Isabelle Guyon

Isabelle Guyon is an independent consultant, specialized in statistical data analysis, pattern recognition and machine learning. Her areas of expertise include computer vision and and bioinformatics. Her recent interest is in applications of machine learning to the discovery of causal relationships. Prior to starting her consulting practice in 1996, Isabelle Guyon was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces and co-invented Support Vector Machines (SVM), a machine learning technique, which has become a textbook method. She is also the primary inventor of SVM-RFE, a variable selection technique based on SVM. The SVM-RFE paper has thousands of citations and is often used as a reference method against which new feature selection methods are benchmarked. She also authored a seminal paper on feature selection that received thousands of citations. She organized many challenges in Machine Learning over the past few years supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, and Texas Instrument. Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie, Paris, France. She is president of Chalearn, a non-profit dedicated to organizing challenges, vice-president of the Unipen foundation, adjunct professor at New-York University, action editor of the Journal of Machine Learning Research, and editor of the Challenges in Machine Learning book series of Microtome.

As a leader in the organization of challenges, Isabelle served as an advisor in the development of the competition platform Codalab developed by Microsoft and Stanford University and pioneered the implementation of several challenges on Codalab.