About me

My name is Michael and I am a final year Ph.D. student at the University of Colorado, Boulder working under Prof. Jed Brown. Prior to beginning my Ph.D. I worked as a Data Scientist in industry for several years where I had the opportunity to work on a range of problems across the healthcare, financial services, and public sectors.

My research interests are broadly in machine learning and optimization. I enjoy investigating ways to learn from new types of data or developing algorithms that are especially data efficient for certain classes of problems. Right now, I’m especially interested in ML for physics-driven systems where we have prior knowledge of system behavior in the form of PDEs, invariances, or conservation laws.

Outside of work, I do a lot of climbing, running, and reading.

News

• 2023/12/18 - MPP won best paper at the NeurIPS 2023 Workshop on AI for Science!
• 2023/12/08 - Our paper on stability of neural operators was accepted to TMLR!
• 2023/10/09 - Released work on multiple physics pretraining with the PolymathicAI collaboration to arxiv!
• 2023/06/22 - Released joint work with Peter and Shashank from LBL on stability in autoregressive neural operators on arxiv!
• 2022/5/10 - Excited to be working with Lawrence Berkeley National Lab on deep learning based weather models this summer!
• 2021/9/28 - The updated version of our earlier workshop paper, now titled, “Learning to Assimilate in Chaotic Dynamical Systems” was accepted to NeurIPS 2021.
• 2021/5/21 - I will be working with Argonne National Lab as a Givens associate this summer!