OrganizeRs

Benjamin Donnot is a PhD student under the supervision of both isabelle GUYON and Antoine MAROT. He has graduated in statistics and machine learning from ENSAE Paristech (Paris, France). During his scholarship he developed different kinds of artificial agents capable of playing at different cards games such as “Tarot” card games or “Coinche” (similar to the Bridge card game). His PhD concerns the application of machine learning methods to the robust operation of large-scale power grids. It aims at assisting the dispatchers that operate the grid todays. His research interest includes machine learning and power systems. Deep learning and reinforcement learning are among his top areas of focus.

Antoine Marot is an R&D engineer at RTE (Reseau de Transport d’Electricité) in charge of a research team on an ambitious and potentially groundbreaking project called Apogee that aims at building a personal assistant for control room operators, in contrast of their always growing fragmented multi-applications with multi-screen working environment today. He owns a double master degree in Engineering from Stanford and Ecole Centrale de Paris with specific interests in both machine learning and the field of energy. After interning at Tesla Motors, he joined RTE on the Apogee project more than 3 years ago in Paris and is looking forward to building new fruitful research collaborations, in addition to the existing ones with INRIA, to build this dreamed personal assistant with a machine learning system. He gave talks at NIPS 2016 at the spatial-temporal workshop for the See4c European Challenge and at the 2017 IREP Conference for a co-authored paper on learning physical simulators. However, it appears that a machine learning for power systems community needs to be reinforced to make faster progress, and he is eager to contribute building such a community.

Isabelle Guyon is chaired professor in “big data” at the Université Paris-Saclay, specialized in statistical data analysis, pattern recognition and machine learning. Her areas of expertise include computer vision, bioinformatic, and power networks. Her recent interest is in applications of machine learning to the discovery of causal relationships. Prior to joining Paris-Saclay she worked as an independent consultant and was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces (with collaborators including Yann LeCun and Yoshua Bengio) and co-invented with Bernhard Boser and Vladimir Vapnik Support Vector Machines (SVM), which became a textbook machine learning 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 since 2003 supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, Facebook, Amazon, Disney Research, and Texas Instrument. Isabelle Guyon holds a Ph.D. degree in Physical Sciences of the University Pierre and Marie Curie, Paris, France. She is also 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, editor of the Challenges in Machine Learning book series of Microtome, and program co-chair of NIPS 2016 and general chair of NIPS 2017. She co-organized several special sessions of IJCNN/WCCI, including in 2017 the « Explainability and Interpretability in Machine Learning » and the « Machine Learning Methods applied to Vision and Robotics (MLMVR) » special sessions.

Louis Wehenkel graduated in Electrical Engineering (Electronics) in 1986 and received the Ph.D. degree in 1990, both from the University of Liège (Belgium), where he is full Professor of Electrical Engineering and Computer Science. His research interests lie in the fields of stochastic methods for systems and modeling, optimization, machine learning and data mining, with applications in complex systems, industrial process control, bioinformatics and computer vision. Since 30 years, he has worked almost continuously on the application of Machine Learning approaches to electric power system planning and operation, by combining them with physical models, numerical simulation, optimization, and Monte-Carlo methods.

He has also contributed to the field of machine learning per se, where his most well-known work is exemplified by the two papers “Tree-based batch mode reinforcement learning” and “Extremely Randomized Trees” published respectively in 2005 and 2006, and still among the most highly cited ones in their respective fields of research. His group has also contributed to the Scikit-Learn machine learning toolbox, by developing its very efficient implementation of the class of tree-based methods.

Since several years, Louis has been fostering research in artificial intelligence and applied mathematics to make electric power systems become smarter and more effective in contributing to the economy vs ecology dilemma. In this context, he gave in 2015 an invited talk to the National Research Council of the National Academies of the USA on his vision about “How to combine observational data sources with first principles of physics to build stable and transportable models for power system design and control”. He is currently engaged in various international initiatives to reduce the gap between electric power systems research and research in artificial intelligence.