Data scientist and ML researcher with experience in large-scale climate modeling, civic data, and scientific computing.

University of California, San Diego — M.S. Data Science (Expected June 2027)

University of California, Los Angeles — B.S. Atmospheric & Oceanic Sciences and Mathematics (June 2024)

jem-samudra-coupler (In Progress) — Adapter package that couples a JAX-based atmospheric general circulation model (JCM) with a PyTorch neural ocean emulator (Samudra) through the JEM coupling framework. Handles cross-framework data exchange (JAX ↔ PyTorch), grid regridding between spectral T31 and OM4 tripolar grids, and physical variable mapping for heat flux and wind stress boundary conditions.

Liver Cancer Spatial Epidemiology (In Progress) — Spatial Bayesian analysis investigating the relationship between environmental exposures and hepatocellular carcinoma (HCC) incidence across US geographies.

Graduate Researcher — UCSD Climate Analytics Lab (Sep 2025 – Present)

• Building ML pipelines predicting atmospheric variables from 60+ years of climate data using PyTorch, xarray, and Dask for distributed processing of 800GB+ datasets
• Developing a coupled atmosphere–ocean model integrating a JAX-based physics atmospheric model with a PyTorch ML ocean emulator

Data Scientist — Hack for LA (Aug 2024 – Sep 2025)

• Contributed to an interactive web app mapping 12.5 million parking citations; analyzed large datasets to surface trends and actionable insights

Undergraduate Researcher — UCLA (Mar 2023 – Sep 2023)

• Applied Lagrangian particle tracking to analyze Southern Ocean currents contributing to ice shelf melting

Undergraduate Researcher — Salk Institute for Biological Studies (Sep 2021 – Sep 2022)

• Analyzed olfactory data using hyperbolic geometry and Bayesian statistics

Python PyTorch JAX Xarray Dask Weights & Biases Hydra Pandas SQL R MATLAB

• 2nd Place — UCSD SMASH & NSF HDR ML Challenge Hackathon for Coastal Flooding (2026)
• 1st Place — Research Conference, San Diego Mesa College (2022)