Amii is pleased to welcome 15 new Canada CIFAR AI Chairs into our research community.
In an announcement made today by the Honourable François-Philippe Champagne, Federal Minister of Innovation, Science and Industry, CIFAR has appointed an additional 29 Canada CIFAR AI Chairs at Amii, Mila and the Vector Institute, Canada’s three AI institutes. This brings the total number of chairs to over 100 researchers -- 26 residing with Amii. The announcement comes as part of AICan, the annual meeting of the Pan-Canadian AI Strategy, which confirms Canada’s place as a world-leading destination for companies looking to invest in their AI capabilities.
The new chairs are based out of the University of Alberta (Alberta), Simon Fraser University (British Columbia), the University of Regina (Saskatchewan) and Carleton University (Ontario).
“Amii is pleased to name these latest Canada CIFAR AI Chairs in recognition of the strong research talent in Canadian universities. For nearly 20 years, Amii and the University of Alberta, alongside other partners, have advanced some of the world’s most innovative technologies, solved many of AI’s most challenging problems, and developed a global hub for AI talent. Now, we’re excited to continue to grow our pan-Canadian connections by naming researchers at Simon Fraser University, Carleton University and the University of Regina – in addition to strengthening our connection with the University of Alberta. This new roster of AI Chairs is the next step in growing Canada’s AI advantage and inspiring world-changing machine intelligence for good and for all.” - Cam Linke, CEO, Amii
“On behalf of CIFAR, I am delighted to welcome and congratulate the newest cohort of Canada CIFAR AI Chairs. They will join a vibrant network of talented researchers across Canada who are responding to some of the world’s most pressing challenges. Their contributions will transform the future and promote an AI world that benefits all.” - Dr. Elissa Strome, Executive Director, Pan-Canadian AI Strategy, CIFAR
Since 2017, 57 researchers have taken up their first faculty position in Canada as Canada CIFAR AI Chairs. The prestigious program, a cornerstone of the $125 million Pan-Canadian AI Strategy, provides researchers with long-term, dedicated funding to support their research programs and help them to train the next generation of AI leaders and practitioners.
Congratulations to the new Canada CIFAR AI Chairs at Amii, who join a rapidly growing community of world-leading researchers. Learn more about their work below:
Richard S. Sutton
University of Alberta
Learning from experience
In addition to his appointment as a Canada CIFAR AI Chair, Richard S. Sutton is Amii’s Chief Scientific Advisor, a Professor of Computing Science at the University of Alberta, a Distinguished Research Scientist at DeepMind. He has been named a Fellow with the Royal Society of Canada, the Association for the Advancement of AI, the Canadian AI Association and CIFAR. Rich is best known as one of the pioneers and world-leaders in reinforcement learning, an approach to artificial and natural intelligence that emphasizes learning and planning from sample experience.
Richard is most interested in understanding what it means to be intelligent, to predict and influence the world, to learn, perceive, act, and think. He seeks to identify general computational principles underlying what we mean by intelligence and goal-directed behaviour. Over his career, he has made a number of significant contributions to the field, including the theory of temporal-difference learning, the actor-critic (policy gradient) class of algorithms, the Dyna architecture (integrating learning, planning and reacting), the Horde architecture, and gradient and emphatic temporal-difference algorithms – among other advancements. Richard currently seeks to extend reinforcement learning ideas to an empirically grounded approach to knowledge representation based on prediction.
Michael Bowling
University of Alberta
Games are serious business
Michael Bowling is fascinated by the problem of how computers can learn to play games through experience. His teams’ milestone advances in poker -- Cepheus, which ‘essentially’ solved the game of heads-up limit Texas hold’em, and DeepStack, the first AI to beat human professionals at heads-up no-limit Texas hold’em -- represent theoretical leaps forward in the world of imperfect (or hidden) information games. He also led the development of the Arcade Learning Environment, which was instrumental in establishing the subfield of deep reinforcement learning.
Mo Chen
Simon Fraser University
Finding the right path in human-machine interactions
Mo Chen’s research centers around developing algorithms that allow robots to interact closely with humans in a safe and natural manner. By combining purely data-driven approaches and classical analytical approaches to problems in robotics and human-robot interactions, he aims to find ways to make learning more effective. Currently, Mo is working on a number of projects including the follow-ahead project, which leverages reinforcement learning to develop a robot that aims to autonomously stay in front of a person, and predicting navigational intent in humans in order to better-inform pathfinding robots.
Russ Greiner
University of Alberta
Personalizing healthcare
Russ Greiner focuses on developing and improving applications of machine learning in medicine, providing solutions for specific real-world problems across a range of clinical considerations. He works closely with clinicians and researchers in medicine (in psychiatry, oncology, cardiovascular, diabetes, and other areas), metabolomics and other disciplines to develop data-driven tools that assist practitioners with screening, diagnosis, prognosis and treatment planning in physical and mental health. Russ is also interested in building better algorithms that learn from experience, working to produce more robust and effective machine learning systems.
Yuhong Guo
Carleton University
Improving learning autonomy
Working within the field of machine learning, Yuhong Guo focuses on learning useful data representations and accurate classification models under various circumstances. Her ultimate research goal is to automate the learning process and reduce the dependence of learning systems on human guidance. Her research program has been founded on three main directions: generalized transfer learning, learning from incomplete data, and learning from weakly supervised data.
