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Research Analyst

Russell Investments

Collected and analyzed numerical and qualitative data on competing mutual funds to guide decision-making and strategy. Wrote macros to automate the preparation and analysis of client portfolio data, resulting in much faster turn-around time.

• Data Analysis

Research Scientist Intern

Google Deepmind UK (Remote)

Derived novel algorithms for continual/lifelong reinforcement learning, where an agent must learn to achieve a long sequence of diverse goals in the most efficient way possible.

• Continual Reinforcement Learning
• Lifelong Reinforcement Learning

Post-Doctoral Researcher

RVL Lab, Department of Computer Science, University of Toronto

Developed robust offline reinforcement learning algorithms in which an agent must evaluate a policy using only a pre-existing data set without any direct interactions with an environment. The advantages of learned world models and guided generative diffusion models explored as they relate to robustness and scalability of learning.

• Offline Reinforcement Learning

Post-Doctoral Researcher

D3M Lab, University of Toronto

Developed novel algorithms for efficient and scalable planning in high-dimensional spaces, particularly using symbolic methods, nonlinear optimization, and gradient-based optimization. Developed and maintained the Python planning library pyRDDLGym.

• Planning

PhD

Industrial Engineering, University of Toronto

Artificial Intelligence

Master of Applied Science

Industrial Engineering, University of Toronto

Operations Research

Bachelor in Business Administration

Schulich School of Business, York University

Finance and Actuarial Science