- talks - lists.cs.princeton.edu

Colloquium Speaker Morgan Dixon, Wed Feb 18, 12:30pm
by Nicole E. Wagenblast 12 Feb '15

12 Feb '15

Colloquium Speaker Morgan Dixon, University of Washington Wednesday, February 18, 12:30pm Computer Science 105 Unlocking Interaction via Pixel-Based Reverse Engineering User interface software has become a victim of its own success. Graphical event-driven systems are the dominant user interface for computational devices, realized via popular user interface toolkits. However, this success has induced a lock-in effect that demonstrably stifles innovation. For example, human-computer interaction researchers regularly propose new interaction techniques for desktop and mobile devices, enhancements for people with disabilities, better support for localization, and new collaboration facilities. However, these techniques rarely make it outside the laboratory because existing software tools cannot support them. Changing the ecosystem would require a mass adoption of new tools, and most of the applications we use today would need to be rewritten. I will describe an approach that unlocks existing graphical application without their source code and without their cooperation, making it possible to explore and deploy many promising ideas in the context of existing real-world interfaces. Prefab is a system that reverse-engineers graphical interfaces from raw pixels. I will show how Prefab can be used to augment user interfaces with a variety of techniques, including accessibility enhancements, improved input for touch screens, UI language translation, and end-user customization. Some of these enhancements were proposed in the literature over the last two decades, but prior to Prefab they had been considered nearly impossible to deploy in practice. Morgan Dixon is a PhD student in Computer Science & Engineering at the University of Washington working with James Fogarty. His research is centered around designing, building, and studying systems that make user interface software more flexible and extensible. He has also worked on novel interaction techniques for accessibility, dual-display mobile devices, health technologies, crossing-based interfaces, and data analysis tools. Morgan received a B.S. in Computer Science and Mathematics from the University of Maryland in 2008, and an M.S. from the University of Washington in 2010. His work on Prefab received a CHI Best Paper Award, and he was named a Microsoft Research PhD Fellow in 2011.

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Fwd: EE Department Seminar - Guy Bresler, 2/19
by Jennifer Rexford 12 Feb '15

12 Feb '15

> > Speaker: > Dr. Guy Bresler > Title: > Learning statistical structure in data: efficient estimation of graphical models > Day: > Thursday, February 19, 2015 > > > Time: > 4:30 pm > Room: > B205, E-Quad > Host: > Prof. Emmanuel Abbe > > > > > > > > > Abstract: > Graphical models are a powerful framework used to succinctly represent high-dimensional distributions, and are at the core of modern large-scale statistical inference. The graph underlying such a distribution specifies interactions between the variables, and explicitly captures the computational aspect inherent to statistical tasks. For unstructured settings such as those found in social networks, biology, and finance, a central problem is to determine a good model from observed data. > > Learning graphical models from high-dimensional data is computationally challenging, and many different algorithms have been proposed over the years. Nevertheless, it is not clear what classes of models can be learned efficiently. In this talk we explore the problem from several angles and obtain a relatively unified view of what determines the computational complexity of learning graphical models. > > > Bio: > Guy Bresler is currently a postdoctoral associate at the Laboratory for Information and Decision Systems at MIT. His research interests are at the interface of computation, statistics, and information theory. He received his Ph.D. from the Dept. of Electrical Engineering and Computer Sciences at UC Berkeley, and his B.S. in electrical and computer engineering and M.S. in mathematics from the Univ. of Illinois at Urbana-Champaign. He has received several fellowships and awards including the NSF graduate research fellowship and a Vodafone Foundation fellowship. > >

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Colloquium Speaker Peter Bailis, Today, 12:30pm
by Nicole E. Wagenblast 12 Feb '15

