I am a final-year Ph.D. candidate in computer science at the University of Chicago, advised by Rebecca Willett. For part of my PhD, I was supported by the NSF Research Traineeship in AI-enabled Molecular Engineering of Materials and Systems for Sustainability. Before my PhD, I was a data scientist working with Haky Im.

Research Interests

I am broadly interested in scientific computing and machine learning for the physical sciences. My current work uses fast direct partial differential equation (PDE) solvers to accelerate data-driven problems in scientific imaging and biological modeling. Here are a few specific interests:

• Nonlinear inverse problems in scientific imaging. I’m interested in inverse imaging problems where the forward model is nonlinear; in many settings the forward model is described by a PDE. This nonlinearity can make optimization formulations of the problem challenging. I am particularly interested in settings where data and fast direct solvers can be leveraged together to improve optimization and uncertainty quantification. Applications include seismic imaging, nondestructive testing, medical imaging, and computational imaging.
• Hardware acceleration for fast, direct PDE solvers. The most promising algorithms available for building fast direct PDE solvers often have hierarchical or other structures which make hardware acceleration challenging. It’s an interesting and important question to design variants of these algorithms for fast execution on hardware such as general-purpose GPUs. Part of this work involves developing reliable software packages; see my Software page for my contributions to open-source software projects.
• Biophysical modeling. I am interested in using fast direct solvers to model biophysical phenomena at a range of length scales, from proteins to organs.
• Medical imaging. I am seeking collaborations in this field. Please reach out!