I am a computational biologist and software developer specializing in genome-scale metabolic modeling and high-performance computing. As the lead developer of BayFlux-v2.0, I built a scalable modeling platform that leverages Bayesian inference to predict metabolic fluxes with uncertainty quantification, enabling optimization of metabolic networks for applications such as biofuel production.
I have implemented two high-performance Multiple-Proposal Markov Chain Monte Carlo (MP-MCMC) algorithms in BayFlux to achieve:

• 50× speedup using sparse matrix optimization
• 100× speedup using PyTorch-based CPU acceleration.

Currently, I am optimizing BayFlux for GPU-based execution using CUDA and PyTorch to enable scalable simulations at unprecedented speeds. To support robust deployment, I designed a Docker-based pipeline tailored for HPC environments like NERSC, utilizing Shifter for containerized, reproducible workflows. I am also extending BayFlux to support high-throughput, cross-species metabolic simulations, including new organisms like Pseudomonas putida, thereby broadening its application in industrial biotechnology.
In addition to my work in biology, I have a strong foundation in computational condensed matter physics, where I used quantum chemistry and electronic structure methods to investigate material properties relevant to renewable energy and next-generation electronics. I am particularly passionate about integrating machine learning techniques into materials modeling to enable real-time screening and reduce dependence on resource-intensive simulations and experiments—an approach aligned with the UN Sustainable Development Goals on clean energy and equitable healthcare.
Outside of research, I enjoy traveling, experimenting with global cuisines through cooking, and watching science documentaries. I also serve on the board of the Berkeley Lab Postdoc Association (BLPA), where I lead the “Career Transitions Talk” series, manage newsletters, and oversee organizational finances.