Plenary Speakers

Professor Chris Bishop is Chief Research Scientist at Microsoft Research Cambridge. He also has a Chair in computer science at the University of Edinburgh, and is a Fellow of Darwin College Cambridge. Chris is the author of the leading textbook "Pattern Recognition and Machine Learning" (Springer, 2006). His research interests include probabilistic approaches to machine learning, as well as their application to fields such as biomedical sciences and healthcare.

The first successful applications of machine intelligence were based on expert systems constructed using rules elicited from human experts. Limitations in the applicability of this approach helped drive the second generation of machine intelligence methods, as typified by neural networks and support vector machines, which can be characterised as black-box statistical models fitted to large data sets. In this talk I will describe a new paradigm for machine intelligence, based on probabilistic graphical models, which has emerged over the last five years and which allows strong prior knowledge from domain experts to be combined with machine learning techniques to enable a new generation of large-scale applications. The talk will be illustrated with tutorial examples as well as real-world case studies.

Shree K. Nayar received his PhD degree in Electrical and Computer Engineering from the Robotics Institute at Carnegie Mellon University in 1990. He is currently the T. C. Chang Professor of Computer Science at Columbia University. He co-directs the Columbia Vision and Graphics Center. He also heads the Columbia Computer Vision Laboratory (CAVE), which is dedicated to the development of advanced computer vision systems. His research is focused on three areas; the creation of novel cameras, the design of physics based models for vision, and the development of algorithms for scene understanding. His work is motivated by applications in the fields of digital imaging, computer graphics, and robotics.

He has received best paper awards at ICCV 1990, ICPR 1994, CVPR 1994, ICCV 1995, CVPR 2000 and CVPR 2004. He is the recipient of the David Marr Prize (1990 and 1995), the David and Lucile Packard Fellowship (1992), the National Young Investigator Award (1993), the NTT Distinguished Scientific Achievement Award (1994), the Keck Foundation Award for Excellence in Teaching (1995) and the Columbia Great Teacher Award (2006). In February 2008, he was elected to the National Academy of Engineering.

The computational camera embodies the convergence of the camera and the computer. It uses new optics to select rays from the scene in unusual ways, and an appropriate algorithm to process the selected rays. This ability to manipulate images before they are recorded and process the recorded images before they are presented is a powerful one. It enables us to experience our visual world in rich and compelling ways.

Prabhakar Raghavan is the head of Yahoo! Labs. Raghavan's research interests include text and web mining, and algorithm design. He is a consulting professor of Computer Science at Stanford University and formerly editor-in-chief of the Journal of the ACM. He has co-authored two textbooks, on randomized algorithms and on information retrieval. Raghavan received his PhD from Berkeley and is a member of the National Academy of Engineering and a fellow of the ACM and of the IEEE.

Prior to joining Yahoo!, he was the chief technology officer at Verity and has held a number of technical and managerial positions at IBM Research.

By blending principles from mechanism design, algorithms, machine learning and massive distributed computing, the search industry has become good at optimizing monetization on sound scientific principles. This represents a successful and growing partnership between computer science and microeconomics. When it comes to understanding how online users respond to the content and experiences presented to them, we have more of a lacuna in the collaboration between computer science and certain social sciences. We will use a concrete technical example from image search results presentation, developing in the process some algorithmic and machine learning problems of interest in their own right. We then use this example to motivate the kinds of studies that need to grow between computer science and the social sciences; a critical element of this is the need to blend large-scale data analysis with smaller-scale eye-tracking and "individualized" lab studies.