- talks - lists.cs.princeton.edu

Colloquium Speaker: Leonidas Guibas, Today, 12:30pm- reminder
by Nicole E. Wagenblast 18 Feb '16

18 Feb '16

Colloquium Speaker Leonidas Guibas, from Stanford University Thursday, February 25, 2016 - 12:30pm Computer Science 105 Networks of Shapes and Images Multimedia content has become a ubiquitous presence on all our computing devices, spanning the gamut from live content captured by device sensors such as smartphone cameras to immense databases of images, audio, video, 3D scans and 3D models stored in the cloud. As we try to maximize the utility and value of all these petabytes of content, we often do so by analyzing each piece of data individually and foregoing a deeper analysis of the relationships between the media. In this talk we focus on developing rigorous mathematical and computational tools for making such relationships or correspondences between signal and media data sets first-class citizens -- so that the relationships themselves become explicit, algebraic, storable and searchable objects. We discuss mathematical and algorithmic issues on how to represent and compute relationships or mappings at multiple levels of detail -- and go on to build entire networks based on these relationships in collections of inter-related data. Information transport and aggregation in such networks naturally lead to abstractions of objects and other visual entities, allowing data compression while capturing variability as well as shared structure. Furthermore, the network can act as a regularizer, allowing us to to benefit from the "wisdom of the collection" in performing operations on individual data sets or in map inference between them, ultimately enabling a certain joint understanding of data that provides the powers of abstraction, analogy, compression, error correction, and summarization. Examples include entity extraction from images or videos, 3D segmentation, the propagation of annotations and labels among images/videos/3D models, variability analysis in a collection of shapes, etc. Finally we briefly describe the ShapeNet effort, an attempt to build a large-scale repository of 3D models richly annotated with geometric, physical, functional, and semantic information -- both individually and in relation to other models and media. More than a repository, ShapeNet is a true network that allows information transport not only between its nodes but also to/from new visual data coming from sensors. This effectively enables us to add missing information to signals, giving us for example the ability to infer what an occluded part of an object in an image may look like, or what other object arrangements may be possible, based on the world-knowledge encoded in ShapeNet. This is joint work with several collaborators, as will be discussed during the talk. Leonidas Guibas obtained his Ph.D. from Stanford under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford. He is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University. He heads the Geometric Computation group and is part of the Graphics Laboratory, the AI Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas’ interests span geometric data analysis, computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover’s distance, Kinetic Data Structures (KDS), Metropolis light transport, heat-kernel signatures, and functional maps. Professor Guibas is an ACM Fellow, an IEEE Fellow and winner of the ACM Allen Newell award.

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Colloquium Speaker: Leonidas Guibas, Thursday, Feburary 25, 12:30pm
by Nicole E. Wagenblast 18 Feb '16

