Canada AI Day

Speakers

Invited Speakers and Panelists

Colin Raffel

Freda Shi

Colin Raffel

Colin Raffel is an Associate Professor at the University of Toronto and an Associate Research Director at the Vector Institute. His research in machine learning aims to make it easy to get computers to do new things. Areas of focus in his lab include machine learning algorithms that require little or no labeled data in order to perform a task and systems for collaborative and continual development of machine learning models. He received a PhD in Electrical Engineering from Columbia University in 2016, a Master’s in Music from Stanford University in 2010, and a Bachelor’s in Mathematics from Oberlin College in 2009.

Freda Shi

Freda Shi is an Assistant Professor in the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute. Shi’s research interests are in computational linguistics, natural language processing, and cognitive science, as well as related aspects of machine learning. Shi received her Ph.D. in Computer Science from the Toyota Technological Institute at Chicago, and her B.S. in Intelligence Science from Peking University.

Victor Zhong

Victor Zhong is an Assistant Professor in the Cheriton School of Computer Science of the University of Waterloo, and a Faculty Member at Vector Institute.

Victor’s research is at the intersection of natural language processing and machine learning, and aims to teach machines to read natural language specifications to generalize to new problems. His work spans interactive learning, robotics, semantic parsing and conversation agents. His recent work includes AI systems that generalize to new environments by reading manuals, automated curriculum learning from language, and automatically generating reward functions from language for robotic control.

Sivan Sabato

Sivan Sabato is an Associate Professor at the Department of Computing and Software at McMaster University, a Canada CIFAR AI Chair and a Vector Institute faculty member. Sivan serves as an Action Editor for the Journal of Machine Learning Research and for Transactions of Machine Learning Research and frequently serves in senior organization positions at machine learning conferences such as ICML, COLT and ALT. Sivan's research interests include machine learning theory, interactive learning algorithms, and algorithmic fairness.

Ming Hou

Dr. Hou is a Fellow of the Canadian Academy of Engineering and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He is a Principal Scientist and Authority within the Department of National Defence (DND), Canada, an Advisor to the Canadian $1.6B Innovation for Defence Excellence and Security program, and the Co-Chair of an international military Human-Autonomy Teaming Specialist Committee. Dr. Hou is responsible for delivering cutting-edge technological solutions, science-based advice, and evidence-based policy recommendations on AI, Autonomy, and Telepresence science, technology, and innovation strategies to senior decision makers within DND and their national and international partner organizations including the United Nations (UN). He is a world-renowned leading expert in autonomous systems and human-machine intelligence interactions. Dr. Hou’s influential book: “Intelligent Adaptive Systems: An Interaction-centered Design Perspective" (two editions) is considered authoritative by experts in academia, industry, and defence & security communities and has been recognized as “quintessential reading for scientists, engineers, practitioners, designers, and anyone interested in building and using 21st century human-computer symbiosis technologies.” His systemic Interaction-Centred Design approach to Autonomy and Human-AI Symbiotic Collaborations has been instrumental in the generation of 1) novel technological solutions including the 1st Canadian Intelligent Tutoring System, the 1st Canadian Command and Control Center for Remotely Piloted Aircraft Systems, and the world 1st AI-enabled Decision Support System for Weapon Engagement; 2) Canadian government Autonomy science and technology innovation programs; 3) disruptive Canadian and allied defence & security capabilities; 4) new international military standards and UN White Papers on Autonomous Systems; and 5) government AI and autonomy policy and regulation frameworks for the society at large. Dr. Hou is the recipient of the most prestigious DND Science and Technology Excellence Award in 2020, the President’s Achievement Award of the Professional Institute of the Public Service of Canada in 2021, and the IEEE Outstanding Contribution Award in 2024. He is the Chair of Human-AI Teaming for IEEE Future Directions, an IEEE Distinguished Lecturer, and internationally sought-after Principal Communicator with 100+ plenary keynote speeches, panel discussions, and invited lectures at various prestigious scientific and defence fora. Dr. Hou is the General Chair of the 2024 IEEE International Conference on Human-Machine Systems and 2024 and 2025 International Defence Excellence and Security Symposia. He is also an Adjunct Professor at the University of Toronto and the University of Calgary, Canada.

Nima Shahbazi

Nima Shahbazi leads the Data Science and Machine Learning team at Collective[i], overseeing a talented group of over 30 data scientists and engineers. He specializes in developing advanced AI solutions, including large language models, retrieval-augmented generation, agentic architectures, and MLOps, with a focus on building adaptive AI agents that drive scalable business impact.

Nima is a $1M Zillow Prize winner, recognized for creating the world’s most accurate home valuation model, and a finalist in the $1M Leaders Prize for AI tools combating misinformation.

In quantitative finance, Nima achieved prize-winning ranks in global competitions, including 2nd place at Two Sigma and 7th at Optiver. As a Kaggle Grandmaster, he was ranked #1 in Canada and #19 worldwide among millions of data scientists.

He is a frequent speaker at major AI conferences, sharing insights on time-series modeling, multi-agent systems, LLM integration, and scalable enterprise ML with a strong scientific and product-driven approach.

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