- talks - lists.cs.princeton.edu

Jeff Dean, TODAY at 4:30
by Emily C. Lawrence 24 Apr '18

24 Apr '18

Jeff Dean from Google Research Tuesday, April 24 , 2018 - 4:30pm Friend Center - Room 101 Host: Kai Li Event page: [ http://www.cs.princeton.edu/events/25679 | http://www.cs.princeton.edu/events/25679 ] "Deep Learning to Solve Challenging Problems" For the past six years, the Google Brain team has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. We have made significant progress in computer vision, speech recognition, language understanding, machine translation, healthcare, robotic control, and other areas. Our group has open-sourced the TensorFlow system, a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the research and computer systems work we've done with an eye towards how it can be used to solve challenging problems. This talk describes joint work with many people at Google. Bio: Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he co-founded and leads the Google Brain team, Google's deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co- implementor of Google's distributed computing infrastructure, including the MapReduce , BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award. **A reception in the Friend Center Upper Atrium will follow immediately after the talk. ***This event will be LIVESTREAMED! Please visit this link to watch the talk. [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ]

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Colloquium Speaker-Umesh Vazirani, TODAY at 12:30
by Emily C. Lawrence 23 Apr '18

23 Apr '18

Umesh Vazirani from UC Berkeley Monday, April 23, 2018 - 12:30pm Computer Science - Room 105 Host: Sanjeev Arora “Quantum Supremacy” and the Complexity of Random Circuit Sampling A critical goal for the field of quantum computation is quantum supremacy -- a demonstration of any quantum computation that is prohibitively hard for classical computers. Besides dispelling any skepticism about the viability of quantum computers, quantum supremacy also provides a test of quantum theory in the realm of high complexity. A leading near-term candidate, put forth by the Google/UCSB team, is sampling from the probability distributions of randomly chosen quantum circuits, called Random Circuit Sampling (RCS). While RCS was defined with experimental realization in mind (the first results are expected later this year), we give the first complexity-theoretic evidence of classical hardness of RCS, placing it on par with the best theoretical proposals for supremacy. Specifically, we show that RCS satisfies an average-case hardness condition -- computing output probabilities of typical quantum circuits is as hard as computing them in the worst-case, and therefore #P-hard. Our reduction exploits the polynomial structure in the output amplitudes of random quantum circuits, enabled by the Feynman path integral. We also describe a new verification measure which in some formal sense maximizes the information gained from experimental samples. Based on joint work with Adam Bouland , Bill Fefferman and Chinmay Nirkhe .

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Reminder: Distinguished Lecturer Jeff Dean, tomorrow at 4:30
by Emily C. Lawrence 23 Apr '18

23 Apr '18

Jeff Dean from Google Research Tuesday, April 24, 2018 - 4:30pm Friend Center - Room 101 Host: Kai Li Event page: [ http://www.cs.princeton.edu/events/25679 | http://www.cs.princeton.edu/events/25679 ] "Deep Learning to Solve Challenging Problems" For the past six years, the Google Brain team has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. We have made significant progress in computer vision, speech recognition, language understanding, machine translation, healthcare, robotic control, and other areas. Our group has open-sourced the TensorFlow system, a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the research and computer systems work we've done with an eye towards how it can be used to solve challenging problems. This talk describes joint work with many people at Google. Bio: Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he co-founded and leads the Google Brain team, Google's deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award. **A reception in the Friend Center Upper Atrium will follow immediately after the talk.

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Reminder: Umesh Vazirani, tomorrow at 12:30
by Emily C. Lawrence 22 Apr '18

22 Apr '18

Umesh Vazirani from UC Berkeley Monday, April 23, 2018 - 12:30pm Computer Science - Room 105 Host: Sanjeev Arora “Quantum Supremacy” and the Complexity of Random Circuit Sampling A critical goal for the field of quantum computation is quantum supremacy -- a demonstration of any quantum computation that is prohibitively hard for classical computers. Besides dispelling any skepticism about the viability of quantum computers, quantum supremacy also provides a test of quantum theory in the realm of high complexity. A leading near-term candidate, put forth by the Google/UCSB team, is sampling from the probability distributions of randomly chosen quantum circuits, called Random Circuit Sampling (RCS). While RCS was defined with experimental realization in mind (the first results are expected later this year), we give the first complexity-theoretic evidence of classical hardness of RCS, placing it on par with the best theoretical proposals for supremacy. Specifically, we show that RCS satisfies an average-case hardness condition -- computing output probabilities of typical quantum circuits is as hard as computing them in the worst-case, and therefore #P-hard. Our reduction exploits the polynomial structure in the output amplitudes of random quantum circuits, enabled by the Feynman path integral. We also describe a new verification measure which in some formal sense maximizes the information gained from experimental samples. Based on joint work with Adam Bouland, Bill Fefferman and Chinmay Nirkhe.