18 Feb '16

Colloquium Speaker Leonidas Guibas, from Stanford University Thursday, February 25, 2016 - 12:30pm Computer Science 105 Networks of Shapes and Images Multimedia content has become a ubiquitous presence on all our computing devices, spanning the gamut from live content captured by device sensors such as smartphone cameras to immense databases of images, audio, video, 3D scans and 3D models stored in the cloud. As we try to maximize the utility and value of all these petabytes of content, we often do so by analyzing each piece of data individually and foregoing a deeper analysis of the relationships between the media. In this talk we focus on developing rigorous mathematical and computational tools for making such relationships or correspondences between signal and media data sets first-class citizens -- so that the relationships themselves become explicit, algebraic, storable and searchable objects. We discuss mathematical and algorithmic issues on how to represent and compute relationships or mappings at multiple levels of detail -- and go on to build entire networks based on these relationships in collections of inter-related data. Information transport and aggregation in such networks naturally lead to abstractions of objects and other visual entities, allowing data compression while capturing variability as well as shared structure. Furthermore, the network can act as a regularizer, allowing us to to benefit from the "wisdom of the collection" in performing operations on individual data sets or in map inference between them, ultimately enabling a certain joint understanding of data that provides the powers of abstraction, analogy, compression, error correction, and summarization. Examples include entity extraction from images or videos, 3D segmentation, the propagation of annotations and labels among images/videos/3D models, variability analysis in a collection of shapes, etc. Finally we briefly describe the ShapeNet effort, an attempt to build a large-scale repository of 3D models richly annotated with geometric, physical, functional, and semantic information -- both individually and in relation to other models and media. More than a repository, ShapeNet is a true network that allows information transport not only between its nodes but also to/from new visual data coming from sensors. This effectively enables us to add missing information to signals, giving us for example the ability to infer what an occluded part of an object in an image may look like, or what other object arrangements may be possible, based on the world-knowledge encoded in ShapeNet. This is joint work with several collaborators, as will be discussed during the talk. Leonidas Guibas obtained his Ph.D. from Stanford under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, DEC/SRC, MIT, and Stanford. He is currently the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University. He heads the Geometric Computation group and is part of the Graphics Laboratory, the AI Laboratory, the Bio-X Program, and the Institute for Computational and Mathematical Engineering. Professor Guibas’ interests span geometric data analysis, computational geometry, geometric modeling, computer graphics, computer vision, robotics, ad hoc communication and sensor networks, and discrete algorithms. Some well-known past accomplishments include the analysis of double hashing, red-black trees, the quad-edge data structure, Voronoi-Delaunay algorithms, the Earth Mover’s distance, Kinetic Data Structures (KDS), Metropolis light transport, heat-kernel signatures, and functional maps. Professor Guibas is an ACM Fellow, an IEEE Fellow and winner of the ACM Allen Newell award.

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Colloquium Speaker: Cynthia Sung, Today, 3:00pm Bowen 222
by Nicole E. Wagenblast 18 Feb '16

18 Feb '16

Cynthia Sung Computational Tools for Robot Design: A Composition Approach Thursday , February 18th 3:00 PM Bowen Hall Rm 222 As robots become more prevalent in society, they must develop an ability to deal with more diverse situations. This ability entails customizability of not only software intelligence, but also of hardware. However, designing a functional ro-bot remains challenging and often involves many iterations of design and testing even for skilled designers. My goal is to create computational tools for making functional machines, allowing future designers to quickly improvise new hardware in response to unexpected circumstances. In this talk, I will discuss one possible approach to automated design using composition. I will describe our origami-inspired print-and-fold process that allows entire robots to be fabricated within a few hours, and I will demonstrate how foldable modules can be composed together to create foldable mechanisms and robots. The modules are represented parametrically, enabling a small set of mod-ules to describe a wide range of geometries and also allowing geometries to be optimized in a straightforward manner. I will also introduce a tool that we have developed that combines this composition approach with simulations to help human designers of all skill levels to design and fabricate custom functional robots. Cynthia Sung is a Ph.D. candidate in the Computer Science and Artificial Intelli-gence Laboratory at the Massachusetts Institute of Technology (MIT). She received a B.S. in Mechanical Engineering from Rice University in 2011 and an M.S. in Elec-trical Engineering and Computer Science from MIT in 2013. Her research interests include computational design, folding theory, and rapid fabrication, and her cur-rent work focuses on algorithms for synthesis and analysis of engineering designs.

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Colloquium- David Duvenaud, Thursday, Feb 18th, 12:30pm
by Mitra Kelly 17 Feb '16

17 Feb '16

Colloquium Speaker David Duvenaud thursday, Feb 18th, 12:30pm CS 105 Talk title: Composing differentiable procedures for modeling, inference, and optimization Talk abstract: Much recent success in machine learning has been through optimizing simple feedforward procedures, such as neural networks, using gradients. Surprisingly, many complex procedures such as message passing, filtering, inference, and even optimization itself can be meaningfully differentiated though as well. Composing these procedures lets us build sophisticated models that generalize existing methods but retain their good properties. We'll show applications to chemical design, gradient-based tuning of optimization procedures, and training procedures that don't require cross-validation. Bio: David Duvenaud is a postdoc in the Harvard Intelligent Probabilistic Systems group, working with Prof. Ryan Adams on model-based optimization, synthetic chemistry, and neural networks. He did his Ph.D. at the University of Cambridge with Carl Rasmussen and Zoubin Ghahramani. Previous to that, he worked on machine vision both with Kevin Murphy at the University of British Columbia, and later at Google Research. David also co-founded Invenia, an energy forecasting and trading firm Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu 609-258-4562

