Pascal Poupart

Faculty Member

Professor, David R. Cheriton School of Computer Science, University of Waterloo

Canada CIFAR Artificial Intelligence Chair

Member, Waterloo Artificial Intelligence Institute

Pascal is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab funded by the Royal Bank of Canada (2018-2020). His research focuses on reinforcement learning and machine learning more generally  with application to natural language processing, sports analytics and material design. He is most well known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team are currently working on include probabilistic deep learning, robust machine learning, data efficient reinforcement learning, conversational agents, automated document editing, adaptive satisfiability and knowledge graphs. Pascal completed his PhD in Computer Science at the University of Toronto, his Master’s degree at the University of British Columbia and undergraduate degree at McGill University. Pascal served as scientific advisor for ElementAI, DialPad, ProNavigator, Scribendi and his research collaborators also include Google, Microsoft, Intel, Ford, Manulife, Royal Bank of Canada, Bank of Montreal, Kik Interactive, In the Chat, Slyce, HockeyTech, the Alzheimer Association, the UW-Schlegel Research Institute for Aging, Sunnybrook Health Science Centre and the Toronto Rehabilitation Institute.

Research Interests

  • Machine Learning and Probabilistic Models
  • Natural Language Processing
  • Material Design


  • David R. Cheriton Faculty Fellowship (2015-2018)
  • Runner up best student paper award (SAT-2017)
  • Best main track solver and best application solver (SAT-2016 Competition)
  • Best paper award runner-up (UAI-2008)
  • Ontario Early Researcher Award (2008-2013)


Optimality and stability in non-convex smooth games

Guojun Zhang and Pascal Poupart and Yaoliang Yu


Quantifying and Improving Transferability in Domain Generalization

Guojun Zhang and Han Zhao and Yaoliang Yu and Pascal Poupart


Linearizing Contextual Bandits with Latent State Dynamics

Elliot Nelson and Debarun Bhattacharjya and Tian Gao and Miao Liu and Djallel Bouneffouf and Pascal Poupart


Learning Functions on Multiple Sets using Multi-Set Transformers

Kira A Selby and Ahmad Rashid and Ivan Kobyzev and Mehdi Rezagholizadeh and Pascal Poupart


System and method for bi-directional translation using sum-product networks

Mehdi Rezagholizadeh and Vahid PARTOVI NIA and Md Akmal Haidar and Pascal Poupart


Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning

Guiliang Liu and Xiangyu Sun and Oliver Schulte and Pascal Poupart


Decentralized Mean Field Games

Sriram Ganapathi Subramanian and Matthew E Taylor and Mark Crowley and Pascal Poupart


Learning Object-Oriented Dynamics for Planning from Text

Guiliang Liu and Ashutosh Adhikari and Amir-massoud Farahmand and Pascal Poupart


Distributional Reinforcement Learning with Monotonic Splines

Yudong Luo and Guiliang Liu and Haonan Duan and Oliver Schulte and Pascal Poupart


RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation

Md Akmal Haidar and Nithin Anchuri and Mehdi Rezagholizadeh and Abbas Ghaddar and Philippe Langlais and Pascal Poupart


Partially Observable Mean Field Reinforcement Learning

Sriram Ganapathi Subramanian and Matthew E Taylor and Mark Crowley and Pascal Poupart