Introduction
I am a Postdoctoral Fellow in the Institute for Foundations of Data Science at Yale University. I completed my Ph.D. in Applied Mathematics at Cornell University, where I was advised by Professor Yunan Yang. During my Ph.D., I was an NDSEG Fellow and completed research internships at Mitsubishi Electric Research Laboratories and Argonne National Laboratory. I previously earned B.A.s in Mathematics and Physics at Amherst College in 2021.
My research interests lie at the intersection of data-driven dynamical systems, measure transport, scientific machine learning, and inverse problems. My dissertation explored connections between dynamical systems and measure transport, spanning theory, algorithms, and applications for learning and reconstructing complex systems from noisy, partially observed, and distributional data. My ongoing work builds on this perspective, focusing on the design of statistically accurate surrogate models, as well as connections with generative modeling and measure-valued operator learning.
For more information, please navigate to any of the following pages: Research, Publications, CV, Talks, Teaching, Awards, and Outside of Research. If you are interested in my work or have any questions, please feel free to reach out to me at jonah.botvinick-greenhouse@yale.edu.
Recent News
- 08/26: I gave a contributed talk at the MSRNE Generative Modeling & Sampling Workshop.
- 07/26: I joined Yale’s Institute for Foundations of Data Science as a Postdoctoral Fellow.
- 07/26: Our paper, “AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition,” was published in Machine Learning: Science and Technology.
- 07/26: Our new preprint "Data Adaptive Learning of Dynamical Systems by Matching Transfer Operators and Invariant Measures" is available on arXiv.
- 06/26: I defended and submitted my dissertation, “Measure Transport for Data-Driven Dynamical Systems: Theory, Algorithms, and Applications.”