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Distinguished Colloquium Speaker Jeff Heer, Today!
by Jennifer Cirillo 06 Nov '17

06 Nov '17

Distinguished Colloquium Speaker Professor Jeffrey Michael Heer, University of Washington Monday, November 6, 2017 - 12:30pm Computer Science - Room 105 Host: Adam Finkelstein Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, this talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. Jeffrey Heer is an Associate Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by Jeff and his collaborators (Vega, D3.js, Protovis, Prefuse) are used by researchers, companies, and thousands of data enthusiasts around the world. Jeff’s research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, UIST, CSCW; IEEE InfoVis, VAST, EuroVis). Other honors include MIT Technology Review’s TR35 (2009), a Sloan Fellowship (2012), and the ACM Grace Murray Hopper Award (2016). Jeff holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then betrayed to join the Stanford faculty (2009–2013). He is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.

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Fwd: CS/EE Seminar - Monday, November 6. 2017, 4:30 pm, Computer Science 105 - Prof. Benjamin Lee
by Margaret Martonosi 05 Nov '17

05 Nov '17

FYI: Prof. Ben Lee from Duke University will give a seminar tomorrow, Monday Nov 6 at 4:30 in the CS Small Auditorium on "Rethinking Datacenter Management with Game Theory”. Full info below. -mrm Margaret Martonosi H. T. Adams '35 Professor of Computer Science. Director, Keller Center for Innovation in Engineering Education Princeton University ■ mrm(a)princeton.edu ■ http://www.princeton.edu/~mrm > Begin forwarded message: > > From: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > Subject: CS/EE Seminar - Monday, November 6. 2017, 4:30 pm, Computer Science 105 - Prof. Benjamin Lee > Date: October 23, 2017 at 11:59:27 AM EDT > To: ee-seminar(a)Princeton.EDU > Reply-To: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > > > > > Department of Computer Science and > Department of Electrical Engineering Seminar > > > > Speaker: Prof. Benjamin Lee > Duke University > > Title: Rethinking Datacenter Management with Game Theory > Date: Monday, November 6, 2017 > Time: 4:30 p.m. > Room: Computer Science, Room 105 > Host: Prof. Margaret Martonosi and Prof. David Wentzlaff > > Abstract: Sharing datacenter hardware improves energy efficiency, but whether strategic users participate in consolidated systems depends on management policies. Users who dislike allocations may refuse to participate and deploy private, less-efficient systems. We rethink systems management, drawing on game theory to model strategic behavior and incentivize participation. We illustrate this perspective for three fundamental challenges in datacenters. For workload colocation, we design matching games to produce stable coalitions in which more users share with preferred co-runners. For resource allocation, we use Cobb-Douglas utility functions to produce fair allocations that incentivize users to share cache and memory. For power delivery, we design sprinting games to produce equilibria in which users selfishly draw power for performance boosts yet avoid oversubscribing the shared supply. Collectively, these varied solution concepts provide a rich toolkit for rigorously managing datacenters shared by strategic and competitive users. > > Biography: Benjamin Lee is an Associate Professor of Electrical and Computer Engineering at Duke University. His research focuses on computer architectures, security / privacy, and energy efficiency with links to statistical inference and algorithmic economics. His research has been recognized by IEEE Micro Top Picks (4x), Communications of the ACM Research Highlights (2x), as well as varied paper honors from ASPLOS, MICRO, and SC. He received the NSF Computing Innovation Fellowship, NSF CAREER Award, and Google Faculty Research Award. He held visiting positions at Microsoft Research, Intel Labs, and Lawrence Livermore National Lab. Dr. Lee received his B.S. from the University of California at Berkeley, his Ph.D. from Harvard University, and his post-doctorate from Stanford University.

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Distinguished Colloquium Speaker Jeff Heer, Monday, November 6th - 12:30pm
by Jennifer Cirillo 05 Nov '17

05 Nov '17

Distinguished Colloquium Speaker Professor Jeffrey Michael Heer, University of Washington Monday, November 6, 2017 - 12:30pm Computer Science - Room 105 Host: Adam Finkelstein Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, this talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. Jeffrey Heer is an Associate Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by Jeff and his collaborators (Vega, D3.js, Protovis, Prefuse) are used by researchers, companies, and thousands of data enthusiasts around the world. Jeff’s research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, UIST, CSCW; IEEE InfoVis, VAST, EuroVis). Other honors include MIT Technology Review’s TR35 (2009), a Sloan Fellowship (2012), and the ACM Grace Murray Hopper Award (2016). Jeff holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then betrayed to join the Stanford faculty (2009–2013). He is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.

