We study computational and statistical methods in materials science
Research Focus Areas
Optimal Experimental Design and Bayesian Optimization
How to select experiments with some objective in mind, with the goal of minimizing the number of experiments needed to achieve the stated objective?
Prior Knowledge Formation, Elicitation, and Representation
Prior knowledge simplifies problems, reducing dimensionality and data requirements. How do we encode what scientists know about their problem, and quantify their confidence in this knowledge?
Machine Learning Enabled Atomistic Simulations
In addition to classical simulation work (such as kinetic Monte Carlo and molecular dynamics), we are interested in augmenting materials simulation with machine learning to gain computational speedups.