12 Feb '15

Colloquium Speaker Peter Bailis, UC Berkeley Thursday February 12, 12:30pm Computer Science 105 Coordination Avoidance in Distributed Databases The rise of Internet-scale geo-replicated services has led to considerable upheaval in the design of modern data management systems. Namely, given the availability, latency, and throughput penalties associated with classic mechanisms such as serializable transactions, a broad class of systems (e.g., "NoSQL") has sought weaker alternatives that reduce the use of expensive coordination during system operation, often at the cost of application integrity. When can we safely forego the cost of this expensive coordination, and when must we pay the price? In this talk, I will discuss the potential for coordination avoidance -- the use of as little coordination as possible while still ensuring application integrity -- in several modern data-intensive domains. Specifically, I will demonstrate how to leverage the semantic requirements of applications in data serving, transaction processing, and statistical analytics to enable more efficient distributed algorithms and system designs. The prototype systems I have built demonstrate order-of-magnitude speedups compared to their traditional, coordinated counterparts on a variety of tasks, including referential integrity and index maintenance, transaction execution under common isolation models, and asynchronous convex optimization. I will also discuss our experiences studying and optimizing a range of open source applications and systems, which exhibit similar results. Peter Bailis is a Ph.D. candidate at UC Berkeley working in databases and distributed systems. As part of his dissertation work, he has studied and built high performance distributed data management systems for large scale transaction processing, data serving, and statistical analytics in the AMPLab and BOOM projects under the advisement of Joseph M. Hellerstein, Ali Ghodsi, and Ion Stoica. He is the recipient of the NSF Graduate Research Fellowship, the Berkeley Fellowship for Graduate Study, and best-of-conference citations for research appearing in both SIGMOD and VLDB. He received his A.B. in Computer Science from Harvard College in 2011, where he also received the CRA Outstanding Undergraduate Researcher Award.

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Colloquium Speaker Walter Lasecki Tues Feb 17, 12:30pm
by Nicole E. Wagenblast 11 Feb '15

11 Feb '15

Colloquium Speaker Walter S. Lasecki , University of Rochester Tuesday, February 17, 2015 - 12:30pm Computer Science 105 Crowd-Agents: Creating Crowd-Powered Interactive Systems I create and deploy interactive systems that use a combination of human and machine intelligence to operate robustly in real-world settings. Unlike prior work in human computation, my “Crowd Agent” model allows crowds of people to support continuous real-time interactive systems that require ongoing context. For example, Chorus allows multi-session conversations with a virtual personal assistant; Scribe allows non-experts to caption speech in real time for deaf and hard of hearing users, where prior approaches were either not accurate enough, or required professionals with years of training; and Apparition allows designers to rapidly prototype new interactive interfaces from sketches in real time. In this talk, I will describe how computationally-mediated groups of people can solve problems that neither people nor computers can solve alone, and scaffold AI systems using the real-world data they collect. Walter S. Lasecki is a Computer Science Ph.D. candidate at the University of Rochester working with Jeffrey Bigham (CMU) and James Allen (Rochester). He creates interactive intelligent systems that are robust enough to be used in real-world settings by combining both human and machine intelligence to exceed the capabilities of either. Mr. Lasecki received a B.S. in Computer Science and Mathematics from Virginia Tech in 2010, and an M.S. from the University of Rochester in 2011. He was named a Microsoft Research Ph.D. Fellow in 2013, and has held visiting research positions at Stanford and Google[x]. Since 2013, he has been a Visiting Researcher at Carnegie Mellon University.

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Colloquium Speaker Peter Bailis, Thurs Feb 12, 12:30pm
by Nicole E. Wagenblast 11 Feb '15

11 Feb '15

Colloquium Speaker Peter Bailis, UC Berkeley Thursday February 12, 12:30pm Computer Science 105 Coordination Avoidance in Distributed Databases The rise of Internet-scale geo-replicated services has led to considerable upheaval in the design of modern data management systems. Namely, given the availability, latency, and throughput penalties associated with classic mechanisms such as serializable transactions, a broad class of systems (e.g., "NoSQL") has sought weaker alternatives that reduce the use of expensive coordination during system operation, often at the cost of application integrity. When can we safely forego the cost of this expensive coordination, and when must we pay the price? In this talk, I will discuss the potential for coordination avoidance -- the use of as little coordination as possible while still ensuring application integrity -- in several modern data-intensive domains. Specifically, I will demonstrate how to leverage the semantic requirements of applications in data serving, transaction processing, and statistical analytics to enable more efficient distributed algorithms and system designs. The prototype systems I have built demonstrate order-of-magnitude speedups compared to their traditional, coordinated counterparts on a variety of tasks, including referential integrity and index maintenance, transaction execution under common isolation models, and asynchronous convex optimization. I will also discuss our experiences studying and optimizing a range of open source applications and systems, which exhibit similar results. Peter Bailis is a Ph.D. candidate at UC Berkeley working in databases and distributed systems. As part of his dissertation work, he has studied and built high performance distributed data management systems for large scale transaction processing, data serving, and statistical analytics in the AMPLab and BOOM projects under the advisement of Joseph M. Hellerstein, Ali Ghodsi, and Ion Stoica. He is the recipient of the NSF Graduate Research Fellowship, the Berkeley Fellowship for Graduate Study, and best-of-conference citations for research appearing in both SIGMOD and VLDB. He received his A.B. in Computer Science from Harvard College in 2011, where he also received the CRA Outstanding Undergraduate Researcher Award.

