[Ml-stat-talks] Fwd: [talks] Colloquium Speaker: Leonidas Guibas, Thursday, Feburary 25, 12:30pm- Reminder

Barbara Engelhardt bee at princeton.edu
Wed Feb 24 12:55:14 EST 2016

Talk of interest tomorrow.

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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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Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
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