- talks - lists.cs.princeton.edu

1-3pm Thu Jan 15 poster session for COS 561 (grad networks) in the Friend Convocation Room
by Jennifer Rexford 14 Jan '15

14 Jan '15

The students in COS 561 (graduate computer networks) are having a poster session to present their final course projects. The poster session will be 1-3pm Thursday January 15 in the Friend Convocation Room. The 25 projects will be divided into two one-hour periods, each including about half of the posters. We'll have snacks and beverages. Everyone is welcome! The project titles and authors are listed below. -- Jen FIRST HOUR (1-2pm) Managing Fairness in Equilibrium Strategies for Routing Among Selfish Nodes in an Ad Hoc Network Andrew Grasso Global Internet Reachability: A Longitudinal Study Anne Edmundson, Elba Garza, Caroline Trippel Improving the Performance of Path Query in Software Defined Networks Mina Tahmasbi Arashloo Proxy Rotation with Bitcoin Micropayments Miles Carlsten, Harry Kalodner, Paul Ellenbogen TIP: An IP Protocol for Detecting BGP Hijacking Attacks Collin Stedman Ravana: Transparent Controller Fault Tolerance in Software-Defined Networks Haoyu Zhang Latency Equalized Routing for Interactive Networks Heemin Seog and Andrew Tran Imagining a Clock-Synchronized Internet Gregory Owen and Jonathan Frankle Statistics Query Support for CoVisor Jennifer Gossels Detecting BGP Man-in-the-Middle Attacks via Control Plane Anomaly Detection Cornellius K. Metto, Laura M. Roberts, Elena Sizikova Fast BGP Convergence with the Supercharged Router: Electrifying Traditional Routers with SDN Michael Alan Chang Compressing Cached Rules in Software-Defined Networks Yatin A. Manerkar, Adi Fuchs, Alexey Lavrov SECOND HOUR (2-3pm) Upgrading HTTPS in Mid-Air: An Empirical Study of Strict Transport Security and Key Pinning Michael Kranch Prediction Based Mechanism for efficient scheduling of Cellular Data usage Themistoklis Melissaris, Nayana Prasad Nagendra Towards Temporal Network Synthesis Ryan Beckett, Qinxiang Cao, Olivier Savary Bélanger Multi-Network Cellular Access Parishad Karimi SDN-Enabled IPv4 Multicast Protocol Design Robert Macdavid, Sergiy Popovych, Hansen Zhang A Visualization Environment for Network Information in Pyretic Nora Coler, Ruth Dannenfelser, and Nevin Li Inter-domain path diversity through virtual peering Shreyasee Mukherjee MITM Detection with P2P Networks Steven Englehardt, Steven Goldfeder, Maciej Halber, Peter Zimmerman Evaluating Path Queries in Software-Defined Networks with Different Forwarding Policies Violeta Ilieva Dynamic Service Chaining Tengyu Ma, Amy Tai, Kelvin Zou Customizable Adaptive Streaming over Cellular Network Yichen Chen, Yixin Sun Exploring Scalability in Hierarchical SDN Controllers Disney Y. Lam Towards remote music collaboration: forecasting mallet motion via networks Huiwen Chang, Zeyu Jin and Shuran Song

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Elba Garza will present her Generals on Monday, January 19, 2015 at 8:30 am in Rm 401
by Nicki Gotsis 12 Jan '15

