Organizers
IVADO
Mila
Université de Montréal
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 holds a Canada CIFAR AI Research Chair, and a Canada Research Chair in Neural Computation and Interfacing. His research is positioned at the intersection of AI and Neuroscience where he develops tools to better understand mechanisms of intelligence common to both biological and artificial systems. His research group’s contributions range from advances in multi-scale learning paradigms for large artificial systems, to applications in neurotechnology. Dr. Lajoie is actively involved in responsible AI development efforts, seeking to identify guidelines and best practices for use of AI in research and beyond.
IVADO
Mila
Université de Montréal
Dhanya Sridhar is an assistant professor in the department of Informatics and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute. She receives support as a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI.
Her research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently, and discover new knowledge alongside us. This large vision includes learning causal representations to interpret complex and unstructured data with limited human supervision, predictors that learn causal mechanisms to remain robust, interpreting large AI systems with causal abstraction, understanding and improving new learning paradigms like in-context learning, and aspects of responsible AI.
IVADO
Mila
Université de Montréal
Taylor Webb is broadly interested in the question of how the brain extracts structured, abstract representations from noisy, high-dimensional perceptual inputs, and uses these representations to achieve intelligent behavior. To better understand these processes, his work exploits a bidirectional interaction between cognitive science and artificial intelligence, with an emphasis on the visual domain. This involves two major components. First, he uses recently developed neural network modeling techniques to build models of higher-order cognitive processes (e.g., metacognition, analogical reasoning) that are grounded in realistic perceptual inputs (images). Second, he takes inspiration from cognitive science to design novel inductive biases aimed at imbuing deep learning algorithms with a more human-like capacity for reasoning and abstraction.
Winnie Street is a Senior Researcher on the Google Paradigms of Intelligence Team and a fellow at the Institute of Philosophy at the University of London. Her research combines philosophical and empirical approaches to questions of AI cognition, consciousness and moral status, and their implications for AI ethics and safety. She previously worked as a software developer, and researched human-computer interaction problems at the intersection of privacy, trust and ambient computing. She is interested in the relationships between intelligence, sociality, and consciousness, and how they might be understood through comparative studies of artificial and natural systems.
University of Chicago
James Evans’ research focuses on the collective system of thinking and knowing, ranging from the distribution of attention and intuition, the origin of ideas and shared habits of reasoning to processes of agreement (and dispute), accumulation of certainty (and doubt), and the texture—novelty, ambiguity, topology—of understanding. He is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery. Much of his work has focused on areas of modern science and technology, but he is also interested in other domains of knowledge—news, law, religion, gossip, hunches, machine and historical modes of thinking and knowing. He supports the creation of novel observatories for human understanding and action through crowd sourcing, information extraction from text and images, and the use of distributed sensors (e.g., RFID tags, cell phones). He uses machine learning, generative modeling, social and semantic network representations to explore knowledge processes, scale up interpretive and field-methods, and create alternatives to current discovery regimes. His research has been supported by the National Science Foundation, the National Institutes of Health, the Air Force office of Science Research, and many philanthropic sources, and has been published in Nature, Science, Proceedings of the National Academy of Science, American Journal of Sociology, American Sociological Review, Social Studies of Science, Research Policy, Critical Theory, Administrative Science Quarterly, and other outlets. His work has been featured in the Economist, Atlantic Monthly, Wired, NPR, BBC, El País, CNN, Le Monde, and many other outlets.
At Chicago, he is Director of Knowledge Lab, which has collaborative, granting and employment opportunities, as well as ongoing seminars. He also founded and now directs the Computational Social Science program at Chicago, and sponsors an associated Computational Social Science workshop. He teaches courses in augmented intelligence, the history of modern science, science studies, computational content analysis, and Internet and Society. Before Chicago, he received his doctorate in sociology from Stanford University, served as a research associate in the Negotiation, Organizations, and Markets group at Harvard Business School, started a private high school focused on project-based arts education, and completed a B. A. in Anthropology at Brigham Young University.
Google DeepMind
Andrew Lampinen is a Staff Research Scientist at DeepMind. He completed hisPhD in Cognitive Psychology at Stanford University. Prior to that, his background is in mathematics, physics, and machine learning.
His research interests bridge cognitive science and artificial intelligence, often with a focus on how the complex behaviors and representations of models, agents, or humans emerge from their learning experiences or data. He has particular interests in language (especially explanations) as a learning signal, the structure of internal representations and how they can be used to study or improve a system, and the effects of environmental complexity on learning.
Workshops
Cognitive Basis of Reasoning (in Minds and AI)
Mechanistic Basis of Reasoning (in Brains and AI)
Social Reasoning and the Ecology of Thought
Long Term Visitors
Queens College CUNY
Sari Kisilevsky is an Associate Professor of Philosophy at CUNY Queens College, where she specializes in ethics, philosophy of law, and political philosophy. She received my LLB and PhD from the University of Toronto, and her BA from McGill, where she first discovered philosophy. She was also a post-doctoral fellow at UCLA and a visiting professor at Princeton University, Hebrew University, and Tel Aviv University.
Her research focuses on the nature of law, law’s moral significance, its role in shaping human society and our moral and political lives, and the nature of normativity or “bindingness” of rules that people give to themselves. She has written on issues of punishment, responsibility, legal judgment, rules and rule following (and application), and other related questions.
She also works on Kant’s political philosophy, especially Kant’s Doctrine of Right, and on Just War Theory, with a special focus on the War on Terror.
Her recent work brings together these lines of research and focuses on Kant’s doctrines of war and peace. This work raises questions of the distinctive role of law in peace, and the moral value of peace more generally.
She also has recent work on responsibility and personhood, and the nature of instrumental value, and hard and easy cases. These projects (and more) are her focus while visiting Montréal for the 2025-26 academic year.
University of Southern California
Jieyu Zhao is an Assistant Professor of Computer Science Department at University of Southern California. Prior to that, she was an NSF Computing Innovation Fellow at University of Maryland, College Park, working with Prof. Hal Daumé III. Jieyu received her Ph.D. from Computer Science Department, UCLA, where she was advised by Prof. Kai-Wei Chang. Her research interest lies in fairness of ML/NLP models. Her paper got the EMNLP Best Long Paper Award (2017). She was one of the recipients of 2020 Microsoft PhD Fellowship and has been selected to participate in 2021 Rising Stars in EECS workshop. Her research has been covered by news media such as Wired, The Daily Mail and so on. She was invited by UN-WOMEN Beijing on a panel discussion about gender equality and social responsibility.