2024 Waterloo.AI Graduate Scholarship Winners

Mahsa's PhD research focuses on developing a human-aware navigation algorithm for autonomous vehicles operating in pedestrian-rich environments. She uses AI techniques to predict pedestrian trajectories in interactive settings and employs these predictions to create an interaction-aware deep reinforcement learning motion planner that accounts for the uncertainty in pedestrians' future behaviors. The designed decision-making algorithm promotes safe, farsighted, and socially compliant vehicle navigation among pedestrians, aiming for a future where autonomous agents can coexist safely with humans.

Olha is currently pursuing her master’s degree under the supervision of Dr. Lukasz Golab and Dr. Robin Cohen. Her research focus is on building Explainable AI (XAI) frameworks and applications which directly help healthcare practitioners. Currently, one of the biggest bottlenecks of AI integration within fields such as healthcare is trust and reliability in these models. As the potential of AI models increases, our understanding of how these models make decisions decreases. Olha’s research is working towards developing XAI tools that help with the clinical decision-making process. These tools can decrease hospital length of stay and mitigate physician burnout and intra-physician variability in decision making.

Yudong’s Ph.D. research focuses on Reinforcement Learning, an emerging field within AI, under the guidance of Professor Pascal Poupart. His work delves into the problem of time-sequential decision-making under uncertainty and risk. His research applies to various real-world domains necessitating risk-aversion and safety considerations, such as minimizing substantial financial losses in portfolio management and ensuring safe behaviour in autonomous driving scenarios.