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Colloquium Speaker Yoram Singer, Today, 4:30pm
by Nicole E. Wagenblast 09 Feb '15

09 Feb '15

Colloquium Speaker Yoram Singer, Google Research Monday, February 9, 4:30pm Computer Science 105 Learning compact models from high dimensional large datasets I review the design, analysis, and implementation of stochastic optimization techniques, online algorithms, and modeling approaches for learning in high dimensional spaces using large amounts of data. The focus is on algorithms and models that are efficient, accurate, and yield compact models. Concretely, the forward-backward shrinkage algorithm (Fobos), mirror descent for learning composite objectives (COMID), and the adaptive gradient (AdaGrad) algorithm. We also discuss simple yet effective modeling approaches based on locality for learning from high dimensional data. Yoram Singer is a senior research scientist at Google. From 1999 through 2007 he was an associate professor at the Hebrew University of Jerusalem. From 1995 through 1999 he was a member of the technical staff at AT&T Research. He was the co-chair of the conference on Computational Learning Theory (COLT) in 2004 and of Neural Information Processing Systems (NIPS) in 2007. He serves as an editor of the Journal of Machine Learning, IEEE Signal Processing Magazine, and IEEE Trans. on Pattern Analysis and Machine Intelligence.

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Colloquium Speaker Yoram Singer, Mon Feb 9, 4:30pm
by Nicole E. Wagenblast 08 Feb '15

08 Feb '15

Colloquium Speaker Yoram Singer, Google Research Monday, February 9, 4:30pm Computer Science 105 Learning compact models from high dimensional large datasets I review the design, analysis, and implementation of stochastic optimization techniques, online algorithms, and modeling approaches for learning in high dimensional spaces using large amounts of data. The focus is on algorithms and models that are efficient, accurate, and yield compact models. Concretely, the forward-backward shrinkage algorithm (Fobos), mirror descent for learning composite objectives (COMID), and the adaptive gradient (AdaGrad) algorithm. We also discuss simple yet effective modeling approaches based on locality for learning from high dimensional data. Yoram Singer is a senior research scientist at Google. From 1999 through 2007 he was an associate professor at the Hebrew University of Jerusalem. From 1995 through 1999 he was a member of the technical staff at AT&T Research. He was the co-chair of the conference on Computational Learning Theory (COLT) in 2004 and of Neural Information Processing Systems (NIPS) in 2007. He serves as an editor of the Journal of Machine Learning, IEEE Signal Processing Magazine, and IEEE Trans. on Pattern Analysis and Machine Intelligence.

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Tianqiang Liu's preFPO Feb. 10 at 10am in CS rm 301.
by Nicki Gotsis 06 Feb '15

06 Feb '15

Tianqiang Liu's will present his PreFPO on Feb. 10 at 10am in CS rm 301. The members of his committee are below: Advisor: Thomas Funkhouser Readers: Szymon Rusinkiewicz, Wilmot Li (Adobe System) Non-readers: Adam Finkelstein, Jianxiong Xiao Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Analyzing, Optimizing and Synthesizing Scenes by Reasoning About Relationships Between Objects Abstract: Large numbers of virtual 3D scenes have been widely used for various applications, such as video games, movies, and creation of product images for furniture catalogs, which leads to an enormous demand for scene modeling. On the one hand, manually modeling 3D scenes is extremely tedious, which requires selecting and placing hundreds of objects even for a single scene. On the other hand, a data-driven approach to 3D scene modeling is also challenging, due to the large variabilities in 3D scenes (e.g. object classes and object placement), and the hardness of maintaining plausibility of 3D scenes (e.g. relative positions and style compatibility between objects). In this thesis, we utilize relationships between objects as cues to address three problems in scene understanding and scene synthesis. First, we develop an algorithm to consistently segment and annotate 3D scenes in online repositories by using a probabilistic grammar learned from examples. Our grammar encodes the geometry priors of object classes and spatial relationships between objects within a semantic group. During experiments with these algorithms, we find our algorithm outperforms alternative approaches that consider only shape similarities and/or spatial relationships without considering semantic groups. Second, we develop a tool to optimize a 3D scene in order to create an aesthetically appealing composition for it. Recently, companies are creating product advertisements and catalog images using computer renderings of 3D scenes. However, this is challenging, not only due to the need to balance the trade-off among aesthetic principles and design constraints, but also because of the huge search space induced by possible camera parameters, object replacement, material choices, etc.. In this work, we identify a set of composition rules by constraining the relationships between objects in the scene space as well as the image space, and develop algorithms to optimize the 2D composition by changing the camera view, object placement and materials. The value of this tool is demonstrated in a variety of applications motivated by product catalogs. Third, we learn a style compatibility metric for furniture models and develop style-aware suggestion systems. Existing tools for selecting models for synthesizing 3D scenes generally ignore style compatibility between objects, and presence of incompatible styles diminishes the plausibility of the resultant 3D scenes. In this work, we develop a mathematical representation of style compatibility between objects that can be used to guide 3D scene modeling tools. During experiments, we find our method is effective at predicting style compatibility agreed upon by people, and we find in user studies that the learned compatibility metric is useful for novel interactive tools.

