About me

I am currently working as a Machine Learning Scientist for Chemistry at Entalpic, where I lead the effort to develop an active learning pipeline for chemistry. Together we are combining AI, computational chemistry, and experimental labs to discover novel materials and accelerate materials discovery.

I completed my PhD at Carnegie Mellon University, advised by John Kitchin and Zachary Ulissi. My research focused on active learning and transfer learning techniques to adapt large-scale graph neural networks to low-resource problems in catalysis. I developed machine learning frameworks capable of accelerating molecular simulations of catalytic systems by orders of magnitude, with the aim of helping to address societal energy and environmental challenges, particularly climate change. I collaborated with the FAIR Chemistry Open Catalyst Project to make use of large graph neural networks to discover new catalysts for renewable energy storage. I also developed uncertainty quantification approaches for graph neural networks which improved their reliability and trustworthiness.

I completed my Bachelor’s of Science in Chemical Engineering, with a minor in Computer Science, at Iowa State University in 2019. There I was involved in researching carbon nanotube biosensors advised by Nigel Reuel I also participated in research into using novel MOFs for carbon capture, which were deployed on the NASA Artemis missions as part of the NASA X-Hab Academic Innovation Challenge.

In my free time I enjoy spending time with family and friends. I love board games and outdoor activities – my favorites being Race for the Galaxy and canoeing. And I always appreciate the opportunity to try a new IPA or sour.

If you have questions about my research or want to collaborate on anything, feel free to reach out to me via email.

Recent News

• March 2025 I started a new position as a Machine Learning Scientist for Chemistry at Entalpic in Paris!
• February 2025 I successfully defended my PhD and graduated from Carnegie Mellon University. Big thanks to everyone I worked with during my time there, especially my advisors John and Zack!
• October 2024 Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis preprint is up on arXiv.
• July 2024: Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances preprint is up on arXiv.
• November 2023: My work on Predictive Uncertainty Quantification for Graph Neural Network Driven Relaxed Energy Calculations accepted to NeurIPS 2023 AI4Science workshop, and I will presenting a poster there in December.
• November 2023: Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials preprint is up on arXiv.
• October 2023: Finished my internship with FAIR at Meta AI, working on uncertainty quantification for the Open Catalyst Demo. Thanks to all the amazing people there!
• June 2023: Open Catalyst Challenge announced for NeurIPS 2023
• January 2023: Wishing all the best to my advisor, Zack Ulissi, as he transitions to the FAIR Chemistry team at Meta AI. Looking forward to working with my new primary advisor, John Kitchin!
• December 2022: Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials paper is published in Machine Learning: Science and Technology.
• November 2022: Gave a talk on my work on Finetuna and nudged elastic band methods at the AIChE annual meeting in Phoenix.
• September 2022: Finetuna paper is published in Machine Learning: Science and Technology.
• June 2022: Open Catalyst Challenge is announced for NeurIPS 2022!
• May 2022: Gave a talk at the 27th North American Catalysis Society Meeting on Accelerating Geometric Optimizations By Finetuning Open Catalyst Project Models with Active Learning
• November 2021: Presented a poster at AICHE 2021 on Accelerating on-the-Fly Active Learning of Catalyst Simulations By Leveraging the OC20 Dataset of Adsorbate Relaxations.
• June 2021: Open Catalyst Challenge announced for NeurIPS 2021