I'm currently a post-doc in the Robot Vision and Learning Lab led by Prof. Florian Shkurti. My research focuses on planning and reinforcement learning with large behavior models, exploring in particular how diffusion policies can be used in the offline setting.

Prior to this, I completed a post-doc in the D3M Lab led by Prof. Scott Sanner, where I developed novel research in automated planning, contributed to the development of the RDDL planning language, developed new software for planning (pyRDDLGym), and co-hosted the probabilistic track of the 2023 International Planning Competition (IPC). I completed my PhD in the department of Mechanical and Industrial Engineering at U of T. My thesis focused on knowledge transfer in deep reinforcement learning, developing novel applications of Bayesian inference and mathematical risk analysis to improve the robustness of transfer. My research has been published in top AI/ML conferences such as NeurIPS, UAI, and ICLR. I also interned at Google DeepMind, and was a post-graduate affiliate of the Vector Institute.

Prior to this, I completed my master's degree (MASc) at U of T, where my thesis focused on the theoretical developments of Thompson sampling applied in queueing and admission control problems with demand uncertainty. I received my Bachelor's degree in Business Administration (BBA) from the Schulich School of Business, graduating with distinction.

I enjoy reading books on cognitive science and play classical piano in my spare time.