| 2020 DLRLSS Speakers |
| Blaise Agüera y Arcas Distinguished Scientist Blaise leads an organization at Google AI working on both basic research and new products. Among the team’s public contributions are MobileNets, Federated Learning, Coral, and many Android and Pixel AI features. They also founded the Artists and Machine Intelligence program, and collaborate extensively with academic researchers in a variety of fields. Until 2014 Blaise was a Distinguished Engineer at Microsoft, where he worked in a variety of roles, from inventor to strategist, and led teams with strengths in interaction design, prototyping, machine vision, augmented reality, wearable computing and graphics. Blaise has given TED talks on Seadragon and Photosynth (2007, 2012), Bing Maps (2010), and machine creativity (2016). In 2008, he was awarded MIT’s TR35 prize. | |
| Pierre-Luc Bacon Assistant Professor Université de Montréal Pierre-Luc Bacon is specialized in reinforcement learning. He is especially interested in the process of understanding and synthesizing disparate concepts into a coherent form. I like to establish connections to other disciplines to build a richer and more complete toolset. He will join the Department of Computer Science and Operations Research (DIRO) of the Université de Montréal in December 2019. | |
| Marco Baroni ICREA Professor / Research Scientist Department of Linguistics, UPF / Facebook AI Research Marco received a PhD in Linguistics from UCLA in 2000. Since then, he has been working for a number of institutions, both in academia and industry, and he is currently an ICREA Professor at Universitat Pompeu Fabra (Barcelona) and a Research Scientist as Fecbook AI Research (Paris). He did extensive work on distributed and multimodal word and sentence representations before it was trendy. He is currentl mainly interested in making neural networks more powerful and flexible by teaching them to communicate with each other and with humans. Marco is a Canada CIFAR AI Chair. | |
| Yoshua Bengio Professor/Scientific Director Université de Montréal/Mila Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. CIFAR’s Learning in Machines & Brains Program Co-Director, he is also the founder and scientific director of Mila, the Quebec Artificial Intelligence Institute, the world’s largest university-based research group in deep learning. In 2018, Yoshua Bengio ranked as the computer scientist with the most new citations worldwide, thanks to his many high-impact contributions. In 2019, he received the Killam Prize and the ACM A.M. Turing Award, “the Nobel Prize of Computing”, jointly with Geoffrey Hinton and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. Concerned about the social impacts of this new technology, he actively contributed to the development of the Montreal Declaration for Responsible Development of Artificial Intelligence. Yoshua is a Canada CIFAR AI Chair. | |
| Sarath Chandar Assistant Professor Polytechnique Montréal, Université de Montréal, Mila Sarath Chandar is a faculty Member, Mila; Assistant Professor, Polytechnique Montréal; Adjunct Professor, Dept. of Computer Science and Operations Research, Université de Montréalis. He is a Canada CIFAR AI Chair. His expertise is in deep learning, dialogue systems, lifelong learning, memory augmented architectures, natural language processing, recurrent neural networks, reinforcement learning | |
| Yejin Choi Associate Professor University of Washington / AI2 Yejin Choi is a Brett Helsel associate professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research manager at AI2 overseeing the project Mosaic. Her research interests include language grounding with vision, physical and social commonsense knowledge, language generation with long-term coherence, conversational AI, and AI for social good. She is a co-recipient of the AAAI Outstanding Paper Award in 2020, a recipient of Borg Early Career Award (BECA) in 2018, among the IEEE’s AI Top 10 to Watch in 2015, a co-recipient of the Marr Prize at ICCV 2013, and a faculty advisor for the Sounding Board team that won the inaugural Alexa Prize Challenge in 2017. Her work on detecting deceptive reviews, predicting the literary success, and interpreting bias and connotation has been featured by numerous media outlets including NBC News for New York, NPR Radio, New York Times, and Bloomberg Business Week. She received her Ph.D. in Computer Science from Cornell University. | |
| Aaron Courville Associate Professor Université de Montréal Aaron Courville is assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of Mila – Quebec Artificial Intelligence Institute. 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. | |
| Audrey Durand Assistant professor Université Laval Audrey is an Assistant Professor in the Computer Science and Software Engineering department, and the Electrical Engineering and Computer Engineering department, at Université Laval. She is also affiliated with Mila — Quebec Artificial Intelligence Institute through a Canada CIFAR AI Chair. Prior to that, she was a postdoc at McGill University. Her research focuses on reinforcement learning and leveraging the power of these algorithms in real-world applications. Through various interdisciplinary collaborations, she aims to bridge the gap between theory and practice. She is especially interested in applications with environmental or human impact, such as supporting health-related research through adaptive experimental designs. | |
