- talks - lists.cs.princeton.edu

Colloquium Speaker, Matt Weinberg Today, 12:30pm
by Nicole E. Wagenblast 31 Mar '16

31 Mar '16

Colloquium Speaker Matt Weinberg, Princeton University Thursday, March 31, 2016 - 12:30pm Computer Science 105 Algorithms for Strategic Agents When real people interact with algorithms (e.g. in auctions, crowdsourcing, Bitcoin, etc.), they impose additional objectives beyond simply that the algorithm is correct, fast, and uses little storage. People strategize during these interactions, so algorithms deployed in these settings must be robust against potential manipulation. Additionally, people prefer transparent interactions, so these algorithms must also be as simple as possible. My research addresses these, and other novel challenges that arise when algorithms interact with strategic agents. In this talk, I will focus on one aspect of this agenda and present a new algorithmic framework to solve any optimization problem in a way that is robust to strategic manipulation. I will further apply this framework to extend Myerson's celebrated characterization of optimal single-item auctions to multiple items (Myerson 1981), design mechanisms for job scheduling (Nisan and Ronen 1999), and resolve other problems at the interface of algorithms and game theory. Finally, I will briefly show how strategic considerations motivate nice questions in "traditional" areas of algorithm design as well, and present some of my work in online algorithms, convex optimization, and parallel algorithms. Matt received his PhD in EECS from MIT in 2014, where he was advised by Costis Daskalakis. He is now a postdoc at Princeton University in the Computer Science department. His research focuses on designing algorithms that address constraints imposed by the strategic nature of people that interact with them. For his thesis work on this topic, he received MIT's George M. Sprowls award and the SIGecom Doctoral Dissertation award. Matt received his B.A. in Math from Cornell University.

1 0

Colloquium Speaker, Matt Weinberg Thursday, March 31, 12:30pm- reminder
by Nicole E. Wagenblast 30 Mar '16

30 Mar '16

Colloquium Speaker Matt Weinberg, Princeton University Thursday, March 31, 2016 - 12:30pm Computer Science 105 Algorithms for Strategic Agents When real people interact with algorithms (e.g. in auctions, crowdsourcing, Bitcoin, etc.), they impose additional objectives beyond simply that the algorithm is correct, fast, and uses little storage. People strategize during these interactions, so algorithms deployed in these settings must be robust against potential manipulation. Additionally, people prefer transparent interactions, so these algorithms must also be as simple as possible. My research addresses these, and other novel challenges that arise when algorithms interact with strategic agents. In this talk, I will focus on one aspect of this agenda and present a new algorithmic framework to solve any optimization problem in a way that is robust to strategic manipulation. I will further apply this framework to extend Myerson's celebrated characterization of optimal single-item auctions to multiple items (Myerson 1981), design mechanisms for job scheduling (Nisan and Ronen 1999), and resolve other problems at the interface of algorithms and game theory. Finally, I will briefly show how strategic considerations motivate nice questions in "traditional" areas of algorithm design as well, and present some of my work in online algorithms, convex optimization, and parallel algorithms. Matt received his PhD in EECS from MIT in 2014, where he was advised by Costis Daskalakis. He is now a postdoc at Princeton University in the Computer Science department. His research focuses on designing algorithms that address constraints imposed by the strategic nature of people that interact with them. For his thesis work on this topic, he received MIT's George M. Sprowls award and the SIGecom Doctoral Dissertation award. Matt received his B.A. in Math from Cornell University.

1 0

Colloquium Speaker: Joel Tropp, Today- 12:30pm
by Nicole E. Wagenblast 29 Mar '16

29 Mar '16

Colloquium Speaker Joel Tropp, California Institute of Technology Tuesday, March 29, 12:30pm Computer Science 105 Finding structure with randomness Computer scientists have long known that randomness can be used to improve the performance of algorithms. A familiar application is the process of dimension reduction, in which a random map transports data from a high-dimensional space to a lower-dimensional space while approximately preserving some geometric properties. By operating with the compact representation of the data, it is possible to produce approximate solutions to certain large problems very efficiently. It has been observed that dimension reduction has powerful applications in numerical linear algebra and numerical analysis. This talk will discuss randomized techniques for constructing standard matrix factorizations, such as the truncated singular value decomposition. In practice, the algorithms are so effective that they compete with—or even outperform—classical algorithms. These methods are already making a significant impact in large-scale scientific computing and learning systems. Joint work with P.-G. Martinsson and N. Halko.

