that deconvolve DNA sequencing data from a single tumor and determine subpopulations of cancer cells harboring different mutations. These algorithms exploit integrality constraints on copy number aberrations and phylogenetic tree constraints that relate subpopulations. Next, I will describe algorithms to identify combinations of mutations that perturb cellular signaling and regulatory networks. One algorithm employs a heat diffusion process to identify subnetworks of a genome-scale interaction network that are recurrently mutated across samples. A second algorithm finds combinations of mutations that optimize a measure of mutual exclusivity across samples. I will illustrate applications of these approaches to multiple cancer types in The Cancer Genome Atlas (TCGA), including a recent Pan-Cancer study of >3000 samples from 12 cancer types. Ben Raphael is an Associate Professor in the Department of Computer Science and Director of the Center for Computational Molecular Biology (CCMB) at Brown University. His research focuses on the design of algorithms for genome sequencing and interpretation. Recent interests include structural variation in human and cancer genomes, and network/pathway analysis of genetic variants. He received an S.B. in Mathematics from MIT, a Ph.D. in Mathematics from the University of California, San Diego (UCSD), and completed postdoctoral training in Bioinformatics and Computer Science at UCSD. He is the recipient of an NSF CAREER award, a Career Award from the Burroughs Wellcome Fund, and a Sloan Research Fellowship.

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Fwd: Colloquium speaker Silvio Savarese Today, 4:30pm
by Adam Finkelstein 09 Dec '14

09 Dec '14

Silvio Savarese talk starting momentarily in the small auditorium in the CS Building... ---------- Forwarded message ---------- From: Nicole E. Wagenblast <nwagenbl(a)cs.princeton.edu> Date: Tue, Dec 9, 2014 at 9:01 AM Subject: [talks] Colloquium speaker Silvio Savarese Today, 4:30pm To: "Talks (colloquium)" <talks(a)lists.cs.princeton.edu> Colloquium Speaker Silvio Savarese, Stanford University Tuesday, December 9, 4:30pm Computer Science 105 Sensing and sensibility We are moving into a world where sensors will be everywhere and will be ubiquitous. In the recent years, we have seen an explosion of new artificial visual sensors that can integrate luminance values with other sensing modalities: infrared, thermal, gravity, depth, to cite a few. Sensing is not the hard problem here, however. Sensibility, or, intelligent understanding of the sensing data is the challenge. When we look at an environment such as a coffee shop, we don't just recognize the objects in isolation, but rather perceive a rich scenery of the 3D space, its objects and all the relations among them. This allows us to effortlessly navigate through the environment, or to interact and manipulate objects in the scene with amazing precision. A major line of work from my group in recent years has been to design visual models that can process different sensing modalities and enable sensibility. I will start by giving an overview of our research for detecting objects and determining their geometric and physical properties such as 3D location, pose or shape from images, videos or RGB-Depth imagery. Then, I will demonstrate that these detection methods play a critical role for modeling the interplay between objects and space which, in turn, enable simultaneous inference of the semantic and spatial properties of a scene. I will conclude this talk by demonstrating that our models for sense and sensibility are potentially transformative in application areas related to autonomous or assisted navigation, smart environments, augmented reality, and large scale information management. Silvio Savarese is an Assistant Professor of Computer Science at Stanford University. He earned his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005 and was a Beckman Institute Fellow at the University of Illinois at Urbana-Champaign from 2005-2008. He joined Stanford in 2013 after being Assistant and then Associate Professor (with tenure) of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, from 2008 to 2013. His research interests include computer vision, object recognition and scene understanding, shape representation and reconstruction, human activity recognition and visual psychophysics. He is recipient of several awards including the James R. Croes Medal in 2013, a TRW Automotive Endowed Research Award in 2012, an NSF Career Award in 2011 and Google Research Award in 2010. In 2002 he was awarded the Walker von Brimer Award for outstanding research initiative.

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Prem Gopalan will present his FPO on December 15, 2014 at 2pm in CS 402
by Nicki Gotsis 09 Dec '14

09 Dec '14

Prem Gopalan will present his Final Public Oral Examination December 15, 2014 at 2pm in 402 Computer Science Building. The members of his committee are: Michael Joseph Freedman, non reader; John D. Storey, non reader (Molecular Biology, Princeton); and Jake Hofman, Reader (Microsoft Research). A copy of his thesis is available in Room 310. Everyone is invited to attend his talk. Nicki Gotsis Graduate Coordinator Room 310, Computer Science (609) 258-5387 ngotsis(a)cs.princeton.edu

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Colloquium speaker Silvio Savarese Tuesday Dec 9, 4:30pm
by Nicole E. Wagenblast 09 Dec '14

