Welcome to my personal webpage! I am an NSF GRFP fellow and fourth-year PhD student at the University of Michigan pursuing a joint doctorate in Mechanical Engineering and Scientific Computing. 

As a member of the UMich Uncertainty Quantification & Scientific Machine Learning Group, my research focuses on uncertainty quantification for scientific and engineering applications. Specifically, I am interested in accelerating computationally expensive problems such as Bayesian optimal experimental design (OED) via novel multi-fidelity methods. Through this work I have made several developments in topics such as Bayesian inference of covariance matrices, Bayesian optimization with noisy targets, and multi-fidelity stochastic optimization.

In addition, I have considerable experience working with experimentalists in solar panel materials discovery and battery modeling applications, where I have applied Machine Learning and OED methods to aid the experimentalists in characterizing existing data and designing new experiments.

I take great joy in learning across disciplines and solving difficult engineering problems via pragmatic decision making. Please do not hesitate to reach out for questions and collaborations!