TechAide AI Conference Speakers - April 17th, Rialto Theatre
Jeff joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he leads the Google Brain team,Google's deep learning research team in Mountain View. He has co-designed/implemented five generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. He is currently working on large-scale distributed systems for machine learning. He received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on compiler techniques for object-oriented languages. He is a Fellow of the ACM, a Fellow of the AAAS, a member of the U.S. National Academy of Engineering, and a recipient of the Mark Weiser Award and the ACM-Infosys Foundation Award in the Computing Sciences.
Yoshua Bengio (computer science, 1991, McGill U; post-docs at MIT and Bell Labs, computer science professor at U. Montréal since 1993): he authored three books, over 300 publications (h-index over 100), mostly in deep learning, holds a Canada Research Chair in Statistical Learning Algorithms, is Officer of the Order of Canada, recipient of the Marie-Victorin Quebec Prize 2017, he is a CIFAR Senior Fellow and co-directs its Learning in Machines and Brains program. He heads the Montreal Institute for Learning Algorithms (MILA), currently the largest academic research group on deep learning. He is on the NIPS foundation board (previously program chair and general chair) and co-created the ICLR conference (specialized in deep learning). He pioneered deep learning and his goal is to uncover the principles giving rise to intelligence through learning, as well as contribute to the development of AI for the benefit of all.
Laurent Charlin is an assistant professor of statistics at HEC Montréal. He earned a Master's degree and a PhD respectively from the universities of Waterloo and Toronto and was a postdoc at Columbia, Princeton and McGill universities. He develops probabilistic graphical models, including deep models, for analyzing large collections of data and to help in decision making. His main contributions are in the field of recommender systems. The Toronto paper matching system (TPMS), a system to recommend and match papers to reviewers that he co-developed, was adopted by dozens of major conferences over the last five years (it has recommended papers for over six thousand reviewers). He has published 20 papers in international conferences and won a second-best paper award at the 2008 Uncertainty in Artificial Intelligence (UAI) conference.
JACKIE CHI KIT CHEUNG
Jackie Chi Kit Cheung is an assistant professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He received his Ph.D. at the University of Toronto. He and his team conduct research on computational semantics and natural language generation, with the goal of developing systems that can perform complex reasoning in applications such as event understanding and automatic summarization.
Dr. Julien Cornebise is a Director of Research at Element AI and Head of the London Office. He is also an Honorary Researcher at University College London. Prior to Element AI, Julien joined DeepMind (later acquired by Google) in 2012 as an early employee. During his four years at DeepMind, he led several fundamental research directions used in early demos and fundraising, he helped create and lead its Health Applied Research Team. Since leaving DeepMind in 2016, he has been working with Amnesty International. Julien holds an MSc in Computer Engineering, an MSc in Mathematical Statistics, and earned his PhD in Mathematics, specialised in Computational Statistics, from University Paris VI Pierre and Marie Curie, for which he received the 2010 Savage Award in Theory and Methods from the International Society for Bayesian Analysis.
Aaron Courville is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of the LISA lab (LISA: Laboratoire d’Informatique des Systèmes Adaptatifs). His current recent research interests focus on the development of deep learning models and methods. Aaron is particularly interested in developing probabilistic models and novel inference methods. While he has mainly focused on applications to computer vision, he is also interested in other domains such as natural language processing, audio signal processing, speech understanding and just about any other artificial-intelligence-related task.
LAYLA EL ASRI
Layla El Asri is a Research Manager at Microsoft Research Montreal, a lab which is teaching machines to think, reason, and communicate with humans. She started her career as a research scientist at Maluuba, a Canadian startup acquired by Microsoft in 2017. Before that, she completed her Ph.D. in computer science at Université de Lorraine in France. Layla’s work focuses on human-machine communication and on how to improve statistical learning of dialogue systems. She leads a team seeking to build dialogue systems that are knowledgeable and can exchange information with users to help them accomplish tasks or gain knowledge.
Simon Lacoste-Julien is a CIFAR fellow and an assistant professor at MILA and DIRO from Université de Montréal. His research interests are machine learning and applied math, with applications to computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.
NICOLAS LE ROUX
Nicolas Le Roux got an MSc in Applied Maths from Ecole Centrale Paris and an MSc in Maths, Learning and Vision from ENS Cachan. He got his PhD in 2008 from University of Montreal where he worked with Yoshua Bengio on neural networks in general and their optimisation in particular. He then moved to Microsoft Research Cambridge to work on generative models of images with John Winn. In 2010, he joined Inria in Francis Bach's team to work on large-scale convex optimisation. From 2012 to 2017, he created and managed the research team at Criteo in Paris. He joined Google Brain Montreal in 2017 where he now works on large-scale optimization and reinforcement learning.
Ioannis Mitliagkas is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal. Before that, he was a Postdoctoral Scholar with the Department of Statistics and Computer Science at Stanford University. He obtained his Ph.D. from the Department of Electrical and Computer Engineering at the University of Texas at Austin. His research focuses on broad-scale statistical learning and inference problems, focusing on efficient broad-scale and distributed algorithms, and the tight theoretical and data-dependent guarantees and tuning complex systems.His recent work includes understanding and optimizing the scanning used in Gibbs sampling for inference, as well as understanding the interaction between optimization and the dynamics of large-scale learning systems.
Pascal Vincent is Associate Professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a Research Scientist at Facebook AI Research Montreal, as well as a founding member of the Montreal Institute for Learning Algorithms (MILA) and Associate Fellow in the Canadian Institute for Advanced Research (CIFAR / Learning Machines and Brains program). He obtained the equivalent of a MSc. in computer science from French engineering school ESIEE Paris in 1996, and a Ph.D. in computer science from Université de Montréal in 2004, in the field of machine learning, under the direction of Yoshua Bengio. He has been conducting research and contributing to advancing the field of artificial neural networks for more than 20 years. His work on principles and algorithms for representation learning led him to uncover seminal ideas that enabled the renaissance in deep neural networks (deep learning). In particular his work on denoising autoencoders was a precursor to approaches that proved essential for training the early generation of deep networks (pretraining & dropout), and to autoencoder-based generative models (variational autoencoders). He also coauthored the seminal paper on neural language models that laid the foundations on which all current neural network based language processing and translation systems are based. His research contributions are credited with over 17000 citations. His current research interests include developing novel principles and training approaches for learning high-dimensional generative models, and developing algorithms for more computationally and statistically efficient learning in deep neural networks.