- talks - lists.cs.princeton.edu

Colloquium Speaker Nikolay Atanasov, Friday Feb 26, 3:30pm
by Nicole E. Wagenblast 25 Feb '16

25 Feb '16

Colloquium Speaker Dr. Nikolay Atanasov Acquiring Metric and Semantic Information using Autonomous Robots Friday, February 26th at 3:30 PM Maeder Hall, ACEE Recent years have seen impressive progress in robot control and perception including adept manipulation, aggressive quadrotor maneuvers, dense metric map reconstruction, and object recognition in real time. The grand challenge in robotics today is to capitalize on these advances in order to enable autonomy at a higher-level of intelligence. It is compelling to envision teams of autonomous robots in environmental monitoring, precision agriculture, construction and structure inspection, security and surveillance, and search and rescue. In this talk, I will emphasize that many such applications can be addressed by thinking about how to coordinate robots in order to extract useful information about the environment. More precisely, I will formulate a general active estimation problem that captures the common characteristics of the aforementioned scenarios. I will show how to manage the complexity of the problem over metric information spaces with respect to long planning horizons and large robot teams. These results lead to computationally scalable, non-myopic algorithms with quantified performance for problems such as distributed source seeking and active simultaneous localization and mapping (SLAM). I will then focus on acquiring information using both metric and semantic observations (e.g., object recognition). In this context, there are several new challenges such as missed detections, false alarms, and unknown data association. To address them, I will model semantic observations via random sets and will discuss filtering using such models. A major contribution of our approach is in proving that the complexity of the problem is equivalent to computing the permanent of a suitable matrix. This enables us to develop and experimentally validate algorithms for semantic localization, mapping, and planning on mobile robots, Google's project Tango phone, and the KITTI visual odometry dataset.

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Colloquium Speaker Bryan Parno Friday Feb 26, 12:30pm- reminder
by Nicole E. Wagenblast 25 Feb '16

25 Feb '16

Colloquium Speaker Bryan Parno, Microsoft Research Friday, February 26, 12:30pm Computer Science 105 Fully Verified Outsourced Computation Frequent headline-grabbing data breaches suggest that current best practices for safeguarding personal data are woefully inadequate. To try to move beyond the cycle of attacks and patches we see today, I design and build systems with formal end-to-end guarantees. For example, to provide strong guarantees for outsourced computations, I developed a new cryptographic framework, verifiable computation, which allows clients to outsource general computations to completely untrusted services and efficiently verify the correctness of each returned result. Through improvements to the theory and the underlying systems, we reduced the costs of verification by over twenty orders of magnitude. As a result, verifiable computation is now a thriving research area that has produced several startups, as well as enhancements to the security and privacy of X.509, MapReduce, and Bitcoin. While verifiable computation provides strong guarantees, even the best cryptographic system is useless if implemented badly, applied incorrectly, or used in a vulnerable system. Thus, I have led a team of researchers and engineers in the Ironclad project, working to expand formal software verification to provide end-to-end guarantees about the security and reliability of complex systems. By creating a set of new tools and methodologies, Ironclad produced the first complete stack of verified-secure software. We also recently developed the first methodology for verifying both the safety and liveness of complex distributed systems implementations. While interesting challenges remain, I expect that verification will fundamentally improve the software that underpins our digital and physical infrastructure. Bryan Parno is a Researcher in the Security and Privacy Group at Microsoft Research. After receiving a Bachelor's degree from Harvard College, he completed his PhD at Carnegie Mellon University, where his dissertation won the 2010 ACM Doctoral Dissertation Award. In 2011, he was selected for Forbes' 30-Under-30 Science List. He formalized and worked to optimize verifiable computation, receiving a Best Paper Award at the IEEE Symposium on Security and Privacy his advances. He coauthored a book on Bootstrapping Trust in Modern Computers, and his work in that area has been incorporated into the latest security enhancements in Intel CPUs. His research into security for new application models was incorporated into Windows and received a Best Paper Awards at the IEEE Symposium on Security and Privacy and the USENIX Symposium on Networked Systems Design and Implementation. He has recently extended his interest in bootstrapping trust to the problem of building practical, formally verified secure systems. His other research interests include user authentication, secure network protocols, and security in constrained environments (e.g., RFID tags, sensor networks, or vehicles).

