Research
My current projects include (1)
developing physics-informed machine learning models for solving partial differential equations (PDEs), with applications to accelerating computational fluid dynamics simulations of wind farms (2) designing ML-based power flow solvers using graph learning techniques. Beyond these projects, I am also interested in exploring problems related to weather forecasting and carbon capture.
Key themes that I explore through my research include:
- Incorporating physics and domain structure into ML models through soft and hard constraints, operator learning, and graph-based methods.
- Taking inspiration from numerical solvers (e.g., spectral and multigrid methods) to inform the design of these models.
- Developing models that are accessible even in compute-constrained and low-data regimes (e.g., via self-supervised training).
About Me
I completed my undergraduate degree at Brown University, where I majored in Physics and Applied Mathematics, followed by a Master’s in Electrical and Computer Engineering at Cornell Tech. As an undergrad/master’s student, I engaged in a broad range of research experiences involving scientific computing.
At Brown, I studied superconductivity in 2D graphene systems using experimental methods and Density Functional Theory (DFT) simulations in the
Low-Dimensional Electronics Lab
(advisors: Jia Li, Brenda Rubenstein). In addition, I developed physics-informed neural networks as part of the
The Crunch Group
(advisor: Somdatta Goswami).
I also had the opportunity to intern at
Microsoft Research
(advisor: Ranveer Chandra) where I worked on creating datasets for food protein property prediction, and
startups such as Aqemia (drug discovery) and
Transcelestial (wireless laser communications).
Outside of work, I love singing, playing piano, composing music, and reading poetry.
I’ve recently started exploring cooking and creating new recipes.
I also enjoy film photography, and you can find some of my photos
here!
