- talks - lists.cs.princeton.edu

ORFE Colloquium: Tim Roughgarden, TODAY at 5pm in Sherrerd Hall 101
by Emily Lawrence 17 Apr '18

17 Apr '18

===== TODAY: ORFE Department Colloquium Announcement===== DATE: Tuesday, April 17, 2018 TIME: 5pm LOCATION: Sherrerd Hall 101 SPEAKER: Tim Roughgarden, Stanford University Title: How Computer Science Informs Modern Auction Design Abstract: Over the last twenty years, computer science has relied on concepts borrowed from game theory and economics to reason about applications ranging from internet routing to real-time auctions for online advertising. More recently, ideas have increasingly flowed in the opposite direction, with concepts and techniques from computer science beginning to influence economic theory and practice. In this lecture, Tim Roughgarden will illustrate this point with a detailed case study of the 2016-2017 Federal Communications Commission incentive auction for repurposing wireless spectrum. Computer science techniques, ranging from algorithms for NP-hard problems to nondeterministic communication complexity, have played a critical role both in the design of the reverse auction (with the government procuring existing licenses from television broadcasters) and in the analysis of the forward auction (when the procured licenses sell to the highest bidder). _____ Bio: Roughgarden is a professor of computer science and, by courtesy, management science and engineering at Stanford University. He joined the Stanford faculty in 2004, following a Ph.D. at Cornell University and a postdoc at the University of California, Berkeley. His research interests include the many connections between computer science and economics as well as the design, analysis, applications and limitations of algorithms. He has received the ACM Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), the Kalai Prize in Computer Science and Game Theory, the Social Choice and Welfare Prize, the Mathematical Programming Societys Tucker Prize, and the EATCS-SIGACT Gödel Prize. His books include Twenty Lectures on Algorithmic Game Theory (2016) and Algorithms Illuminated (2017).

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Colloquium Speaker-Joy Arulraj, TODAY at 12:30
by Emily C. Lawrence 17 Apr '18

17 Apr '18

Joy Arulraj from Carnegie Mellon University Tuesday, April 17, 2018 - 12:30pm Computer Science - Room 105 Host: Wyatt Lloyd “Data Management on Non-Volatile Memory” We are at an exciting point in the evolution of memory technology. Device manufacturers have created a new non-volatile memory (NVM) technology that can serve as both system memory and storage. NVM supports fast reads and writes similar to volatile memory, but all writes to it are persistent like a solid-state disk. The advent of NVM invalidates decades of design decisions that are deeply embedded in today's database management systems (DBMSs). These systems are unable to take full advantage of NVM because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of today's DBMSs are unnecessary and will degrade the performance of data-intensive applications. Thus, the best way to resolve these shortcomings is by designing a new system explicitly tailored for NVM. In this talk, I will present my research on the design and development of an NVM DBMS, called Peloton. Peloton's architecture shows that the impact of NVM spans across all the layers of the DBMS. I will first introduce write-behind logging, an NVM-centric protocol that improves the availability of the database system by two orders-of-magnitude compared to the widely-used write-ahead logging protocol. I will then present the BzTree , an NVM-centric index data structure that illustrates how to simplify programming on NVM. In drawing broader lessons from this work, I will argue that all types of software systems, including file systems, machine-learning systems, and key-value stores, are amenable to similar architectural changes to achieve high performance and availability on NVM. Bio: Joy Arulraj is a Ph.D. candidate at Carnegie Mellon University. His research interests are in database systems and data science. As part of his dissertation work, he has studied and built the first non-volatile memory database system, called Peloton, for performing large-scale transaction processing and real-time data analytics. He is the recipient of the Carnegie Mellon Presidential Fellowship and a Samsung Ph.D. Fellowship.

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Reminder: Colloquium Speaker-Joy Arulraj, tomorrow at 12:30
by Emily C. Lawrence 16 Apr '18