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Colloquium Speaker-Bo Wang, Friday, April 27
by Emily C. Lawrence 20 Apr '18

20 Apr '18

Bo Wang, Stanford University Friday, April 27, 2018 - 12:30pm Computer Science - Room 105 Host: Mona Singh "Machine Learning Algorithms for Exploiting Spectral Structures of Biological Networks" Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from populations to a single cell. How to extract and understand non-trivial topological features and structures inherent in the networks is critical to understanding interactions within complicated biological systems. In this talk, I will introduce recent developments of machine learning algorithms that exploit spectral structures of networks for a wide range of biological applications, ranging from single-cell analysis to function prediction on protein-protein interaction networks. Specifically, I will first present a new method named SIMLR, combining both low-rank spectral regularization and multiple-kernel learning, to construct cell networks for sparse noisy single-cell RNA- seq data. The learned cell networks will enable effective dimension reduction, clustering and visualization. Second, I will discuss a novel method, Network Enhancement (NE), that aims to de-noise complex networks such as protein-protein interaction networks without corrupting spectral structures of the networks, therefore improving function prediction accuracy. Last, I will also briefly introduce recent advances where deep convolutional neural network is applied on biological networks (e.g., drug-target network) as a first-order spectral approximation of network structures. Bio: Bo Wang is a recent PhD graduate in Computer Science at Stanford University, an IEEE and CVF Fellow and an NSF Graduate Research Fellow. His research focuses on machine learning (particularly deep learning) on many applications in computer vision (e.g., image segmentation, object detection) and computational biology (e.g., single-cell analysis, integrative cancer subtyping). Prior to Stanford, he received his master degree at University of Toronto, majoring in numerical analysis.

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Colloquium Speaker-Umesh Vazirani, Monday, April 23
by Emily Lawrence 19 Apr '18

19 Apr '18

Umesh Vazirani from UC Berkeley Monday, April 23, 2018 - 12:30pm Computer Science - Room 105 Host: Sanjeev Arora "Quantum Supremacy" and the Complexity of Random Circuit Sampling A critical goal for the field of quantum computation is quantum supremacy -- a demonstration of any quantum computation that is prohibitively hard for classical computers. Besides dispelling any skepticism about the viability of quantum computers, quantum supremacy also provides a test of quantum theory in the realm of high complexity. A leading near-term candidate, put forth by the Google/UCSB team, is sampling from the probability distributions of randomly chosen quantum circuits, called Random Circuit Sampling (RCS). While RCS was defined with experimental realization in mind (the first results are expected later this year), we give the first complexity-theoretic evidence of classical hardness of RCS, placing it on par with the best theoretical proposals for supremacy. Specifically, we show that RCS satisfies an average-case hardness condition -- computing output probabilities of typical quantum circuits is as hard as computing them in the worst-case, and therefore #P-hard. Our reduction exploits the polynomial structure in the output amplitudes of random quantum circuits, enabled by the Feynman path integral. We also describe a new verification measure which in some formal sense maximizes the information gained from experimental samples. Based on joint work with Adam Bouland, Bill Fefferman and Chinmay Nirkhe.

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Colloquium Speaker-Taesoo Kim, TODAY at 12:30
by Emily C. Lawrence 19 Apr '18

19 Apr '18

Taesoo Kim from Georgia Institute of Technology Thursday, April 19, 2018 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan “Scaling Security Practices: Automated Approaches to Eliminate Security Vulnerabilities” Computer systems are highly vulnerable; attackers everyday discover new security vulnerabilities and exploit them to compromise the target systems. This talk will present our approaches to automatically prevent software vulnerabilities from exploitation. In particular, this talk will describe in detail two classes of vulnerabilities: an emerging class, called "type confusion" (or "bad casting"), that are commonly seen in modern web browsers, and a new class that we discovered, called "uninitialized padding," causing information leakage in the Linux kernel. This talk will explain what these vulnerabilities are, how attackers exploit them, why/how developers introduced them, and why it is non-trivial to avoid them in complex, real-world programs. Finally, our approaches to automatically eliminate them in practice will be demonstrated. Bio: Taesoo Kim is a Catherine M. and James E. Allchin Early Career Assistant Professor in the School Computer Science at the Georgia Institute of Technology (Georgia Tech). He also serves as the director of the Georgia Tech Systems Software and Security Center (GTS3). He is genuinely interested in building a system that prioritizes security principles first and foremost. Those principles include the total design of the system, analysis of its implementation, elimination of certain classes of vulnerabilities, and clear separation of its trusted components. His thesis work, in particular, focused on detecting and recovering from attacks on computer systems, known as "undo computing." Taesoo holds a S.M. (2011) and a Ph.D. (2014) from MIT.