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Allison Chaney will present her Pre-FPO on Wednesday, February 24, 2016 at 1:30pm in Room CS 302.
by Nicki Gotsis 17 Feb '16

17 Feb '16

Allison Chaney will present her Pre-FPO on Wednesday, February 24, 2016 at 1:30pm in Room CS 302. The members of her committee are: David Blei (advisor, reader), Barbara Engelhardt (reader), Brandon Stewart (Sociology), and Elad Hazan Everyone is invited to attend her talk. The talk title and abstract follow below: Title: A Bayesian Model for Detecting and Characterizing Events Social scientists, such as historians and political scientists, commonly read large quantities of text to construct an accurate picture of who, what, when, and where an event happened. In this talk, I will present a Bayesian model for analyzing documents to identify and characterize events of potential significance, where events are characterized by interactions between entities (e.g., countries, organizations, individuals) that deviate from typical interaction patterns. Specifically, the model builds on topic modeling to distinguish between topics that describe “business-as-usual” and topics that deviate from these patterns. I will also describe details of approximate posterior inference for this model as well as visualization and evaluation of the results. This talk includes ongoing work.

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Fwd: EE Department Seminar - Hamed Hassani- February 25, 2016
by Jennifer Rexford 17 Feb '16

17 Feb '16

> > Speaker: > Hamed Hassani, ETH Zurich > Title: > From Communication to Sensing and Learning: Information Theory at the Heart of Data Science > > Day: > Thursday, February 25, 2016 > > > Time: > 4:30 pm > Room: > B205, E-Quad > Host: > Prof. Mung Chiang > > > > > > > > > > Abstract: > We are witnessing a new era of science — ushered in by our ability to collect massive amounts of data and unprecedented ways to learn about the physical world. Beyond the challenges of storage and communication, there are new frontiers in the acquisition, analysis and exploration of the data. In this talk, I will view these frontiers through the lens of information theory. I will argue that information theory lies at the center of data science, offering insights beyond its classical applications. As a concrete example, I will consider the problem of optimal data acquisition, a challenge which arises in active learning, optimal sensing and experimental design. Based on information theoretic foundations, and equipped with tools from submodular optimization theory, I will present a rigorous analysis of the widely-used sequential information maximization policy (also known as the information-gain heuristic). Our analysis establishes conditions under which this policy provably works near-optimally and identifies situations where the policy fails. In the latter case, our framework suggests novel, efficient surrogate objectives and algorithms that outperform classical techniques. > > Bio: > Hamed Hassani is a post-doctoral scholar at the Institute for Machine Learning at ETH Zurich. He received a Ph.D. degree in Computer and Communication Sciences from EPFL, Lausanne. Prior to that, he received a B.Sc. degree in Electrical Engineering and a B.Sc. degree in Mathematics from Sharif University of Technology, Tehran. Hamed's fields of interest include machine learning, coding and information theory as well as theory and applications of graphical models. He is the recipient of the 2014 IEEE Information Theory Society Thomas M. Cover Dissertation Award. His co-authored paper at NIPS 2015 was selected for an oral (plenary) presentation, and his co-authored paper at ISIT 2015 received the IEEE Jack Keil Wolf ISIT Student Paper Award. > >

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Colloquium Speaker: Cynthia Sung, Thursday, February 18, 3:00pm Bowen 222
by Nicole E. Wagenblast 16 Feb '16