Matthew Guzdial
University of Alberta
Learning creativity
Matthew Guzdial works on creative AI and ML, an area of research that can enable machine learning to move from predicting what has come before to anticipating and creating new possibilities. His research applies AI and ML to domains we would typically consider requiring human creativity, such as generating content for video games, visual art and creative commentary. He has applied computational creativity for image classification and generation in a transfer learning framework (beating state of the art baselines), and built a benchmark for the development of new creative ML agents.
Nidhi Hegde
University of Alberta
Busting bias and preserving privacy
Nidhi Hedge’s current research focus is on a fundamental approach to privacy and ethics in AI. Her goal is to investigate how outcomes from AI and ML methods breach privacy and impact fairness and bias. She seeks to create algorithms that are private and fair by design, which involves new mathematical models and algorithms that provide desired outcomes while maintaining privacy and fairness. She likes to work on real, practical problems, which often lead to fundamental questions that need to be addressed before a solution can be designed.
Levi Lelis
University of Alberta
Intelligent systems, augmented strategies
Levi Lelis’ research goal is to develop intelligent systems that are able to augment people through teaching and collaboration. Currently, his group is working on algorithms to generate knowledge, such as strategies for playing games, that people can easily interpret and understand. They seek to use machine-generated knowledge to teach humans how to solve problems. For example, these machine-created interpretable strategies can be used to compile human-readable manuals for teaching people game strategies.
Lei Ma
University of Alberta
Engineering better learning systems
Lei Ma’s research focuses on providing both fundamental quality assurance methodologies and systematic engineering support for building complex AI systems to make them more reliable, safe and secure. He works to bridge the gap between AI and its real-world applications. His research mainly covers software engineering, machine learning, and their interdisciplinary fields, including designing better machine learning techniques with principles and methodologies of software engineering (also known as machine learning system engineering).
Martin Müller
University of Alberta
Search, plan, Go!
Martin Müller is interested in developing efficient search methods for hard problems. He and his research team work on understanding and improving Monte Carlo tree search, exploring and sampling in reinforcement learning, exploration in SAT, search and deep learning for Hex, and combinatorial game theory – especially developing efficient algorithms that combine search and subgame decomposition. He also works on domain-independent planning, random walk planning and motion planning and random sampling from time-changing discrete distributions. Martin and his team have produced programs and algorithms for the games of Go, Amazons, Clobber and Hex.
Patrick Pilarski
University of Alberta
Learning limbs
Patrick leads the Amii Adaptive Prosthetics Program – an interdisciplinary initiative focused on creating intelligent artificial limbs to restore and extend abilities for people with amputations. As part of this research, Patrick explores new machine learning techniques for sensorimotor control and prediction, including methods for human-device interaction and communication, long-term control adaptation, and patient-specific device optimization. He and his research teams have developed technologies such as the Bento Arm – a 3D printed robotic arm for myoelectric training and research – the HANDi (humanoid anthropometric naturally dextrous intelligent) Hand, and a robotic arm mapping software called BrachI/Oplexus.
Dale Schuurmans
University of Alberta
Modeling complexity
Dale Schuurmans’ long-term research goal is to develop systems that learn predictive models from massive data sources when the requisite models are complex – for example: in perception, language interpretation, information extraction, bioinformatics, or robot learning. Some of the key challenges he tackles are knowledge representation for learning -- how to usefully express and debug prior domain assumptions -- and navigating complex model spaces -- how to find good models while avoiding over/under-fitting.
Matthew E. Taylor
University of Alberta
Keeping humans in the loop
Matthew (Matt) E. Taylor focuses his research on developing intelligent agents, physical or virtual entities that interact with their environments. His main goals are to enable individual agents, and teams of agents, to learn tasks in real-world environments that are not fully known when the agents are designed; to perform multiple tasks, rather than just a single task; and to robustly coordinate with, and reason about, other agents. Additionally, he is interested in exploring how agents can learn from humans, whether the human is explicitly teaching the agent, the agent is passively observing the human, or the agent is actively cooperating with the human on a task.
Osmar Zaïane
University of Alberta
Augmented decision-making
Osmar Zaïane focuses on pattern discovery and information extraction from large databases, also known as data mining. His work involves data mining from the disparate heterogeneous data sources, such as on the Internet, as well as the analysis of complex information networks, also known as social network analysis. Specific research projects include the development of tools for data analytics such as Meerkat, a tool for analysing changes over time in a network of entities. Other of Osmar’s research projects relate to data mining in health informatics and the development of tools for document categorization and decision support systems.
Sandra Zilles
University of Regina
Data efficient learning
Sandra Zilles and her team focus on theoretical aspects of machine learning. She is particularly interested in methods for modelling and exploiting special types of interaction with machines to enable them to learn using less data than with conventional approaches. Intuitively, the research will make intelligent machines exploit the quality of well-chosen data rather than requiring a large quantity of potentially expensive data. The models and algorithmic techniques that will ultimately arise from this research may provide efficient solutions to complex problems in artificial intelligence – at a lower cost and with less data than is currently possible.
One of Canada’s three centres of AI excellence as part of the Pan-Canadian AI Strategy, Amii (the Alberta Machine Intelligence Institute) is an Alberta-based non-profit institute that supports world-leading research in artificial intelligence and machine learning and translates scientific advancement into industry adoption. Amii grows AI capabilities through advancing leading-edge research, delivering exceptional educational offerings and providing business advice – all with the goal of building in-house AI capabilities.