AI Insights for Policymakers

Nidhi is a Fellow and Canada CIFAR AI Chair at Amii and an Associate Professor in the Department of Computing Science at the University of Alberta. Before joining UAlberta, she spent many years in industry research labs. 

Most recently, she was a Research team lead at Borealis AI (a research institute at Royal Bank of Canada), where her team worked on privacy-preserving methods for machine learning models and other applied problems for RBC. Her 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.

Richards’ research lies at the intersection of neuroscience and AI. His laboratory investigates universal principles of intelligence that apply to both natural and artificial agents.

He has received several awards for his work, including the NSERC Arthur B. McDonald Fellowship in 2022, the Canadian Association for Neuroscience Young Investigator Award in 2019, and a Canada CIFAR AI Chair in 2018. Richards was a Banting Postdoctoral Fellow at SickKids Hospital from 2011 to 2013.

He obtained his PhD in neuroscience from the University of Oxford in 2010, and his BSc in cognitive science and AI from the University of Toronto in 2004.

Ulrich Aïvodji is an assistant professor of computer science in the Software and Information Technology Engineering Department of the École de technologie supérieure (ÉTS) in Montréal. He also leads the Trustworthy Information Systems Lab (TISL).

Aïvodji’s research areas are computer security, data privacy, optimization and machine learning. His current research focuses on several aspects of trustworthy machine learning, such as fairness, privacy-preserving machine learning and explainability.

Before his current position, he was a postdoctoral researcher at Université du Québec à Montréal, where he worked with Sébastien Gambs on machine learning ethics and privacy.

He earned his PhD in computer science from Université Paul-Sabatier (Toulouse) under the supervision of Marie-José Huguet and Marc-Olivier Killijian. He was affiliated with two research groups at the Systems Analysis and Architecture Laboratory–CNRS, one on dependable computing, fault tolerance and operations research, and another on combinatorial optimization and constraints.

Glen Berseth is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute.

He is a Canada CIFAR AI Chair and co-directs the Robotics and Embodied AI Lab (REAL). He was formerly a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine.

Berseth’s previous and current research has focused on solving sequential decision-making problems (planning) for real-world autonomous learning systems (robots). More specifically, his research has focused on human-robot collaboration, reinforcement, and continual-, meta-, multi-agent and hierarchical learning.

He has published in the top venues in robotics, machine learning and computer animation. He teaches a course on robot learning at Université de Montréal and at Mila, in which he covers the most recent research on machine learning techniques for creating generalist robots.

Dr. Alissa Centivany is an Assistant Professor in the Faculty of Information and Media Studies at Western University where she works on issues related to technology policy, law, and ethics. She holds a doctorate in information, with emphasis on the study of sociotechnical systems. She also holds a juris doctor, specializing in intellectual property and technology law. Prior to joining Western, Dr. Centivany held research fellowships at the University of Toronto Law School and at the University of California-Berkeley School of Law. In 2023, Dr. Centivany provided expert testimony before Canadian Parliament on copyright reform bills C-244 (diagnosis, maintenance, repair) and C-294 (interoperability). Dr. Centivany has made many guest appearances on radio and television programs and has been quoted by The Globe and Mail, CBC News, Toronto Star, and a variety of other news media outlets.

Dr. Centivany conducts research on technology policy, law, and ethics.  Her primary areas of inquiry include: breakdown, repair, and the right to repair movement; copyright, AI, and sociotechnical transformation; scholarly communications, open access, and open source technologies; and participatory policymaking.  

Marie Charbonneau joined the University of Calgary as an assistant professor in September 2021. Her research focuses on humanoid and collaborative robots, compliant whole-body control, and physical contact-based human-robot collaboration.

Before joining the University of Calgary, Dr. Charbonneau was a post-doctoral fellow in the RoboHub and the Human-Centred Robotics and Machine Intelligence Lab of the University of Waterloo. Her research focused on reliable whole-body control for humanoid robots that closely, physically interact with people. Marie worked as an early-stage researcher at the Istituto Italiano di Tecnologia (IIT) in Genoa from 2015 until 2019, while completing a PhD in whole-body control of humanoid robots. She obtained her master's in Robotics Engineering in 2014, through the Erasmus Mundus EMARO program, carried out as a joint degree between the University of Genoa and Warsaw University of Technology, with the thesis project carried out at ETH Zürich. She received a BASc in Mechanical Engineering from the University of Sherbrooke in 2010, leading her to begin her career as a mechanical design and simulation consultant before leaping to the fascinating field of robotics.

Audrey Durand is an assistant professor in the Department of Computer Science and Software Engineering and in the Department of Electrical and Computer Engineering at Université Laval.

She specializes in algorithms that learn through interaction with their environment using reinforcement learning, and is particularly interested in leveraging these approaches in health-related applications.

She completed a Ph.D. in Electrical Engineering in the Computer Vision and Systems Laboratory at Université Laval.

Shion Guha is an Assistant Professor in the Faculty of Information at the University of Toronto. He was an Assistant Professor of Computer Science at Marquette University from 2016-2021 where he helped develop their undergraduate and graduate data science programs. He received his PhD from Cornell University (2016). His work has been supported by grants from National Science Foundation, Facebook, American Political Science Association, Northwestern Mutual, Parkview Health etc. 

Guha’s research interests include human-computer interaction, data science, and public policy. He has been involved in developing the emerging field of Human-Centred Data Science. This intersectional research area combines technical methodologies with interpretive inquiry to address biases and structural inequalities in socio-technical systems. Guha is interested in understanding how algorithmic decision-making processes are designed, implemented and evaluated in public services. He often works with marginalized and vulnerable populations, such as child welfare, criminal justice, and healthcare systems.

Guillaume Lajoie is an Associate professor in the Department of Mathematics and Statistics at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute. He is also a Fonds de recherche du Québec - Health Research Scholar and holds a Tier 2 Canada Research Chair in Neural Computation and Interfacing.

Previously, Lajoie was a postdoctoral fellow at the Max Planck Institute for Dynamics and Self-Organization in Germany and at the University of Washington’s Institute for Neuroengineering. He obtained his PhD from the Department of Applied Mathematics at the University of Washington (Seattle).

Lying at the intersection of AI and neuroscience, Lajoie’s research pursues questions surrounding neural network dynamics and computations, which has potential applications to neuroengineering.

Recent work has focused on the development of architectural inductive biases for information propagation in recurrent networks, as well as the development of algorithms and models for bidirectional brain-machine interface optimization.

Dr. Laleh Seyyed-Kalantari is an Assistant Professor at York University's Lassonde School of Engineering. She conducted postdoctoral research at the Vector Institute and the University of Toronto as an NSERC fellow (2019-2022). She holds a Ph.D. in electrical engineering from McMaster University (2017). Her research interests are responsible AI, generative AI, and AI fairness. Dr. Seyyed-Kalantari has garnered prestigious awards such as Google Research Scholar Program award (2024), Banting Postdoctoral Fellowship (2022-declined) and NSERC Postdoctoral Fellowship (2018), among others. She has received recognition for her contributions to AI model fairness in medical imaging, featured in various tech news outlets.