With a broad interest in applying AI to science and engineering problems, I am currently focusing on the development and evaluation of machine learning-assisted protein engineering tools as a Bioengineering Ph.D. student at Caltech, co-advised by Frances Arnold and Yisong Yue. My current main project involves developing zero-shot predictors for non-native enzyme activity prediction, building on my recent work, Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes. I have also worked with Kevin K. Yang, Alex X. Lu, and Ava P. Amini through my summer internship at Microsoft Research New England on Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models, which was presented at ICML 2024.

I am passionate about making computational tools usable and accessible to the wet-lab scientists and engineers. In collaboration, I contributed to developing data analysis and interactive visualization software for Long-read every variant Sequencing (LevSeq), a newly developed nanopore-based technology for rapid protein sequencing.

During and shortly after my time at the University of California, Berkeley where I earned my B.S. in Bioengineering and my B.S. in Chemical Biology, I gained experience in various research areas: developing RNA-seq software tools at Zymergen, discovering genetic circuit components with Richard Murray, contributing to cancer immunotherapy and SARS-CoV-2 antibody therapeutics development with Shohei Koide,optimizing cell-free platforms at Tierra Biosciences, and undertaking metabolic engineering and synthetic biology tool building projects at the Dueber Lab.

Outside of research, I enjoy being active outdoors, experiencing diverse cultures, solving fun puzzles, and doing minimalism iPhoneography. Given my personal background and journey, I am committed to promoting equitable opportunities and individualized education in STEM through mentoring, teaching, and outreach volunteering.

Download my resumé .