teaching

Experience

Advanced AI for Accelerated Materials Discovery · Co-Instructor · Department of Materials Science and Engineering, University of Toronto · 2026

Engineering Statistics and Numerical Methods · Co-Instructor · Department of Materials Science and Engineering, University of Toronto · 2025

Undergraduate Physics Lab · Instructor · Department of Physics, Indiana University Indianapolis · 2018–2024

Educational Materials

Experimental Design for Self Driving Laboratories · 2026

Introduction to Foundation Models for Science · 2025

Connecting Monte Carlo Methods to Modern ML/AI · 2023

Teaching Philosophy

My experiences advising graduate and undergraduate research have shaped my understanding of how—until now—science has been a human endeavor, performed by and communicated between humans. Now, artificial intelligence and machine learning (AI/ML) have the potential to change this aspect of science. AI/ML may improve experimental outcomes through increased accuracy and efficiency, but at the cost of distancing scientists from their tools, methods, data, and results. Crucially, AI/ML may isolate researchers from each other by replacing key peer-to-peer and mentor-mentee conversations with chat-bots trained to become domain experts. In the age of scientific AI/ML, my goal as a teacher is to offer something that differs in value and content from what my future students can obtain using AI/ML. I accomplish this through physical presence, connecting my mentees to a network of like-minded researchers who value the human-in-the-loop, and the discernment of knowing when to leverage a frontier model versus when to reach out to a domain expert for support.