WebNotice Postings for Department of Computer Science

23-Nov-2009 through 23-Nov-2010


Monday, 23 November 2009, 11:00AM -- D.L. Pratt, 290 C
Machine Learning Seminar
Speaker: Amit Gruber, Dept. Computer Science, University of Toronto
Title: "Latent Topic Models for Hypertext"
Abstract: Latent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections. With the proliferation of hypertext document collection such as the Internet, there has also been great interest in extending these approaches to hypertext (Cohn and Hofmann '01, Erosheva et al. '04). These approaches typically model links in an analogous fashion to how they model words - the document-link co-occurrence matrix is modeled in the same way that the document-word co-occurrence matrix is modeled in standard topic models.

We present a probabilistic generative model for hypertext document collections that explicitly models the generation of links. Specifically, links from a word w to a document d depend directly on how frequent the topic of w is in d, in addition to the in-degree of d. We show how to perform EM learning on this model efficiently. By not modeling links as analogous to words, we end up using far fewer free parameters and obtain better link prediction results.

Joint work with Michal Rosen-Zvi and Yair Weiss.

Wednesday, 25 November 2009, 10:00AM -- Bahen Centre, Rm. 5256
Computational Vision Seminar
Speaker: Yanxi Liu, Penn State University
Title: "Symmetry"
Abstract: Symmetry is an essential mathematical concept, as well as a ubiquitous, observable phenomenon in nature, science and art. Either by evolution or by design, symmetry implies an efficiency coding that makes it universally appealing, especially so to computational science. Recognition and categorization of symmetry and regularity is the first step towards capturing the essential skeleton of a real world problem, while at the same time minimizing computational redundancy. However, symmetry group detection from real world data turns out to be a challenging problem that has been puzzling computer vision, computer graphics and psychology researchers for decades. We explore a formal and computational characterization of real world regularity using a hierarchical model of symmetry groups as a theoretical basis, embedded in a well-defined Bayesian framework. Such a formalization simultaneously facilitates (1) a robust and comprehensive algorithmic treatment of the whole regularity spectrum, from regular (perfect symmetry), near-regular (approximate symmetry), to various types of irregularities; (2) an effective detection scheme for real world symmetries and symmetry groups; and (3) a set of computational bases for measuring and discriminating quantified regularities on diverse data sets. Besides some theoretical background on crystallographic groups in particular, I shall illustrate various applications of computational symmetry in texture synthesis, analysis, tracking, and manipulation; human gait and activity recognition; symmetry-based dance analysis; grid-cell clustering; automatic geo-tagging; and image ‘de-fencing’.

BIO: Yanxi Liu received her B.S. degree in physics/electrical engineering in Beijing and her Ph.D. degree in computer science for group theory applications in robotics from University of Massachusetts (Amherst). Her postdoctoral training was at LIFIA/IMAG (France). She also spent one year at DIMACS (NSF center for Discrete Mathematics and Theoretical Computer Science) under an NSF research-education fellowship award. Dr. Liu was with the research faculty in the Robotics Institute (RI) of Carnegie Mellon University before she joined the Computer Science Engineering and Electrical Engineering departments of Penn State University in Fall of 2006 as a tenured faculty and the co-director of the lab for perception, action and cognition (LPAC). Dr. Liu's research interests span a wide range of applications including computer vision, computer graphics, robotics, human perception and computer aided diagnosis in medicine, with two main themes: computational symmetry/regularity and discriminative subspace learning. Dr. Liu chaired the First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA) in conjunction with ICCV 2005. Dr. Liu served as an area chair or organizing committee member for CVPR08/MICCAI08/CVPR09, and has served as a multi-year chartered study section member for the US National Institute of Health (NIH). Dr. Liu is a senior member of IEEE and the IEEE Computer Society.

Thursday, 26 November 2009, 2:00PM -- GB 220 - Galbraith Bldg
Combinatorics Seminar
Speaker: Pawel Pralat, Department of Mathematics, West Virginia University
Title: "Chasing robbers on random graphs"
Abstract: We study the vertex pursuit game of Cops and Robbers where cops try to capture a robber on the vertices of the graph. The minimum number of cops required to win on a given graph G is the cop number of G. We present asymptotic results for the game of Cops and Robber played on a random graph G(n,p) for a wide range of p=p(n). It has been shown that the cop number as a function of an average degree forms an intriguing zigzag shape.

Friday, 27 November 2009, 11:00AM -- GB 244 - Galbraith Bldg
Theoretical Computer Science Seminar
Speaker: Irénée Briquel, Laboratoire de l’Informatique du Parallélisme - ENS, Lyon and Fields Institure
Title: "Lower bounds on the tree-width of boolean formulas."
Abstract: To a boolean formula can be associated the clause graph, where the vertices are the variables, and where two variables are linked in the graph when they belong to the same clause.

