Current Research

Anomaly detection in 3D printing

3D printing failures incur a significant economic cost upon manufacturers, but can be challenging to quickly detect. While some machine-learning models have already shown promise in quickly exposing failed runs, training such models on real-world printing setups is expensive. By using numerical methods to simulate 3D printing, we can quickly iterate models and explore efficient solutions. 

Reinforcement learning for soliton nucleation/control

Magnetic solitons such as skyrmions and hopfions represent energy efficient and stable phenomena that allow for the encoding and transfer of information in magnetic materials. While computing paradigms based on solitons are promising, efficient production and control of solitons in general remain to be fully explored. We aim to evaluate the efficacy of methods such as reinforcement learning and genetic algorithms in this context. 

Computational linguistics, word learnability (NEW)

What makes words learnable, and how does the way LLMs acquire vocabulary compare to a child learner? Are these trends universal across languages? Leveraging pre-existing datasets, we form computational models to answer these questions.

Potential Fall 2026 project Themes

Simulated robotics

Modeling and data analysis for wet-lab collaboration