Organizers
[AutoML challenge][Schedule][Talk abstracts][Committee]
For any question, please contact us by email at nips2015@chalearn.org.
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Isabelle Guyon is an independent consultant, specialized in statistical data analysis, pattern recognition and machine learning. 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). She is also the primary inventor of SVM-RFE, a variable selection technique based on SVM. 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. 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.
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Co-founder and CTO of Kaggle
Ben Hamner leads Kaggle's product and engineering teams. Ben is the principal architect of many of Kaggle's most advanced machine learning projects, including developing machine learning for oil exploration and GE's flight arrival prediction and optimization modeling.
Kaggle is the world leader competition platform in data science.
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Research Scientist, Linear Accelerator Laboratory, CNRS-University of Paris
Balázs Kégl received the Ph.D. degree in computer science from Concordia University, Montreal, in 1999. From January to December 2000 he was a Postdoctoral Fellow at the Department of Mathematics and Statistics at Queen's University, Kingston, Canada, receiving NSERC Postdoctoral Fellowship. He was in the Department of Computer Science and Operations Research at the University of Montreal, as an Assistant Professor from 2001 to 2006. Since 2006 he has been a research scientist in the Linear Accelerator Laboratory of the CNRS. He has published more than hundred papers on unsupervised and supervised learning (principal curves, intrinsic dimensionality estimation, boosting), large-scale Bayesian inference and optimization, and on various applications ranging from music and image processing to systems biology and experimental physics. At his current position he has been the head of the AppStat team working on machine learning and statistical inference problems motivated by applications in high-energy particle and astroparticle physics. Since 2014, he has been the chair of the Center for Data Science of the (future) University of Paris Saclay.
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Evelyne Viegas is the Director of Semantic Computing at Microsoft Research, based in Redmond, Washington, United States. Semantic computing is about interacting with data in rich, safe, and semantically meaningful ways, to create the path from data to information, knowledge, and intelligence. In her current role, Evelyne is building initiatives that focus on information seen as an enabler of innovation, working in partnership with universities and government agencies worldwide. In particular, she creates programs in the area of computational intelligence to unleash the power of data and drive open innovation via agile experimentation and cloud-based services, as well as projects to advance the state of the art in machine learning, information-rich programming, knowledge representation, and reasoning under uncertainty at web scale. She currently leads the Machine Learning community effort for CodaLab.
Prior to her present role, Evelyne worked as a technical lead at Microsoft delivering Natural Language Processing components to projects for MSN, Office, and Windows. Before Microsoft, and after completing her Ph.D. in France, she worked as a Principal Investigator at the Computing Research Laboratory in New Mexico on an ontology-based Machine Translation project. Evelyne serves on international editorial, program, and award committees.