16 Apr '18

Joy Arulraj from Carnegie Mellon University Tuesday, April 17, 2018 - 12:30pm Computer Science - Room 105 Host: Wyatt Lloyd “Data Management on Non-Volatile Memory” We are at an exciting point in the evolution of memory technology. Device manufacturers have created a new non-volatile memory (NVM) technology that can serve as both system memory and storage. NVM supports fast reads and writes similar to volatile memory, but all writes to it are persistent like a solid-state disk. The advent of NVM invalidates decades of design decisions that are deeply embedded in today's database management systems (DBMSs). These systems are unable to take full advantage of NVM because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of today's DBMSs are unnecessary and will degrade the performance of data-intensive applications. Thus, the best way to resolve these shortcomings is by designing a new system explicitly tailored for NVM. In this talk, I will present my research on the design and development of an NVM DBMS, called Peloton. Peloton's architecture shows that the impact of NVM spans across all the layers of the DBMS. I will first introduce write-behind logging, an NVM-centric protocol that improves the availability of the database system by two orders-of-magnitude compared to the widely-used write-ahead logging protocol. I will then present the BzTree , an NVM-centric index data structure that illustrates how to simplify programming on NVM. In drawing broader lessons from this work, I will argue that all types of software systems, including file systems, machine-learning systems, and key-value stores, are amenable to similar architectural changes to achieve high performance and availability on NVM. Bio: Joy Arulraj is a Ph.D. candidate at Carnegie Mellon University. His research interests are in database systems and data science. As part of his dissertation work, he has studied and built the first non-volatile memory database system, called Peloton, for performing large-scale transaction processing and real-time data analytics. He is the recipient of the Carnegie Mellon Presidential Fellowship and a Samsung Ph.D. Fellowship.

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Colloquium Speaker-Jacob Andreas, TODAY at 12:30
by Emily C. Lawrence 16 Apr '18

16 Apr '18

Jacob Andreas from University of California at Berkeley Monday, April 16, 2018 - 12:30pm Computer Science - Room 105 Host: Sanjeev Arora “Learning from Language” Natural language is built from a library of concepts and compositional operators that provide a rich source of information about how humans understand the world. Can this information help us build better machine learning models? In this talk, we'll explore three ways of integrating compositional linguistic structure and learning: using language as a source of modular reasoning operators for question answering, as a scaffold for fast and generalizable reinforcement learning, and as a tool for understanding representations in neural networks. Bio: Jacob Andreas is a fifth-year PhD student at UC Berkeley working in natural language processing. He holds a B.S. from Columbia and an M.Phil. from Cambridge, where he studied as a Churchill scholar. His papers were recognized at NAACL 2016 and ICML 2017. Jacob has been an NSF graduate fellow, a Huawei--Berkeley AI fellow, and a Facebook fellow.

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Jonathan Schneider will present his PreFPO Thursday, April 19, 2018 at 1:30 in Room CS302
by Barbara A. Mooring 15 Apr '18

15 Apr '18

Jonathan Schneider will present his PreFPO Thursday, April 19, 2018 at 1:30 CS302 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-Jacob Andreas, tomorrow at 12:30
by Emily C. Lawrence 15 Apr '18

15 Apr '18

Jacob Andreas from University of California at Berkeley Monday, April 16, 2018 - 12:30pm Computer Science - Room 105 Host: Sanjeev Arora “Learning from Language” Natural language is built from a library of concepts and compositional operators that provide a rich source of information about how humans understand the world. Can this information help us build better machine learning models? In this talk, we'll explore three ways of integrating compositional linguistic structure and learning: using language as a source of modular reasoning operators for question answering, as a scaffold for fast and generalizable reinforcement learning, and as a tool for understanding representations in neural networks. Bio: Jacob Andreas is a fifth-year PhD student at UC Berkeley working in natural language processing. He holds a B.S. from Columbia and an M.Phil. from Cambridge, where he studied as a Churchill scholar. His papers were recognized at NAACL 2016 and ICML 2017. Jacob has been an NSF graduate fellow, a Huawei--Berkeley AI fellow, and a Facebook fellow.

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Colloquium Speaker-Taesoo Kim, Thursday, April 19
by Emily C. Lawrence 13 Apr '18

13 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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MONDAY April 16 at 4:30pm in B205 E-Quad: EE Seminar Series - Davide Scaramuzza, ETH Zurich and University of Zurich
by Emily Lawrence 12 Apr '18