16 Feb '16

Cynthia Sung Computational Tools for Robot Design: A Composition Approach Thursday , February 18th 3:00 PM Bowen Hall Rm 222 As robots become more prevalent in society, they must develop an ability to deal with more diverse situations. This ability entails customizability of not only software intelligence, but also of hardware. However, designing a functional ro-bot remains challenging and often involves many iterations of design and testing even for skilled designers. My goal is to create computational tools for making functional machines, allowing future designers to quickly improvise new hardware in response to unexpected circumstances. In this talk, I will discuss one possible approach to automated design using composition. I will describe our origami-inspired print-and-fold process that allows entire robots to be fabricated within a few hours, and I will demonstrate how foldable modules can be composed together to create foldable mechanisms and robots. The modules are represented parametrically, enabling a small set of mod-ules to describe a wide range of geometries and also allowing geometries to be optimized in a straightforward manner. I will also introduce a tool that we have developed that combines this composition approach with simulations to help human designers of all skill levels to design and fabricate custom functional robots. Cynthia Sung is a Ph.D. candidate in the Computer Science and Artificial Intelli-gence Laboratory at the Massachusetts Institute of Technology (MIT). She received a B.S. in Mechanical Engineering from Rice University in 2011 and an M.S. in Elec-trical Engineering and Computer Science from MIT in 2013. Her research interests include computational design, folding theory, and rapid fabrication, and her cur-rent work focuses on algorithms for synthesis and analysis of engineering designs.

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Colloquium Speaker Anirudha Majumdar today, 3:00pm Bowen 222
by Nicole E. Wagenblast 16 Feb '16

16 Feb '16

Anirudha Majumdar Control of agile robots in complex environments with formal safety guarantees Tuesday, February 16th ( Today ) 3:00 PM Bowen Hall Rm 222 The goal of my research is to develop algorithmic and theoretical techniques that push highly agile robotic systems to the brink of their hardware limits while guaranteeing that they operate in a safe manner despite uncertainty in the environment and dynamics.In this talk, I will describe my work on algorithms for the synthesis of feedback controllers that come with associated formal guarantees on the stability of the robot and show how these control-lers and certificates of stability can be used for robust planning in environments previously un-seen by the system. In order to make these results possible, my work connects deeply to compu-tational tools such as sums-of-squares (SOS) programming and semidefinite programming from the theory of mathematical optimization, along with approaches from nonlinear control theory.I will describe this work in the context of the problem of high-speed unmanned aerial vehicle (UAV) flight through cluttered environments previously unseen by the robot. In this context, the tools I have developed allow us to guarantee that the robot will fly through its environment in a collision-free manner despite uncertainty in the dynamics (e.g., wind gusts or modeling errors). The resulting hardware demonstrations on a fixed-wing airplane constitute one of the first exam-ples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in realtime in environments with complex geometric constraints. Anirudha Majumdar is a Ph.D. candidate in the Electrical Engineering and Computer Science de-partment at MIT. He is a member of the Robot Locomotion Group at the Computer Science and Artificial Intelligence Lab and is advised by Prof. Russ Tedrake. Ani received his undergraduate degree in Mechanical Engineering and Mathematics from the University of Pennsylvania, where he was a member of the GRASP lab. His research is primarily in robotics: he works on algorithms for controlling highly dynamics robots such as unmanned aerial vehicles with formal guarantees on the safety of the system. Ani's research has been recognized by the Siebel Foundation Scholar-ship and the Best Conference Paper Award at the International Conference on Robotics and Auto-mation (ICRA) 2013.

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Anirudha Majumdar talk TODAY
by Mitra Kelly 16 Feb '16