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Colloquium Speaker: Leonidas Guibas, Today, 12:30pm- reminder
by Nicole E. Wagenblast 25 Feb '16

25 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. _______________________________________________ talks mailing list talks(a)lists.cs.princeton.edu To edit subscription settings or remove yourself, use this link: https://lists.cs.princeton.edu/mailman/listinfo/talks

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Colloquium Speaker: Leonidas Guibas, Thursday, Feburary 25, 12:30pm- Reminder
by Nicole E. Wagenblast 24 Feb '16

24 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-Greg Durrett, Tuesday, March 1, 12:30PM
by Mitra D. Kelly 23 Feb '16

23 Feb '16

Colloquium Speaker Greg Durrett, UC Berkeley Tuesday, March 1st, 12:30PM Cs 105 Data-Driven Text Analysis with Joint Models One reason that analyzing text is hard is that it involves dealing with deeply entangled linguistic variables: objects like syntactic structures, semantic types, and discourse relations depend on one another in complex ways. Our work tackles several facets of text analysis using joint modeling, combining model components both across and within the various subtasks of this analysis. This model structure allows us to pass information between these entangled subtasks and propagate high-confidence predictions rather than errors. Critically, our models have the capacity to learn key linguistic phenomena as well as other important patterns in the data; that is, linguistics tells us how to structure these models, then the data injects knowledge into them. We describe state-of-the-art systems for a range of tasks, including syntactic parsing, entity resolution, and document summarization. Bio: Greg is a Ph.D. candidate at UC Berkeley working on natural language processing with Dan Klein. He is interested in building structured machine learning models for a wide variety of text analysis problems and downstream NLP applications. His work is comprised of two broad thrusts: first, designing joint models that combine information across different tasks or different views of a problem, and second, building systems that strike a balance between being linguistically motivated and data-driven Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker Nikolay Atanasov, Friday Feb 26, 3:30pm
by Nicole E. Wagenblast 23 Feb '16

23 Feb '16

Colloquium Speaker Dr. Nikolay Atanasov Acquiring Metric and Semantic Information using Autonomous Robots Friday, February 26th at 3:30 PM Maeder Hall, ACEE Recent years have seen impressive progress in robot control and perception including adept manipulation, aggressive quadrotor maneuvers, dense metric map reconstruction, and object recognition in real time. The grand challenge in robotics today is to capitalize on these advances in order to enable autonomy at a higher-level of intelligence. It is compelling to envision teams of autonomous robots in environmental monitoring, precision agriculture, construction and structure inspection, security and surveillance, and search and rescue. In this talk, I will emphasize that many such applications can be addressed by thinking about how to coordinate robots in order to extract useful information about the environment. More precisely, I will formulate a general active estimation problem that captures the common characteristics of the aforementioned scenarios. I will show how to manage the complexity of the problem over metric information spaces with respect to long planning horizons and large robot teams. These results lead to computationally scalable, non-myopic algorithms with quantified performance for problems such as distributed source seeking and active simultaneous localization and mapping (SLAM). I will then focus on acquiring information using both metric and semantic observations (e.g., object recognition). In this context, there are several new challenges such as missed detections, false alarms, and unknown data association. To address them, I will model semantic observations via random sets and will discuss filtering using such models. A major contribution of our approach is in proving that the complexity of the problem is equivalent to computing the permanent of a suitable matrix. This enables us to develop and experimentally validate algorithms for semantic localization, mapping, and planning on mobile robots, Google's project Tango phone, and the KITTI visual odometry dataset.

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Colloquium speaker Yuxin Chen, TODAY, 4:30pm
by Nicole E. Wagenblast 23 Feb '16

23 Feb '16

Colloquium Speaker Yuxin Chen, Stanford University Monday, February 29, 4:30pm Computer Science 104- large auditorium The Power of Nonconvex Paradigms for High-Dimensional Estimation In various scenarios in the information sciences, one wishes to estimate a large number of parameters from highly incomplete / imperfect data samples. A growing body of recent work has demonstrated the effectiveness of convex relaxation --- in particular, semidefinite programming --- for solving many problems of this kind. However, the computational cost of such convex paradigms is often unsatisfactory, which limits applicability to large-dimensional data. This talk follows another route: by formulating the problems into nonconvex programs, we attempt to optimize the nonconvex objectives directly. To illustrate the power of this strategy, we present two concrete stories. The first involves solving a random quadratic system of equations, which spans many applications ranging from the century-old phase retrieval problem to various latent-variable models in machine learning. The second is about recovering a collection of discrete variables from noisy pairwise difference measurements, which arises when one wishes to jointly align multiple images or to retrieve the genome phases from paired sequencing reads. We propose novel nonconvex paradigms for solving these two problems. In both cases, the proposed solutions can be accomplished within linear time, while achieving a statistical accuracy that is nearly un-improvable. Yuxin Chen is currently a postdoctoral scholar in the Department of Statistics at Stanford University, supervised by Prof. Emmanuel Candès. He received the Ph.D. degree in Electrical Engineering and M.S. in Statistics from Stanford University, M.S. in Electrical and Computer Engineering from the University of Texas at Austin, and B.E. in microelectronics from Tsinghua University. His research interests include high-dimensional structured estimation, convex and nonconvex optimization, statistical learning, and information theory.

