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VP and Chief AI Scientist, Facebook | Silver Professor, New York University

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department.

He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty.

His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks and was reading between 10 and 20% of all the checks in the US in the early 2000s. His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to access scanned documents on the Web. Since the late 80's he has been working on deep learning methods, particularly the convolutional network model, which is the basis of many products and services deployed by companies such as Facebook, Google, Microsoft, Baidu, IBM, NEC, AT&T and others for image and video understanding, document recognition, human-computer interaction, and speech recognition.

LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR'06, and is chair of ICLR 2013 and 2014. He is on the science advisory board of Institute for Pure and Applied Mathematics, and has advised many large and small companies about machine learning technology, including several startups he co-founded. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.

Emeritus Professor, University of Toronto | VP and Engineering Fellow, Google | Chief Scientific Adviser, Vector Institute

Geoffrey Hinton is a VP engineering fellow at Google and chief scientific adviser at the Vector Institute. He received his Ph.D. in artificial intelligence from Scotland's University of Edinburgh in 1978. After five years as a faculty member at Carnegie-Mellon University in Pennsylvania, he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto, Ontario, where he's now an emeritus professor. Geoffrey was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification. Geoffrey Hinton is a fellow of the U.K. Royal Society and a foreign member of the U.S. National Academy of Engineering and the American Academy of Arts and Sciences. His awards include the David E. Rumelhart Prize, the IJCAI Award for Research Excellence, the Killam Prize for Engineering, the IEEE Frank Rosenblatt Medal, the NSERC Herzberg Gold Medal, the IEEE James Clerk Maxwell Gold medal, the NEC C&C Award, the BBVA award, the Honda Prize, and the Turing Award.

Founder/CEO, Insitro | Co-founder, Coursera | Adjunct Professor CS & Pathology, Stanford

Daphne Koller is CEO and Founder of Insitro, a machine-learning enabled drug discovery company. She was a Stanford CS Professor, co-founder of Coursera and Engageli, one of TIME Magazine’s 100 influential people, and a MacArthur Fellow.

Full Professor, University of Toronto

Raquel Urtasun is a professor in the Department of Computer Science at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. Prior to this, she was an assistant professor at the Toyota Technological Institute at Chicago, an academic computer science institute affiliated with the University of Chicago. She was also a visiting professor at ETH Zurich during the spring semester of 2010. She received her Ph.D. degree from the Computer Science department at Ecole Polytechnique Federal de Lausanne, Switzerland in 2006 and did her postdoc at the Massachusetts Institute of Technology and the University of California, Berkeley. She's a world-leading expert in AI for self-driving cars. Her research interests include machine learning, computer vision, robotics, and remote sensing. Her lab was selected as an NVIDIA NVAIL lab. Raquel has received an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award, an UPNA alumni award, and two Best Paper Runner-up prizes at the Conference on Computer Vision and Pattern Recognition in 2013 and 2017. She was also named Chatelaine 2018 Woman of the Year, and one of Toronto's 2018 top influencers by Adweek magazine.