1 0

Colloquium Speaker Stephanie Mueller, TODAY at 12:30pm. March 28th
by Mitra D. Kelly 28 Mar '16

28 Mar '16

Colloquium Speaker Stephanie Mueller, Human-Computer Interaction Lab at Hasso Plattner Institute March 28, 12:30pm Computer Science 105 Talk Title: Interacting with Personal Fabrication Machines In anticipation of 3D printers reaching millions of users, I am investigating how to allow future users to interact with the new hardware. I present a series of interactive software+hardware systems that I created to answer this question. They are characterized by two main properties. First, they produce physical output quickly, allowing users not only to see their results, but also to touch and test their mechanical properties as users work towards a solution. Second, the systems allow users to interact directly with the workpiece, i.e., rather than using a digital 3D editor, users manipulate the workpiece located inside the 3D printer by pointing at it, causing the machine to then modify the workpiece accordingly. I put my research into perspective by drawing analogies to the evolution of interactive computing from batch processing, to turn taking, to direct manipulation. Bio: Stefanie Mueller is a PhD student working with Patrick Baudisch at the Human-Computer Interaction Lab at Hasso Plattner Institute. In her research, she develops novel hardware and software systems that advance personal fabrication technologies. Stefanie has published 10 papers at the most selective HCI venues CHI and UIST, for which she received a best paper award and two best paper nominees. She is also serving on the CHI and UIST program committees as an associate chair. In addition, Stefanie has been an invited speaker at universities and research labs, such as MIT, Stanford, UC Berkeley, Microsoft Research, Disney Research, and Adobe Research. 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

1 0

Topic Change: Colloquium Speaker: Joel Tropp, Tuesday, March 29- 12:30pm
by Nicole E. Wagenblast 28 Mar '16

28 Mar '16

Colloquium Speaker Joel Tropp, California Institute of Technology Tuesday, March 29, 12:30pm Computer Science 105 Finding structure with randomness Computer scientists have long known that randomness can be used to improve the performance of algorithms. A familiar application is the process of dimension reduction, in which a random map transports data from a high-dimensional space to a lower-dimensional space while approximately preserving some geometric properties. By operating with the compact representation of the data, it is possible to produce approximate solutions to certain large problems very efficiently. It has been observed that dimension reduction has powerful applications in numerical linear algebra and numerical analysis. This talk will discuss randomized techniques for constructing standard matrix factorizations, such as the truncated singular value decomposition. In practice, the algorithms are so effective that they compete with—or even outperform—classical algorithms. These methods are already making a significant impact in large-scale scientific computing and learning systems. Joint work with P.-G. Martinsson and N. Halko.

1 0

Colloquium Speaker: Joel Tropp, Tuesday, March 29- 12:30pm- reminder
by Nicole E. Wagenblast 28 Mar '16

28 Mar '16

Colloquium Speaker Joel Tropp, California Institute of Technology Tuesday, March 29, 12:30pm Computer Science 105 Universality laws for randomized dimension reduction Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space to facilitate its analysis. In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data. The question is how large the embedding dimension must be to ensure that randomized dimension reduction succeeds with high probability. This talk describes a phase transition in the behavior of the dimension reduction map as the embedding dimension increases. The location of this phase transition is universal for a large class of datasets and random dimension reduction maps. Furthermore, the stability properties of randomized dimension reduction are also universal. These results have many applications in numerical analysis, signal processing, and statistics. Joint work with Samet Oymak.

1 0

Colloquium Speaker, Matt Weinberg Thursday, March 31, 12:30pm
by Nicole E. Wagenblast 25 Mar '16

25 Mar '16

Colloquium Speaker Matt Weinberg, Princeton University Thursday, March 31, 2016 - 12:30pm Computer Science 105 Algorithms for Strategic Agents When real people interact with algorithms (e.g. in auctions, crowdsourcing, Bitcoin, etc.), they impose additional objectives beyond simply that the algorithm is correct, fast, and uses little storage. People strategize during these interactions, so algorithms deployed in these settings must be robust against potential manipulation. Additionally, people prefer transparent interactions, so these algorithms must also be as simple as possible. My research addresses these, and other novel challenges that arise when algorithms interact with strategic agents. In this talk, I will focus on one aspect of this agenda and present a new algorithmic framework to solve any optimization problem in a way that is robust to strategic manipulation. I will further apply this framework to extend Myerson's celebrated characterization of optimal single-item auctions to multiple items (Myerson 1981), design mechanisms for job scheduling (Nisan and Ronen 1999), and resolve other problems at the interface of algorithms and game theory. Finally, I will briefly show how strategic considerations motivate nice questions in "traditional" areas of algorithm design as well, and present some of my work in online algorithms, convex optimization, and parallel algorithms. Matt received his PhD in EECS from MIT in 2014, where he was advised by Costis Daskalakis. He is now a postdoc at Princeton University in the Computer Science department. His research focuses on designing algorithms that address constraints imposed by the strategic nature of people that interact with them. For his thesis work on this topic, he received MIT's George M. Sprowls award and the SIGecom Doctoral Dissertation award. Matt received his B.A. in Math from Cornell University.