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Colloquium Speaker Peter Bailis, Thurs Feb 12, 12:30pm
by Nicole E. Wagenblast 06 Feb '15

06 Feb '15

Colloquium Speaker Peter Bailis, UC Berkeley Thursday February 12, 12:30pm Computer Science 105 Coordination Avoidance in Distributed Databases The rise of Internet-scale geo-replicated services has led to considerable upheaval in the design of modern data management systems. Namely, given the availability, latency, and throughput penalties associated with classic mechanisms such as serializable transactions, a broad class of systems (e.g., "NoSQL") has sought weaker alternatives that reduce the use of expensive coordination during system operation, often at the cost of application integrity. When can we safely forego the cost of this expensive coordination, and when must we pay the price? In this talk, I will discuss the potential for coordination avoidance -- the use of as little coordination as possible while still ensuring application integrity -- in several modern data-intensive domains. Specifically, I will demonstrate how to leverage the semantic requirements of applications in data serving, transaction processing, and statistical analytics to enable more efficient distributed algorithms and system designs. The prototype systems I have built demonstrate order-of-magnitude speedups compared to their traditional, coordinated counterparts on a variety of tasks, including referential integrity and index maintenance, transaction execution under common isolation models, and asynchronous convex optimization. I will also discuss our experiences studying and optimizing a range of open source applications and systems, which exhibit similar results. Peter Bailis is a Ph.D. candidate at UC Berkeley working in databases and distributed systems. As part of his dissertation work, he has studied and built high performance distributed data management systems for large scale transaction processing, data serving, and statistical analytics in the AMPLab and BOOM projects under the advisement of Joseph M. Hellerstein, Ali Ghodsi, and Ion Stoica. He is the recipient of the NSF Graduate Research Fellowship, the Berkeley Fellowship for Graduate Study, and best-of-conference citations for research appearing in both SIGMOD and VLDB. He received his A.B. in Computer Science from Harvard College in 2011, where he also received the CRA Outstanding Undergraduate Researcher Award.

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Colloquium Speaker Omer Reingold, today, 12:30pm
by Nicole E. Wagenblast 04 Feb '15

04 Feb '15

Colloquium Speaker Randomness Vs. Memory: A Treasure(s) Hunt Omer Reingold, Weizmann Institute of Science Wednesday, February 4, 12:30pm Computer Science 105 Randomness Vs. Memory: A Treasure(s) Hunt Randomness plays a major role in computation, allowing the execution of tasks that would otherwise be impossible or prohibitively inefficient. Yet the relative power of randomness compared with other resources of computation is mostly unresolved. In this talk we will address the tradeoff between memory and randomness. Namely, can randomness signiﬁcantly reduce the amount of memory needed for algorithmic tasks? This is one of the central problems in Computational Complexity and its study is tied with the study of pseudorandom generators that fool memory-bounded algorithms (i.e. combinatorial objects that look random to small-memory distinguishers). There are many exciting questions on the path towards resolving the randomness vs memory problem. In this talk we will focus on two such areas of research. First, we will discuss small memory algorithms for graph connectivity as well as pseudorandom walks, which we will relate to the general problem of randomness vs. memory. Then we will describe pseudorandom families of hash functions for data structures that are simultaneously small and efficient (with applications to load balancing, the two-choice paradigm, cuckoo hashing, and min-hashing). We will relate such families to progress on more traditional pseudorandom objects such as epsilon-bias sequences and functions with limited independence. Omer Reingold is a Professor of Computer Science at the Weizmann Institute of Science and a Visiting Professor at Stanford University. Past positions include Microsoft Research, the Institute for Advanced Study in Princeton, NJ, and AT&T Labs. His research is in the Foundations of Computer Science and most notably in Computational Complexity and the Foundations of Cryptography with emphasis on randomness, derandomization and explicit combinatorial constructions. He is an ACM Fellow and among his distinctions are the 2005 Grace Murray Hopper Award and the 2009 Gödel Prize.

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