I'm a researcher in materials informatics, with a focus in applied machine learning. Also, I am a strong believer in the open science movement and an advocate for science outreach organizations.
I enjoy cooking, martial arts (Taekwondo), listening to and playing music, reading sci-fi/fantasy novels, and playing video games. I'm an alumnus of the University of Guelph with a BSc in Nanoscience; currently, I'm working on my PhD in Materials Science at MIT in the Olivetti Group.
Most of my time is spent working on my PhD thesis, which focuses on a new approach for NLP-driven predictive materials synthesis. The goal of this project is to further accelerate the design of novel inorganic materials - particularly those with properties conducive to energy-related applications.
My efforts on this project include full-stack engineering and high-level project design, but my passion is in the application of machine learning models. I'm especially interested in investigating the effectiveness of various feature engineering approaches, the interpretability of ML models which aim to make physically-meaningful predictions, and adapting NLP algorithms to domain-specific contexts.
Over the past several years, I've worked on a variety of projects spanning the fields of experimental physics, computational materials science, materials informatics, Python modules, machine learning consulting, and battery electrode design. My formal training is in nanoscience, physics, and materials science - but my focus has gradually shifted from inorganic materials to broader domains.