| Alexei Efros Professor UC Berkeley Alexei (Alyosha) Efros is a professor in the EECS Department at UC Berkeley. Prior to that, he has worked at CMU, Oxford, and ENS/INRIA. His research is in the area of computer vision and computational photography. He is a recipient of the Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), SIGGRAPH Young Researcher Award (2010), three Helmholtz Test-of-Time awards (1999,2003,2005), ACM Prize in Computing (2016), and Diane S. McEntyre Award for Excellence in Teaching Computer Science (2019). | |
| Chelsea Finn Assistant Professor in Computer Science and Electrical Engineering Stanford University Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. She received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, an NSF graduate fellowship, a Facebook fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Chelsea is a CIFAR Fellow, Learning in Machines & Brains. | |
| Jakob Foerster Research Scientist Facebook AI Research Jakob Foerster received a CIFAR AI chair in 2019 and is starting as an Assistant Professor at the University of Toronto and the Vector Institute in fall 2020. During his PhD at the University of Oxford, he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since. | |
| Will Hamilton Assistant Professor McGill University William (Will) Hamilton is an Assistant Professor in the School of Computer Science at McGill University, a Canada CIFAR AI Chair, and a member of the Mila AI Institute of Quebec. Will completed his PhD in Computer Science at Stanford University in 2018. He received the 2018 Arthur Samuel Thesis Award for best Computer Science PhD Thesis from Stanford University, the 2014 CAIAC MSc Thesis Award for best AI-themed MSc thesis in Canada, as well as an honorable mention for the 2013 ACM Undergraduate Researcher of the Year. His interests lie at the intersection of machine learning, network science, and natural language processing, with a current emphasis on the fast-growing subject of graph representation learning. | |
| Jessica Hamrick Research Scientist DeepMind Jessica Hamrick is a Senior Research Scientist at DeepMind, where she studies how to build machines that can flexibly build and deploy models of the world. Her work combines insights from cognitive science with structured relational architectures, model-based deep reinforcement learning, and planning. Jessica received her Ph.D. in Psychology from UC Berkeley, and her M.Eng. in Computer Science and Engineering from MIT. | |
| Katja Hofmann Principal Researcher Microsoft Dr Katja Hofmann is a Principal Researcher at Microsoft Research in Cambridge, where she leads the Game Intelligence theme. She and her team develop new AI approaches with applications in video games, and other real-world applications. | |
| Sasha Luccioni Postdoctoral Researcher Mila Alexandra (Sasha) Luccioni is a Postdoctoral Researcher working on AI for Humanity initiatives at Mila - Quebec AI Institute, under the supervision of Yoshua Bengio. She obtained her PhD in Cognitive Computing from UQÀM in 2018 and spent two years working in applied ML, specifically in applying deep learning and NLP to different industrial applications. Sasha is currently working with Yoshua Bengio and others on a project that uses Artificial Intelligence to visualize the consequences of climate change. She is highly involved in community initiatives, serving on the Research and Policy Committee of Women in Machine Learning (WiML) and on the Advisory board of Kids Code Jeunesse. | |
| Rupam Mahmood Assistant Professor University of Alberta Rupam Mahmood is a Canada CIFAR AI Chair at Alberta Machine Intelligence Institute (Amii) and a faculty member of the Department of Computing Science at the University of Alberta, where he directs the Reinforcement Learning and Artificial Intelligence (RLAI) laboratory. He works in the areas intersecting reinforcement learning and robotics and develops learning systems and constructive learning mechanisms for continually learning robots. Prior to joining UAlberta, Rupam was the Lead of the AI Research team at Kindred AI, which provides robotic solutions for unstructured environments such as the warehouses of Gap Inc. At Kindred, Rupam had extensive industrial experience in developing reinforcement learning algorithms and real-time learning systems for controlling physical robots in real-world production setups. He is the creator of SenseAct, the first open-source toolkit and benchmark task suite for general-purpose real-time learning with different physical robots. During his graduate studies, he developed solutions to some long-standing problems with step-size adaptation, online representation search, and off-policy learning, a class of techniques for learning knowledge representations in a counter-factual and computationally scalable manner. | |
| Ofir Nachum Research Scientist Google Research Ofir Nachum works at Google Brain as a Research Scientist. His research focuses on deep reinforcement learning, including applications of convex duality as well as hierarchical and offline RL. He received his education at MIT. Before joining Google, he was an engineer at Quora, leading machine learning efforts on the feed, ranking, and quality teams. | |