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Distinguished Colloquium Speaker Jeff Heer, Monday, November 6th - 12:30pm
by Jennifer Cirillo 31 Oct '17

31 Oct '17

Distinguished Colloquium Speaker Professor Jeffrey Michael Heer, University of Washington Monday, November 6, 2017 - 12:30pm Computer Science - Room 105 Host: Adam Finkelstein Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, this talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. Jeffrey Heer is an Associate Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by Jeff and his collaborators (Vega, D3.js, Protovis, Prefuse) are used by researchers, companies, and thousands of data enthusiasts around the world. Jeff’s research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, UIST, CSCW; IEEE InfoVis, VAST, EuroVis). Other honors include MIT Technology Review’s TR35 (2009), a Sloan Fellowship (2012), and the ACM Grace Murray Hopper Award (2016). Jeff holds B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then betrayed to join the Stanford faculty (2009–2013). He is also a co-founder of Trifacta, a provider of interactive tools for scalable data transformation.

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popular talk on ML at IAS today 5:30pm
by Sanjeev Arora 27 Oct '17

27 Oct '17

I am involved in a popular lecture + panel discussion today on ML at IAS. 5:30pm. https://www.ias.edu/events/arora-zemel-publiclecture Best Sanjeev

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Yonatan Naamad will present his FPO, "Hardness from Densest Subgraph Conjectures"on Monday, October 30, 2017 at 4pm in CS 402
by Nicki Gotsis 24 Oct '17

24 Oct '17

Yonatan Naamad will present his FPO, "Hardness from Densest Subgraph Conjectures"on Monday, October 30, 2017 at 4pm in CS 402. The members of his committee are: Moses Charikar (adviser); Readers: Sanjeev Arora and Anthony Wirth (University of Melbourne); Examiners: Moses Charikar, Mark Braverman, and Matt Weinberg. A copy of his thesis is available in Room 310. Everyone is invited to attend his talk. The talk abstract is attached to this email.

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Fwd: CS/EE Seminar - Monday, November 6. 2017, 4:30 pm, Computer Science 105 - Prof. Benjamin Lee
by Jennifer Rexford 23 Oct '17

23 Oct '17

> > From: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > Subject: CS/EE Seminar - Monday, November 6. 2017, 4:30 pm, Computer Science 105 - Prof. Benjamin Lee > Date: October 23, 2017 at 11:59:27 AM EDT > To: ee-seminar(a)Princeton.EDU > Reply-To: "Lori A. Bailey" <lbailey(a)Princeton.EDU> > > > > > Department of Computer Science and > Department of Electrical Engineering Seminar > > > > Speaker: Prof. Benjamin Lee > Duke University > > Title: Rethinking Datacenter Management with Game Theory > Date: Monday, November 6, 2017 > Time: 4:30 p.m. > Room: Computer Science, Room 105 > Host: Prof. Margaret Martonosi and Prof. David Wentzlaff > > Abstract: Sharing datacenter hardware improves energy efficiency, but whether strategic users participate in consolidated systems depends on management policies. Users who dislike allocations may refuse to participate and deploy private, less-efficient systems. We rethink systems management, drawing on game theory to model strategic behavior and incentivize participation. We illustrate this perspective for three fundamental challenges in datacenters. For workload colocation, we design matching games to produce stable coalitions in which more users share with preferred co-runners. For resource allocation, we use Cobb-Douglas utility functions to produce fair allocations that incentivize users to share cache and memory. For power delivery, we design sprinting games to produce equilibria in which users selfishly draw power for performance boosts yet avoid oversubscribing the shared supply. Collectively, these varied solution concepts provide a rich toolkit for rigorously managing datacenters shared by strategic and competitive users. > > Biography: Benjamin Lee is an Associate Professor of Electrical and Computer Engineering at Duke University. His research focuses on computer architectures, security / privacy, and energy efficiency with links to statistical inference and algorithmic economics. His research has been recognized by IEEE Micro Top Picks (4x), Communications of the ACM Research Highlights (2x), as well as varied paper honors from ASPLOS, MICRO, and SC. He received the NSF Computing Innovation Fellowship, NSF CAREER Award, and Google Faculty Research Award. He held visiting positions at Microsoft Research, Intel Labs, and Lawrence Livermore National Lab. Dr. Lee received his B.S. from the University of California at Berkeley, his Ph.D. from Harvard University, and his post-doctorate from Stanford University.

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Andrej Risteski will present his FPO, "New techniques for learning and inference in Bayesian models" on Thursday, October 26, 2017 at 1pm in CS 402
by Nicki Gotsis 19 Oct '17