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Colloquium speaker Yuxin Chen, Monday Feb 29, 4:30pm
by Nicole E. Wagenblast 23 Feb '16

23 Feb '16

Colloquium Speaker Yuxin Chen, Stanford University Monday, February 29, 4:30pm Computer Science 104- large auditorium The Power of Nonconvex Paradigms for High-Dimensional Estimation In various scenarios in the information sciences, one wishes to estimate a large number of parameters from highly incomplete / imperfect data samples. A growing body of recent work has demonstrated the effectiveness of convex relaxation --- in particular, semidefinite programming --- for solving many problems of this kind. However, the computational cost of such convex paradigms is often unsatisfactory, which limits applicability to large-dimensional data. This talk follows another route: by formulating the problems into nonconvex programs, we attempt to optimize the nonconvex objectives directly. To illustrate the power of this strategy, we present two concrete stories. The first involves solving a random quadratic system of equations, which spans many applications ranging from the century-old phase retrieval problem to various latent-variable models in machine learning. The second is about recovering a collection of discrete variables from noisy pairwise difference measurements, which arises when one wishes to jointly align multiple images or to retrieve the genome phases from paired sequencing reads. We propose novel nonconvex paradigms for solving these two problems. In both cases, the proposed solutions can be accomplished within linear time, while achieving a statistical accuracy that is nearly un-improvable. Yuxin Chen is currently a postdoctoral scholar in the Department of Statistics at Stanford University, supervised by Prof. Emmanuel Candès. He received the Ph.D. degree in Electrical Engineering and M.S. in Statistics from Stanford University, M.S. in Electrical and Computer Engineering from the University of Texas at Austin, and B.E. in microelectronics from Tsinghua University. His research interests include high-dimensional structured estimation, convex and nonconvex optimization, statistical learning, and information theory.

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colloquium, Alp Kucukelbir , today Feb 23rd at 12:30pm
by Mitra D. Kelly 23 Feb '16

23 Feb '16

Colloquium Alp Kucukelbir, Columbia Feb 23rd @12:30pm Cs 105 # Talk Title Towards Automated Machine Learning # Talk Abstract Machine learning is changing the way we do science. We want to study large datasets to shed light onto natural processes. (How do proteins function? How does the brain work?) To this end, we need tools to rapidly and iteratively explore hidden patterns in data. However, using machine learning to discover such patterns requires enormous effort and cross-disciplinary expertise. My goal is to develop easy-to-use machine learning tools that empower scientists to gain new insights from data. This requires research in automated algorithms, scalable software, and robust machine learning. These are the building blocks of effective machine learning tools. # Bio Alp is a postdoctoral research scientist at the Data Science Institute and the department of Computer Science at Columbia University. He works with David Blei on developing scalable and robust machine learning tools. He collaborates with Andrew Gelman on the Stan probabilistic programming system. Alp received his Ph.D. from Yale University, where he was awarded the Becton prize for best thesis in engineering and applied science. He holds a B.A.Sc. from the University of Toronto. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Colloquium Speaker -Alp Kucukelbir, Columbia University, Tuesday, Feb 23
by Mitra D. Kelly 22 Feb '16

22 Feb '16

Colloquium Speaker Alp Kucukelbir, Columbia University Tuesday, Feb 23 at 12:30 CS 105 # Talk Title Towards Automated Machine Learning # Talk Abstract Machine learning is changing the way we do science. We want to study large datasets to shed light onto natural processes. (How do proteins function? How does the brain work?) To this end, we need tools to rapidly and iteratively explore hidden patterns in data. However, using machine learning to discover such patterns requires enormous effort and cross-disciplinary expertise. My goal is to develop easy-to-use machine learning tools that empower scientists to gain new insights from data. This requires research in automated algorithms, scalable software, and robust machine learning. These are the building blocks of effective machine learning tools. # Bio Alp is a postdoctoral research scientist at the Data Science Institute and the department of Computer Science at Columbia University. He works with David Blei on developing scalable and robust machine learning tools. He collaborates with Andrew Gelman on the Stan probabilistic programming system. Alp received his Ph.D. from Yale University, where he was awarded the Becton prize for best thesis in engineering and applied science. He holds a B.A.Sc. from the University of Toronto. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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