Invited Speakers


Kristen Grauman
Kristen Grauman

Kristen Grauman's research in computer vision and machine learning focuses on visual search and object recognition. She is the Clare Boothe Luce Assistant Professor in the Department of Computer Science at the University of Texas at Austin. Before joining UT-Austin in 2007, she received her Ph.D. in EECS from the MIT Computer Science and Artificial Intelligence Laboratory. She is a Microsoft Research New Faculty Fellow, and a recipient of an NSF CAREER award and the Frederick A. Howes Scholar Award in Computational Science.


Dana Pe'er
Dana Pe'er

Dana Pe'er is an assistant professor in the department of biological sciences, at Columbia University. She was a postdoctoral fellow at Harvard Medical School genetics department where she worked with George Church. She received her Ph.D. in computer science from the Hebrew University, where she worked with Nir Friedman. She has won numerous awards and prizes, including the Burroughs Welcome Career Award Fellowship, NIH Directors New Innovator program, and the Packard Fellowship. Her work was featured on Science journal 2nd runner-up for breakthrough of the year 2005.


Odelia Schwartz
Odelia Schwartz

Odelia Schwartz is an assistant professor at the Albert Einstein College of Medicine in New York. Her laboratory uses tools of computational and theoretical neuroscience and machine learning, to study sensory systems from the neural levels through to perception and behavior. She was a postdoctoral fellow at the Howard Hughes Medical Institute and The Salk Institute. She received her Ph.D. from New York University in computational neuroscience, and her MS from University of Florida in Computer Science and Engineering.


Michèle Sebag
Michèle Sebag

With a background in maths (Ecole Normale Superieure, agregation of Mathematics) , I went to industry (Thomson-CSF, now Thales) where I started to learn about computer science, project management, and Artificial Intelligence. I got really interested in AI, became Consulting Engineer, and realized that Machine Learning was something to be. I was offered the opportunity to start research on Machine Learning for applications in Numerical Engineering, specifically Mechanics of Solids, at Laboratoire de Mécanique des Solides at Ecole Polytechnique I passed my PhD in between Machine Learning (LRI, Université Paris-Sud Orsay), Data Analysis (Ceremade, Université Paris-10 Dauphine) and Mechanics (LMS, Ecole Polytechnique), and I entered CNRS (CR1, 1991). In 2001, I went to LRI, Université Paris-Sud as head of the Inference and Apprentissage research group, founded by Yves Kodratoff. In 2003, I founded together with Marc Schoenauer the research group TAO, Apprentissage and Optimization at INRIA.