1 0

Colloquium Speaker: Mayank Goel Today 12:30pm
by Nicole E. Wagenblast 25 Mar '16

25 Mar '16

Colloquium Speaker Mayank Goel , University of Washington Friday, March 25, 2016 - 12:30pm Computer Science 105 Teaching Old Sensors New Tricks A fundamental issue with any new sensing technology is the deployment burden. The new technology often comes with a rigid and perhaps expensive list of pre-requisites; and it affects the overall cost-effectiveness, deployability, and accessibility of the solution. For example, a user might find it hard to buy and carry a medical device with them everyday. The primary theme of my Ph.D. research has been to reduce the adoption and deployment barriers of new sensing technologies by using the sensors that are already around us. In this talk, I will present my research on extending the capabilities of the on-board sensors on consumer devices for various applications. First, I will discuss how we can leverage the on-device sensors to enable novel human-computer interactions and make the mobile devices more usable. Next, I will discuss my work on using the on-device sensors for building health technologies that work as well as their clinically-approved counterparts and they continue to evolve through deployments in various parts of the world. Finally, I will discuss how my recent work in extending the on-device sensors is shaping my future interests and how I plan to continue to lower the adoption barriers for new sensing technologies. Mayank Goel is a Ph.D. candidate in Computer Science and Engineering in the Ubiquitous Computing (UbiComp) Lab at the University of Washington, advised by Shwetak Patel and Gaetano Borriello. His research focuses on building sensing systems to solve hard problems in diverse application areas, ranging from health sensing, technologies for the developing world to novel user interaction; with an eye toward reducing deployment barriers. His research in the area of health technologies is currently used by hundreds of patients every week, around the world. He was awarded the Microsoft Research Ph.D. Fellowship in 2014, a University of Washington CSE Fellowship, and 7 Best Paper Nominations. He received an M.S. in Computer Science from Georgia Institute of Technology in 2009, where he was advised by Gregory Abowd and specialized in mobile and ubiquitous computing.

1 0

Colloquium Speaker: Joel Tropp, Tuesday, March 29- 12:30pm
by Nicole E. Wagenblast 24 Mar '16

24 Mar '16

Colloquium Speaker Joel Tropp, California Institute of Technology Tuesday, March 29, 12:30pm Computer Science 105 Universality laws for randomized dimension reduction Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space to facilitate its analysis. In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data. The question is how large the embedding dimension must be to ensure that randomized dimension reduction succeeds with high probability. This talk describes a phase transition in the behavior of the dimension reduction map as the embedding dimension increases. The location of this phase transition is universal for a large class of datasets and random dimension reduction maps. Furthermore, the stability properties of randomized dimension reduction are also universal. These results have many applications in numerical analysis, signal processing, and statistics. Joint work with Samet Oymak.

1 0

Colloquium Speaker Stefanie Mueller, Monday March 28th, 12:30pm
by Mitra D. Kelly 24 Mar '16

24 Mar '16

Colloquium Speaker Stefanie Mueller, Human-Computer Interaction Lab at Hasso Plattner Institute Monday March 28th, 12:30pm Computer Science 105 Talk Title: Interacting with Personal Fabrication Machines Abstract: In anticipation of 3D printers reaching millions of users, I am investigating how to allow future users to interact with the new hardware. I present a series of interactive software+hardware systems that I created to answer this question. They are characterized by two main properties. First, they produce physical output quickly, allowing users not only to see their results, but also to touch and test their mechanical properties as users work towards a solution. Second, the systems allow users to interact directly with the workpiece, i.e., rather than using a digital 3D editor, users manipulate the workpiece located inside the 3D printer by pointing at it, causing the machine to then modify the workpiece accordingly. I put my research into perspective by drawing analogies to the evolution of interactive computing from batch processing, to turn taking, to direct manipulation. Bio: Stefanie Mueller is a PhD student working with Patrick Baudisch at the Human-Computer Interaction Lab at Hasso Plattner Institute. In her research, she develops novel hardware and software systems that advance personal fabrication technologies. Stefanie has published 10 papers at the most selective HCI venues CHI and UIST, for which she received a best paper award and two best paper nominees. She is also serving on the CHI and UIST program committees as an associate chair. In addition, Stefanie has been an invited speaker at universities and research labs, such as MIT, Stanford, UC Berkeley, Microsoft Research, Disney Research, and Adobe Research. 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

1 0