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Ben Lee, May 1 at 4:30
by Emily Lawrence 18 Apr '18

18 Apr '18

Ben Lee, Duke University May 1, 2018 at 4:30 p.m. Maeder Hall auditorium Title: Datacenters and Energy Efficiency: A Game-Theoretic Perspective 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 two fundamental challenges in datacenters. 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. For resource allocation, we use Cobb-Douglas utility functions to produce fair allocations that incentivize users to share cache and memory. These solutions provide foundations for rigorously managing systems shared by strategic, competitive participants. Biography: Benjamin Lee is an Associate Professor of Electrical and Computer Engineering at Duke University. He received his B.S. from the University of California at Berkeley (2004), his Ph.D. from Harvard University (2008), and his post-doctorate from Stanford University (2010). He has held visiting positions at Microsoft Research, Intel Labs, and Lawrence Livermore National Lab. Dr. Lee’s research interests include computer architecture, energy efficiency, and security / privacy. He pursues these interests by building interdisciplinary links to statistical inference and algorithmic economics. His research has been recognized by IEEE Micro Top Picks (4x), Communications of the ACM Research Highlights (3x), as well as paper honors from the ASPLOS, HPCA, MICRO, and SC conferences. He received the NSF Computing Innovation Fellowship, NSF CAREER Award, and Google Faculty Research Award.

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NOTICE NEW TIME AND ROOM Jonathan Schneider will present his PreFPO Thursday, April 19, 2018 at 10:00 am in CS402
by Barbara A. Mooring 18 Apr '18

18 Apr '18

Jonathan Schneider will present his PreFPO Thursday, April 19, 2018 at 10:00 am in CS402 The members of his committee are as follows: Mark Braverman - Adviser, Matt Weinberg,Bernard Chazelle, Elad Hazan and Omri Weinstein(Columbia) All are welcomed to attend. Title: "Learning Algorithms in Strategic Environments" Abstract: Learning algorithms are often analyzed under the assumption their inputs are drawn from stochastic or adversarial sources. Increasingly, these algorithms are being applied in strategic settings, where we can hope for stronger guarantees. My research aims to understand the performance of existing learning algorithms in these settings, and to design new algorithms that perform well in these settings. In the first part of the talk, I will discuss the problem of repeatedly auctioning off an item to a single low-regret buyer. We will show that if the buyer uses classical low-regret learning algorithms such as EXP3 or Multiplicative Weights, it is possible for the seller to extract the full welfare of the buyer; on the other hand, we present a new low-regret learning algorithm for the buyer which guarantees that the seller receives no more than Myerson revenue (the best they can achieve by offering the same price every round). In the second part of the talk, I will discuss the problem of "contextual search", a contextual, multidimensional generalization of binary search. We will use tools from integral geometry (namely, the notion of "intrinsic volumes") to design an algorithm which obtains constant regret bounds for this problem. We then apply these techniques to the problem of dynamic pricing, where we achieve a tight regret bound of O(log log T) (improving exponentially on the best existing algorithm which achieved O(log T) regret). Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Reminder: Colloquium Speaker-Taesoo Kim, tomorrow at 12:30
by Emily C. Lawrence 18 Apr '18

18 Apr '18

Taesoo Kim from Georgia Institute of Technology Thursday, April 19, 2018 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan “Scaling Security Practices: Automated Approaches to Eliminate Security Vulnerabilities” Computer systems are highly vulnerable; attackers everyday discover new security vulnerabilities and exploit them to compromise the target systems. This talk will present our approaches to automatically prevent software vulnerabilities from exploitation. In particular, this talk will describe in detail two classes of vulnerabilities: an emerging class, called "type confusion" (or "bad casting"), that are commonly seen in modern web browsers, and a new class that we discovered, called "uninitialized padding," causing information leakage in the Linux kernel. This talk will explain what these vulnerabilities are, how attackers exploit them, why/how developers introduced them, and why it is non-trivial to avoid them in complex, real-world programs. Finally, our approaches to automatically eliminate them in practice will be demonstrated. Bio: Taesoo Kim is a Catherine M. and James E. Allchin Early Career Assistant Professor in the School Computer Science at the Georgia Institute of Technology (Georgia Tech). He also serves as the director of the Georgia Tech Systems Software and Security Center (GTS3). He is genuinely interested in building a system that prioritizes security principles first and foremost. Those principles include the total design of the system, analysis of its implementation, elimination of certain classes of vulnerabilities, and clear separation of its trusted components. His thesis work, in particular, focused on detecting and recovering from attacks on computer systems, known as "undo computing." Taesoo holds a S.M. (2011) and a Ph.D. (2014) from MIT.

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