Léon BOTTOU joined the NEC Research Institute in 2002.  He received his doctorate in Computer Science from Université de Paris-Sud in 1991, and spent most of his research career with AT&T Bell Laboratories and AT&T Labs-Research. A large part of his machine learning work addresses the scalability of machine learning algorithms, from both the theoretical and practical point of views. Léon's publications can be viewed here.
 

Olivier CHAPELLE graduated in theoretical computer science from the Ecole Normale Supérieure de Lyon in 1999. From 1998 he has been working in AT&T Labs with V. Vapnik on Support Vector Machines and regularization theory. The main field of application has been in computer vision. In 2002, he received a doctorate from the University of Paris 6 in the field of learning theory with advisors Vladimir Vapnik and Patrick Gallinari. Since 2002, he is pursuing a post-doc at the Max Planck Institute in Tübingen. His current research interest include semi-supervised learning and the design of algorithms for kernel machines. Olivier's publications can be viewed here.

Dennis DECOSTE is Research Director of the Machine Learning group at Yahoo! Research. Before joining Yahoo! in 2003, he was a Principal Research Scientist at the Jet Propulsion Laboratory / California Institute of Technology.  He received his Ph. D. in Computer Science from the University of Illinois at Urbana-Champaign in 1994.  His research focuses on developing machine learning methods (especially variants of support vector machines) which are suitable for exploiting and handling massive data sets, across a wide variety of real-world applications (including time-series prediction, image classification, and (web page) text categorization).  Dennis' publications can be viewed here.

Jason WESTON completed his Ph. D. at Royal Holloway in 1999 (supervisor: Vladimir Vapnik) studying kernel learning algorithms and support vector machines. He now works in NEC Research Labs, Princeton. His areas of interest are scaling up kernel learning algorithms, structured learning, semi-supervised learning and applications to bioinformatics. Jason's publications can be viewed here.