I am a Ph.D. student in Computational & Applied Mathematics at University of Chicago. Advised by Prof. Lek-Heng Lim, I am interested in the two-way traffic between computation and geometry.

• Geometric structures play a crucial role in real-world applications, including numerical analysis, statistics, physics, and engineering. How can we efficiently compute fundamental geometric objects to facilitate automated computation and optimization on these spaces using numerical linear algebra?
• Concepts from geometry, such as equivariance and homology, have already demonstrated their potential in fields like machine learning and data analysis. What are some novel applications of these tools in real-world problems? What other mathematical structures can be explored to further enhance computational methodologies?