12 Jan '15

Elba Garza will present her Generals on Monday, January 19, 2015 at 8:30 am in Rm 401. The members of her committee are David August, Margaret Martonosi, Kelly Shaw (University of Richmond). Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His/her abstract and reading list follow below. Abstract The emerging popularity of heterogeneous architectures results in a necessary and complex selection of runtime hardware/software parameters. As these heterogeneous systems have grown to offload computation to graphics processing units (GPUs), various Application-specific integrated circuits (ASICs) and other compute units, there is a higher need to optimize these units’ software and hardware settings to reach better power, performance, and area goals. Exhaustive exploration techniques are impractical since the design spaces grow exponentially with the addition of new parameters. Design spaces can easily reach thousands or millions of configurations to simulate or test. Manual and inference-based information can be flawed due to individual knowledge and bias, which may lead to unsearched design areas. Using statistical learning techniques, we have been able to optimize design space exploration for parameters of heterogeneous architecture components such as GPUs and Accelerator ASICs. These techniques allow for efficient and tractable design space assessment and can output optimal or near-optimal parameter settings for given applications automatically. Reading List [Cas+10] C. Cascaval et al. “A taxonomy of accelerator architectures and their programming models”. In: IBM Journal of Research and Development 54.5 (Sept. 2010), 5:1–5:10. issn: 0018-8646. doi: 10. 1147/JRD.2010.2059721. [Con+14] Jason Cong et al. “Accelerator-Rich Architectures: Opportunities and Progresses”. In: Proceedings of the 51st Annual Design Automation Conference. DAC ’14. San Francisco, CA, USA: ACM, 2014, 180:1–180:6. isbn: 978-1-4503-2730-5. doi: 10 . 1145 / 2593069 . 2596667. url: http://doi.acm.org/10.1145/2593069.2596667. [Esm+11] Hadi Esmaeilzadeh et al. “Dark Silicon and the End of Multicore Scaling”. In: Proceedings of the 38th Annual International Symposium on Computer Architecture. ISCA ’11. San Jose, California, USA: ACM, 2011, pp. 365–376. isbn: 978-1-4503-0472-6. doi: 10. 1145/2000064.2000108. url: http://doi.acm.org/10.1145/ 2000064.2000108. [Lyo+12] Michael J. Lyons et al. “The Accelerator Store: A Shared Memory Framework for Accelerator-based Systems”. In: ACM Trans. Archit. Code Optim. 8.4 (Jan. 2012), 48:1–48:22. issn: 1544-3566. doi: 10. 1145/2086696.2086727. url: http://doi.acm.org/10.1145/ 2086696.2086727. [Sha+14] Yakun Sophia Shao et al. “Aladdin: A Pre-RTL, Power-performance Accelerator Simulator Enabling Large Design Space Exploration of Customized Architectures”. In: Proceeding of the 41st Annual International Symposium on Computer Architecuture. ISCA ’14. Minneapolis, Minnesota, USA: IEEE Press, 2014, pp. 97–108. isbn: 1lba Garza January 19, 2015 978-1-4799-4394-4. url: http://dl.acm.org/citation.cfm?id= 2665671.2665689. [Ham+10] Rehan Hameed et al. “Understanding Sources of Inefficiency in General-purpose Chips”. In: Proceedings of the 37th Annual International Symposium on Computer Architecture. ISCA ’10. SaintMalo, France: ACM, 2010, pp. 37–47. isbn: 978-1-4503-0053-7. doi: 10.1145/1815961.1815968. url: http://doi.acm.org/10.1145/ 1815961.1815968. [JSM12] Wenhao Jia, Kelly A. Shaw, and Margaret Martonosi. “Stargazer: Automated Regression-based GPU Design Space Exploration”. In: Proceedings of the 2012 IEEE International Symposium on Performance Analysis of Systems & Software. ISPASS ’12. Washington, DC, USA: IEEE Computer Society, 2012, pp. 2–13. isbn: 978-1- 4673-1143-4. doi: 10 . 1109 / ISPASS . 2012 . 6189201. url: http : //dx.doi.org/10.1109/ISPASS.2012.6189201. [JSM13] Wenhao Jia, Kelly A. Shaw, and Margaret Martonosi. “Starchart: Hardware and Software Optimization Using Recursive Partitioning Regression Trees”. In: Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques. PACT ’13. Edinburgh, Scotland, UK: IEEE Press, 2013, pp. 257– 268. isbn: 978-1-4799-1021-2. url: http://dl.acm.org/citation. cfm?id=2523721.2523757. [JVT06a] P. J. Joseph, K. Vaswani, and Matthew J. Thazhuthaveetil. “Construction and use of linear regression models for processor performance analysis”. In: High-Performance Computer Architecture, 2006. The Twelfth International Symposium on. Feb. 2006, pp. 99– 108. doi: 10.1109/HPCA.2006.1598116. [JVT06b] P. J. Joseph, Kapil Vaswani, and Matthew J. Thazhuthaveetil. “A Predictive Performance Model for Superscalar Processors”. In: Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture. MICRO 39. Washington, DC, USA: IEEE Computer Society, 2006, pp. 161–170. isbn: 0-7695-2732-9. doi: 10.1109/MICRO.2006.6. url: http://dx.doi.org/10.1109/ MICRO.2006.6. [LB06] Benjamin C. Lee and David M. Brooks. “Accurate and Efficient Regression Modeling for Microarchitectural Performance and Power Prediction”. In: SIGPLAN Not. 41.11 (Oct. 2006), pp. 185–194. issn: 0362-1340. doi: 10 . 1145 / 1168918 . 1168881. url: http : //doi.acm.org/10.1145/1168918.1168881. 2lba Garza January 19, 2015 Book List [HP12] J.L. Hennessy and D.A. Patterson. Computer Architecture: A Quantitative Approach. Morgan Kaufmann. Morgan Kaufmann/Elsevier, 2012. isbn: 9780123838728. Nicki Gotsis Graduate Coordinator Room 310, Computer Science (609) 258-5387 ngotsis(a)cs.princeton.edu

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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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