About Me

I’m a second-year PhD student in the Applied and Computational Mathematics department at Caltech, advised by Professor Joel Tropp. My research focuses on the intersection of randomized numerical linear algebra (rNLA) and large-scale data science, with an emphasis on random tensors.

I am currently funded through the National Science Foundation Graduate Research Fellowship, and the Kortschak Scholars Fellowship.

Research Interests

• Randomized Numerical Linear Algebra: matrix sketching, fast linear solvers, randomized eigensolvers, algorithm acceleration.
• Tensor Networks and Quantum Mechanical Simulation: MPO-MPS contraction, randomized methods for tensor networks, efficient Hamiltonian time evolution.
• Approximate Gaussian Processes: sub-cubic gaussian process models, inducing point approximations, conjugate gradient methods for kernel inversion.

Recent Work

News

• [Oct. 2025] Attended the Simon’s Institute workshop on Linear Systems and Eigenvalue Problems, presenting a poster on sparse and tensor sketching.
• [Aug. 2025] Attended the Institute of Pure and Applied Mathematics (IPAM) RNLA workshop on randomized Krylov methods.
• [Apr. 2025] Invited to speak at Southern California Applied Math Symposium on randomized tensor networks.
• [Apr. 2025] Invited to speak at UCSD Mathematics of Information, Data, and Signals Seminar on randomized tensor networks.
• [Feb. 2025] Invited to speak at the Argonne National Laboratory Toward Next-Generation Ecosystems for Scientific Computing workshop on randomized tensor networks.