16 Feb '16

Anirudha Majumdar Control of agile robots in complex environments with formal safety guarantees Tuesday, February 16th (Today) 3:00 PM Bowen Hall Rm 222 The goal of my research is to develop algorithmic and theoretical techniques that push highly agile robotic systems to the brink of their hardware limits while guaranteeing that they operate in a safe manner despite uncertainty in the environment and dynamics. In this talk, I will describe my work on algorithms for the synthesis of feedback controllers that come with associated formal guarantees on the stability of the robot and show how these control-lers and certificates of stability can be used for robust planning in environments previously un-seen by the system. In order to make these results possible, my work connects deeply to compu-tational tools such as sums-of-squares (SOS) programming and semidefinite programming from the theory of mathematical optimization, along with approaches from nonlinear control theory. I will describe this work in the context of the problem of high-speed unmanned aerial vehicle (UAV) flight through cluttered environments previously unseen by the robot. In this context, the tools I have developed allow us to guarantee that the robot will fly through its environment in a collision-free manner despite uncertainty in the dynamics (e.g., wind gusts or modeling errors). The resulting hardware demonstrations on a fixed-wing airplane constitute one of the first exam-ples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in realtime in environments with complex geometric constraints. Anirudha Majumdar is a Ph.D. candidate in the Electrical Engineering and Computer Science de-partment at MIT. He is a member of the Robot Locomotion Group at the Computer Science and Artificial Intelligence Lab and is advised by Prof. Russ Tedrake. Ani received his undergraduate degree in Mechanical Engineering and Mathematics from the University of Pennsylvania, where he was a member of the GRASP lab. His research is primarily in robotics: he works on algorithms for controlling highly dynamics robots such as unmanned aerial vehicles with formal guarantees on the safety of the system. Ani's research has been recognized by the Siebel Foundation Scholar-ship and the Best Conference Paper Award at the International Conference on Robotics and Auto-mation (ICRA) 2013. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu 609-258-4562

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Colloquium Speaker David Chu, Monday, Feb 15, 12:30pm
by Mitra Kelly 15 Feb '16

15 Feb '16

Colloquium Speaker David Chu, MSR Monday, Feb 15th, 12:30pm Room CS 105 Title: In Pursuit of Low-Latency Interactions on Mobile Devices Abstract: Human attention is a scarce resource, but when available, it can also be wonderfully perceptive. My research seeks to understand: what does it take for mobile devices --- power-constrained as they are --- to operate at the speed of human perception? And what new opportunities emerge as a result? Via a pair of vignettes, I illustrate two such low-latency mobile systems. The first focuses on app streaming, an emerging app execution model in which remote servers execute logic and rendering on behalf of thin clients. App streaming promises any device access to any app at any time. Unfortunately, the reality is that wide-area network latencies often exceed thresholds above which many interactive apps such as games tend to be deemed too slow. In response, I describe Outatime, a speculative execution system for app streaming that masks network latency. In Outatime, the server renders speculative frames of future possible outcomes, delivering them to the client one entire roundtrip early, and recovers quickly from mis-speculations when they occur. Clients perceive little latency. Outatime has been implemented on two high-quality, commercially-released twitch-based games. Users report strongly preferring Outatime to standard streaming, since Outatime delivers real-time interactivity as fast as --- and in some cases, even faster than --- traditional local client-side execution. In a second example of low-latency interaction, I describe a Kinect-like device tracking system, FAR. Unlike Kinect, FAR is portable, requiring only the phones in our hands. Yet FAR performs continuous, fast and accurate phone-to-phone localization that matches the (often very fast) speed and sensitivity of human movement. In fact, FAR's accuracy is comparable to --- and in some cases, even superior to --- that of Kinect. Lab trials and many real world deployments indicate that FAR can fully support dynamic human motion in real-time. Bio: David Chu is a researcher in Microsoft's Mobility and Networking Research Group. His research interests are in mobile systems and applications, cyber-physical systems, sensing systems, ubiquitous computing and applied machine learning. The main thrust of David's current work is toward low-latency perception-aligned mobile systems. He received the Best Paper award in MobiSys 2015, the Best Paper nomination in MobiSys 2012, the Best Demo award in MobiSys 2014, and the Best Demo nomination in SenSys 2011. David's research has appeared on multiple occasions in tech news such as TechCrunch, PC Magazine, GameSpot, Ars Technia, Slashdot, The Verge, Engadget and Wired. At Microsoft, David has contributed to Windows and Windows Phone, Xbox and HoloLens. David received his B.S. from the University of Virginia in 2004; and his M.S. and Ph.D. from the University of California, Berkeley in 2005 and 2009, respectively, while an NSF Graduate Research Fellow. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu 609-258-4562

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