09 Dec '14

Colloquium Speaker Silvio Savarese, Stanford University Tuesday, December 9, 4:30pm Computer Science 105 Sensing and sensibility We are moving into a world where sensors will be everywhere and will be ubiquitous. In the recent years, we have seen an explosion of new artificial visual sensors that can integrate luminance values with other sensing modalities: infrared, thermal, gravity, depth, to cite a few. Sensing is not the hard problem here, however. Sensibility, or, intelligent understanding of the sensing data is the challenge. When we look at an environment such as a coffee shop, we don't just recognize the objects in isolation, but rather perceive a rich scenery of the 3D space, its objects and all the relations among them. This allows us to effortlessly navigate through the environment, or to interact and manipulate objects in the scene with amazing precision. A major line of work from my group in recent years has been to design visual models that can process different sensing modalities and enable sensibility. I will start by giving an overview of our research for detecting objects and determining their geometric and physical properties such as 3D location, pose or shape from images, videos or RGB-Depth imagery. Then, I will demonstrate that these detection methods play a critical role for modeling the interplay between objects and space which, in turn, enable simultaneous inference of the semantic and spatial properties of a scene. I will conclude this talk by demonstrating that our models for sense and sensibility are potentially transformative in application areas related to autonomous or assisted navigation, smart environments, augmented reality, and large scale information management. Silvio Savarese is an Assistant Professor of Computer Science at Stanford University. He earned his Ph.D. in Electrical Engineering from the California Institute of Technology in 2005 and was a Beckman Institute Fellow at the University of Illinois at Urbana-Champaign from 2005–2008. He joined Stanford in 2013 after being Assistant and then Associate Professor (with tenure) of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, from 2008 to 2013. His research interests include computer vision, object recognition and scene understanding, shape representation and reconstruction, human activity recognition and visual psychophysics. He is recipient of several awards including the James R. Croes Medal in 2013, a TRW Automotive Endowed Research Award in 2012, an NSF Career Award in 2011 and Google Research Award in 2010. In 2002 he was awarded the Walker von Brimer Award for outstanding research initiative.

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Dan Larkin will present his Pre FPO on Tuesday, December 9 at 11am in CS 302.
by Nicki Gotsis 08 Dec '14

08 Dec '14

Dan Larkin will present his Pre FPO on Tuesday, December 9, 2014 at 11am in CS 302. The members of his committee are: Robert Tarjan (advisor), Jeff Erickson and Robert Sedgewick (readers), and Mark Braverman and Sanjeev Arora. Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Compressing Trees with a Sledgehammer Abstract: A number of fundamental algorithms make use of the compressed tree framework to accelerate computation on graphs and sets. Perhaps the most well-known of these problems is disjoint set union (union-find). The main contributions of this disseration are a simple, optimal, randomized solution to the disjoint set union problem; an optimal, space-saving solution to the nested variation of the same problem; and a clean, unified analysis which encapsulates virtually all known optimal compressed tree algorithms and provides significant flexibility to handle a wide class of possible algorithms. Nicki Gotsis Graduate Coordinator Room 310, Computer Science (609) 258-5387 ngotsis(a)cs.princeton.edu

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Colloquium Speaker Thomas Wenisch, Today, Dec 8, 4:30pm
by Nicole E. Wagenblast 08 Dec '14

08 Dec '14

Colloquium Speaker Thomas Wenisch, University of Michigan Monday, December 8, 4:30pm Computer Science 105 Computational Sprinting Although transistor density continues to increase, power density is increasing each chip generation. Particularly in mobile devices, which have limited cooling options, these trends lead to "dark silicon" in which sustained chip performance is limited primarily by power rather than area. However, many mobile applications do not demand sustained performance; rather they comprise short bursts of computation in response to sporadic user activity. To improve responsiveness for such applications, this talk describes "computational sprinting ", in which the system activates otherwise powered-down cores for brief bursts of intense parallel computation. During a sprint the chip temporarily exceeds its sustainable thermal power budget to provide instantaneous throughput, after which the chip must return to nominal operation to cool down. To enable longer sprints , we describe a thermal design that incorporates phase-change materials to provide thermal capacitance. Results indicate that sprinting can both provide large improvements in responsiveness while actually saving energy due to race-to-idle effects. Thomas Wenisch is an Associate Professor of Computer Science and Engineering at the University of Michigan, specializing in computer architecture. His prior research includes memory streaming for commercial server applications, multiprocessor memory systems, memory disaggregation, and rigorous sampling-based performance evaluation methodologies. His ongoing work focuses on computational sprinting , server and data center architectures, programming models for byte-addressable NVRAM, and architectures to enable hand-held 3D ultrasound . Wenisch received the NSF CAREER award in 2009. Prior to his academic career, Wenisch was a software developer at American Power Conversion, where he worked on data center thermal topology estimation. He received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University.

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Colloquium Speaker Thomas Wenisch, Monday, Dec 8, 4:30pm
by Nicole E. Wagenblast 07 Dec '14

07 Dec '14

Colloquium Speaker Thomas Wenisch, University of Michigan Monday, December 8, 4:30pm Computer Science 105 Computational Sprinting Although transistor density continues to increase, power density is increasing each chip generation. Particularly in mobile devices, which have limited cooling options, these trends lead to "dark silicon" in which sustained chip performance is limited primarily by power rather than area. However, many mobile applications do not demand sustained performance; rather they comprise short bursts of computation in response to sporadic user activity. To improve responsiveness for such applications, this talk describes "computational sprinting ", in which the system activates otherwise powered-down cores for brief bursts of intense parallel computation. During a sprint the chip temporarily exceeds its sustainable thermal power budget to provide instantaneous throughput, after which the chip must return to nominal operation to cool down. To enable longer sprints , we describe a thermal design that incorporates phase-change materials to provide thermal capacitance. Results indicate that sprinting can both provide large improvements in responsiveness while actually saving energy due to race-to-idle effects. Thomas Wenisch is an Associate Professor of Computer Science and Engineering at the University of Michigan, specializing in computer architecture. His prior research includes memory streaming for commercial server applications, multiprocessor memory systems, memory disaggregation, and rigorous sampling-based performance evaluation methodologies. His ongoing work focuses on computational sprinting , server and data center architectures, programming models for byte-addressable NVRAM, and architectures to enable hand-held 3D ultrasound . Wenisch received the NSF CAREER award in 2009. Prior to his academic career, Wenisch was a software developer at American Power Conversion, where he worked on data center thermal topology estimation. He received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University.

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