In a previous work, Pascal Koiran and Klaus Meer studied the link between the complexity of the formula and the tree-width of the clause graph - for short, the tree-width of the formula. They found an algorithm to compute efficiently polynomials associated with boolean formulas of bounded tree-width.

To estimate the limits of this method, it is interesting to look for lower bounds on the tree-width of boolean formulas, establishing that the previously mentioned algorithm is not efficient for the associated polynomials.

To find lower bounds on the tree-width, we show a link between this notion and the communication complexity of the boolean formula. We show that lower bounds on the communication complexity can transpose to the tree-width, and explore the possibilities of this method.

This is based on joint work with Pascal Koiran and Klaus Meer.

Tuesday, 1 December 2009, 11:00AM -- Bahen Centre, Rm. 1180
Distinguished Lecture Series Lecture
Speaker: Professor Fran Allen, IBM T.J. Watson Research Center
Title: "High Performance Computers and Compilers: A Personal Perspective"
Remarks: Fran Allen, IBM Fellow Emerita IBM T. J. Watson Research Center 1101 Kitchawan Rd. Yorktown Heights, NY 10598
Abstract: The talk will describe a related sequence of projects including some early, very bold projects that profoundly influenced high performance computing even as some of them failed. The talk includes a personal perspective of what worked and what didn’t, the historical threads of some ideas and the lessons learned. The talk concludes by identifying some current compiler challenges and the need for a new focus on new compilers.

Friday, 4 December 2009, 11:00AM -- D.L.Pratt Building, Rm. 266
Computational Vision Seminar
Speaker: Sam Hasinoff, MIT CSAIL
Title: "Fragmented Lenses and High ISO for Efficient Photography"
Abstract: I'll describe two projects that address basic technical challenges in photography: (1) minimizing defocus blur, and (2) capturing high dynamic range. In both cases we characterize fundamental limits, and propose new methods which improve efficiency over the state-of-the-art.

First, I'll describe our new lens design, the "lattice-focal" lens, that can capture in-focus images over a greater range of depths than previous approaches. The design follows from our analysis of lens defocus over the 4D space of light rays. As we show, the only usable energy lies on a 3D subset of this space in the Fourier domain. We establish an upper bound on performance (ie. over any possible lens design), and show that the lattice-focal lens is closer to this bound than any previous design.

Second, I'll show how existing cameras can be used more efficiently, to capture high dynamic range scenes. For a given scene and camera, our analysis lets us compute the optimal sequence of photos to capture, maximizing worst-case SNR. This provides significant gains over standard exposure bracketing, typically 10 dB better or 3 times faster, when capture time is limited. As I'll explain, most our gains come from using high (but varying) ISO settings -- counterintuitively, "turning up the amplifier" can help reduce noise.

Bio: Sam Hasinoff received the BSc degree in computer science from the University of British Columbia in 2000, and the MSc and PhD degrees in computer science from the University of Toronto in 2002 and 2008, respectively. He is currently an NSERC Postdoctoral Fellow at the Massachusetts Institute of Technology. In 2006, he received an honorable mention for the Longuet-Higgins Best Paper Award at the European Conference on Computer Vision. He is the recipient of the Alain Fournier Award for the top Canadian dissertation in computer graphics in 2008.

http://www.csail.mit.edu/~hasinoff/

Tuesday, 9 February 2010, 11:00AM -- Bahen Centre, Rm. 1180
Distinguished Lecture Series Lecture
Speaker: Dr. Joe Marks, Vice President, Disney Research
Title: "Interactive Media Research at The Walt Disney Company"
Remarks: Speaker bio: Joe Marks grew up in Dublin, Ireland. Being more perseverant than imaginative, he earned three degrees from Harvard University. His areas of interest include computer graphics, human-computer interaction, and artificial intelligence. He has worked previously at Bolt Beranek and Newman and at Digital's Cambridge Research Laboratory. Prior to joining The Walt Disney Company he was the Research Director at Mitsubishi Electric Research Labs in Cambridge, MA, from 2000-2006.
Abstract: The research challenges in interactive media faced by the different business units of The Walt Disney Company fall in four broad categories: motion pictures, park attractions, games & sports, and media networks. Enumerating these challenges provides a key industry perspective on promising directions for future research in digital arts, experiential media systems, creative environments, and emerging media technologies.

Tuesday, 30 March 2010, 11:00AM -- Bahen Centre, Rm. 1180
Distinguished Lecture Series Lecture
Speaker: Professor Maja Mataric, Computer Science and Neuroscience, University of Southern California
Title: "To be announced"


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