| Courtney Paquette Assistant Professor McGill University, Google Courtney Paquette is currently working as a Research Scientist at Google Brain, Montreal. Starting 2020, she will be joining the Mathematics and Statistics department at McGill University as an assistant professor and is a Canada CIFAR AI Chair. Courtney received her Ph.D. from the Mathematics department at the University of Washington (2017) under Prof. Dmitriy Drusvyatskiy then she held a postdoctoral position in the Industrial and Systems Engineering at Lehigh University where she worked with Prof. Katya Scheinberg. She held an NSF postdoctoral fellowship (2018-2019) under Prof. Stephen Vavasis in the Combinatorics and Optimization Department at the University of Waterloo. Her research broadly focuses on designing and analyzing algorithms for large-scale optimization problems, motivated by applications in data science. | |
| Joelle Pineau Associate Professor and William Dawson Scholar/Faculty Member McGill University/Mila Joelle Pineau is a faculty member at Mila and an Associate Professor and William Dawson Scholar at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She is also co-Managing Director of Facebook AI Research, and the director of its lab in Montreal, Canada. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is Past-President of the International Machine Learning Society. She is a recipient of NSERC's E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR), a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada, and a 2019 recipient of the Governor General's Innovation Awards. | |
| Valérie Pisano President and CEO Mila Valérie Pisano has over 10 years of experience in talent, leadership, diversity and organizational dynamics. Appointed CEO of Mila – Quebec Artificial Intelligence Institute – she joins an emerging group of women leading tech companies. She was previously Chief Talent Officer at Cirque du Soleil and cofounded The Mobïus Bias Project, an initiative focused accelerating the dialogue on female leadership roles by exploring unconscious bias. She began her career at McKinsey & Company after a Master Degree in Economics at HEC Montreal, and is the proud mother of three girls. | |
| Doina Precup Associate Professor McGill University Doina Precup teaches at McGill while conducting fundamental research on reinforcement learning, working in particular on AI applications in areas that have a social impact, such as health care. She’s interested in machine decision-making in situations where uncertainty is high. She is a senior fellow of the CIFAR Fellow, Learning in Machines & Brains program, Canada CIFAR AI Chair, fellow of the Association for the Advancement of Artificial Intelligence and she also heads the Montreal office of Deepmind. Specialist In: Artificial intelligence, machine learning, reinforcement learning, reasoning and planning under uncertainty, applications. | |
| Andrew Saxe Sir Henry Dale Fellow University of Oxford Dr. Andrew Saxe is a Sir Henry Dale Fellow in the Department of Experimental Psychology, University of Oxford. He was previously a Swartz Postdoctoral Fellow in Theoretical Neuroscience at Harvard University with Haim Sompolinsky, and he completed his PhD in Electrical Engineering at Stanford University, advised by Jay McClelland, Surya Ganguli, Andrew Ng, and Christoph Schreiner. His research focuses on the theory of deep learning and its applications to phenomena in neuroscience and psychology. | |
| Angela Schoellig Associate Professor University of Toronto; Vector Institute Angela Schoellig is an Associate Professor at the University of Toronto Institute for Aerospace Studies. She holds a Canada CIFAR AI Chair and a Canada Research Chair in Machine Learning for Robotics and Control. She is a principal investigator of the NSERC Canadian Robotics Network and a Faculty Member of the Vector Institute for Artificial Intelligence. She conducts research at the intersection of robotics, controls, and machine learning. Her goal is to enhance the performance, safety, and autonomy of robots by enabling them to learn from past experiments and from each other. More information can be found at: www.schoellig.name. | |
| Elissa Strome AVP Research & Executive Director, Pan-Canadian AI Strategy CIFAR Elissa was appointed Executive Director of the CIFAR Pan-Canadian AI Strategy in January 2018 and Associate VP Research in March 2019. In her role, she works with Canada’s three national AI Institutes in Edmonton (Amii), Montreal (Mila), and Toronto (Vector Institute) and researchers across the country to advance Canada’s leadership in AI research and Innovation. She is a member of the federal government’s AI Advisory Council, the Joint Ministers’ (Ontario) Roundtable on AI and health data, and the OECD Network of Experts on AI.
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| Adam White Assistant Professor, Research Scientist University of Alberta, Amii, DeepMind Adam White is an Assistant Professor at the University of Alberta and a Senior Research Scientist at DeepMind. He is a principal investigator of the Alberta Machine Intelligence Institute and the Reinforcement Learning and Artificial Intelligence group at the University of Alberta. Adam is a Canada CIFAR Chair in Artificial Intelligence. His work has been published in top conferences in ML and AI, including NeurIPS, ICML, ICLR, AAAI, IJCAI, and AAMAS. In particular, his work on off-policy learning and predictive knowledge representations is used by several major research groups as a foundation for their AI research. | |