19 Oct '17

Andrej Risteski will present his FPO, "New techniques for learning and inference in Bayesian models" on Thursday, October 26, 2017 at 1pm in CS 402. The members of his committee are: Readers: Sanjeev Arora (adviser), Rong Ge (Duke University), and Elad Hazan Nonreaders: Sanjeev Arora, Elad Hazan, Bernard Chazelle, and Rong Ge (Duke University), and Mark Braverman A copy of his thesis, is available in Room 310. Everyone is invited to attend his talk. The talk abstract follows below. Abstract: A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the best fit graphical model from raw data, while inference is the task of answering probabilistic queries for a known graphical model (for example what is the marginal distribution of one of the variables or what is the distribution of a subset of variables, after conditioning on the values of some other subset of variables). Learning can be thought of as finding a graphical model that explains the raw data, while the inference queries extract the knowledge the graphical model contains. This thesis introduces new provable techniques for performing both of these tasks, in the context of both latent-variable models – in which a portion of the variables in the graphical model are not observed, as well as fully-observable undirected graphical models (Markov Random Fields). Chapters 2 and 3 will focus on learning latent-variable models, while Chapter 4 will focus on inference in Markov Random Fields. In Chapter 2, I will contribute the first provable results for analyzing variational Bayes: a family of alternatingminimization style algorithms which is very popular in practice for learning latent-variable models. Despite it’s popularity with practitioners, the only theoretical guarantees prior to this work concerned convergence to local minima. We will prove that under reasonable assumptions, in the context of topic models, these algorithms will converge to the global minimum. Subsequently, in Chapter 3, we will use the method-of-moments along with new techniques in tensor decomposition and constrained matrix factorization to derive algorithms for learning noisy-OR networks – the textbook example of a probabilistic model for causal relationships. Importantly, these techniques were only applicable to linear latentvariable models – which noisy-OR is not. In Chapter 4, I will contribute a new understanding of a class of variational methods for calculating partition functions in Markov Random Fields. The key technical ingredient is a connection to convex programming hierarchies – a recent area of interest in combinatorial optimization, along with approximations of the entropy of a distribution based on low-order moment information.

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Sotirios Apostolakis will be presenting his general exam "Securing Commodity Systems with Containment: a Redis Server Case Study" on Monday, October 23, 2017 at 2:30pm in CS 402.
by Nicki Gotsis 18 Oct '17

18 Oct '17

Sotirios Apostolakis will be presenting his general exam " Securing Commodity Systems with Containment: a Redis Server Case Study" on Monday, October 23, 2017 at 2:30pm in CS 402. The members of his committee are David August (adviser), Nick Feamster, and Andrew Appel. Everyone is invited to attend his talk. Abstract: Modern computing systems are too complex to be vulnerability free. Current reactive approaches to security are not effective, and proposed "clean slate" approaches, requiring formal proofs of correctness for whole systems, are impractical. Prior work introduced TrustGuard, an approach that provides proactive protection against malicous and buggy hardware by allowing only the correct execution of signed software to produce output. This work expands TrustGuard’s containment guarantees to additionally protect against flawed software. To demonstrate TrustGuard's expanded capabilities, we implemented a system that secures a commodity Redis server against deviations from the Redis server specification. We show that 4K RTL LOC (one time cost on FPGA network card) and 3K software LOC can secure a modern Redis server consisting of hundreds of millions of LOC of hardware, OS, libraries and applications.

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Raghuvansh Saxena will present his General Exam on Monday, October 16, 2017 in CS 233 at 11am
by Nicki Gotsis 13 Oct '17

13 Oct '17

Raghuvansh Saxena will present his General Exam on Monday, October 16, 2017 in CS 233 at 11am. The members of his committee are: Gillat Kol (adviser), Matt Weinberg, and Mark Braverman. Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. "Interactive Coding Over the Noisy Broadcast Channel" A set of n players, each holding a private input bit, communicate over a noisy broadcast channel. Their mutual goal is for all players to learn all inputs. At each round one of the players broadcasts a bit to all the other players, and the bit received by each player is flipped with a fixed constant probability (independently for each recipient). How many rounds are needed? This problem was first suggested by El Gamal in 1984. In 1988, Gallager gave an elegant noise-resistant protocol requiring only O(n log log n) rounds. The problem got resolved in 2005 by a seminal paper of Goyal, Kindler, and Saks, proving that Gallager’s protocol is essentially optimal. We revisit the above noisy broadcast problem and show that O(n) rounds suffice. This is possible due to a relaxation of the model assumed by the previous works. We no longer demand that exactly one player broadcasts in every round, but rather allow any number of players to broadcast. However, if it is not the case that exactly one player chooses to broadcast, each of the other players gets an adversely chosen bit. We generalized the above result and initiate the study of interactive coding over the noisy broadcast channel. We show that any interactive protocol that works over the noiseless broadcast channel can be simulated over our restrictive noisy broadcast model with constant blowup of the communication. Reading list: Textbook: Computational Complexity: A Modern Approach - By Sanjeev Arora and Boaz Barak. Survey: Coding for Interactive Communication: A Survey - By Ran Gelles. Papers: Finding parity in a simple broadcast network - Robert Gallager Deterministic coding for interactive communication - Leonard Schulman Communication on noisy channels: a coding theorem for computation - Leonard Schulman Lower Bounds for the Noisy Broadcast Problem - Navin Goyal, Guy Kindler, and Michael Saks Towards Coding for Maximum Errors in Interactive Communication - Mark Braverman and Anup Rao A coding theorem for distributed computation - Sridhar Rajagopalan and Leonard Schulman Information Equals Amortized Communication - Mark Braverman and Anup Rao Interactive channel capacity - Gillat Kol and Ran Raz Constant-rate coding for multiparty interactive communication is impossible - Mark Braverman, Klim Efremenko, Ran Gelles, and Bernhard Haeupler Optimal Error Rates for Interactive Coding I: Adaptivity and Other Settings - Mohsen Ghaffari, Bernhard Haeupler, and Madhu Sudan

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