12 Apr '18

EE SEMINAR SERIES Speaker: Davide Scaramuzza, ETH Zurich and University of Zurich Title: Autonomous, Agile, Vision-controlled Drones: from Frame to Event Vision Day: Monday, April 16, 2018 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Naveen Verma Abstract: Davide Scaramuzza Autonomous quadrotors will soon play a major role in search-and-rescue and remote-inspection missions, where a fast response is crucial. Quadrotors have the potential to navigate quickly through unstructured environments, enter and exit buildings through narrow gaps, and fly through collapsed buildings. However, their speed and maneuverability are still far from those of birds. Indeed, agile navigation through unknown, indoor environments poses a number of challenges for robotics research in terms of perception, state estimation, planning, and control. In this talk, I will show that active vision is crucial in order to plan trajectories that improve the quality of perception. Also, I will talk about our recent results on event based vision to enable low latency sensory motor control and navigation in low light and high dynamic environment, where traditional vision sensor fail. Bio: Davide Scaramuzza (born in 1980, Italian) is Tenured Associate Professor of Robotics and Perception at both departments of Informatics (University of Zurich) and Neuroinformatics (ETH Zurich and University of Zurich), where he does research at the intersection of robotics, computer vision, and neuroscience. He did his PhD in robotics and computer vision at ETH Zurich (with Roland Siegwart) and a postdoc at the University of Pennsylvania (with Vijay Kumar and Kostas Daniilidis). >From 2009 to 2012, he led the European project sFly, which introduced the PX4 autopilot and pioneered visual-SLAM–based autonomous navigation of micro drones. For his research contributions, he was awarded the IEEE Robotics and Automation Society Early Career Award, Misha Mahowald Neuromorphic Engineering Award, the SNSF-ERC Starting Grant (equivalent to NSF Career Award), a Google Research Award, the European Young Researcher Award, and several conference paper awards. He coauthored the book “Introduction to Autonomous Mobile Robots” (published by MIT Press) and more than 100 papers on robotics and perception. In 2015, he cofounded a venture, called Zurich-Eye, dedicated to the commercialization of visual-inertial navigation solutions for mobile robots, which later became Facebook-Oculus VR Switzerland. He was also the strategic advisor of Dacuda, an ETH spinoff dedicated to inside-out VR solutions, which later became Magic Leap Zurich. Many aspects of his research have been prominently featured in the popular press, such as Discovery Channel, BBC, IEEE Spectrum, MIT Technology Review Magazine. Furthermore, I was invited to speak at TEDxZurich.

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Colloquium Speaker-Saurabh Gupta TODAY at 12:30pm
by Emily C. Lawrence 12 Apr '18

12 Apr '18

Saurabh Gupta, University of California at Berkeley Thursday, April 12, 2018 - 12:30pm Computer Science - Room 105 Host: Szymon Rusinkiewicz “Visual Perception and Navigation in 3D Scenes” Abstract: In recent times, computer vision has made great leaps towards 2D understanding of sparse visual snapshots of the world. This is insufficient for robots that need to exist and act in the 3D world around them based on a continuous stream of multi-modal inputs. In this talk, I will present some of my efforts in bridging this gap between computer vision and robotics. I will show how thinking about computer vision and robotics together, brings out limitations of current computer vision tasks and techniques, and motivates joint study of perception and action for robotic tasks. I will showcase these aspects via three examples: 3D scene understanding, representation learning for varied modalities, and visual navigation. I will conclude by pointing out future research directions at the intersection of computer vision and robotics, thus showing how the two fields are ready to get back together. Bio: Saurabh Gupta is a Ph.D. student at UC Berkeley, where he is advised by Jitendra Malik. His research interests include computer vision, robotics and machine learning. His PhD work focuses on 3D scene understanding, and visual navigation. His work is supported by a Berkeley Fellowship and a Google Fellowship in Computer Vision.

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Reminder: Colloquium Speaker-Saurabh Gupta tomorrow at 12:30pm
by Emily C. Lawrence 11 Apr '18

11 Apr '18

Saurabh Gupta, University of California at Berkeley Thursday, April 12, 2018 - 12:30pm Computer Science - Room 105 Host: Szymon Rusinkiewicz “Visual Perception and Navigation in 3D Scenes” Abstract: In recent times, computer vision has made great leaps towards 2D understanding of sparse visual snapshots of the world. This is insufficient for robots that need to exist and act in the 3D world around them based on a continuous stream of multi-modal inputs. In this talk, I will present some of my efforts in bridging this gap between computer vision and robotics. I will show how thinking about computer vision and robotics together, brings out limitations of current computer vision tasks and techniques, and motivates joint study of perception and action for robotic tasks. I will showcase these aspects via three examples: 3D scene understanding, representation learning for varied modalities, and visual navigation. I will conclude by pointing out future research directions at the intersection of computer vision and robotics, thus showing how the two fields are ready to get back together. Bio: Saurabh Gupta is a Ph.D. student at UC Berkeley, where he is advised by Jitendra Malik. His research interests include computer vision, robotics and machine learning. His PhD work focuses on 3D scene understanding, and visual navigation. His work is supported by a Berkeley Fellowship and a Google Fellowship in Computer Vision.

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