Computational physicist and scientific software engineer applying machine learning to physical simulation problems. My work sits at the intersection of high-performance computing, numerical methods, and data-driven modeling.
I build things from the ground up: simulation codes, ML pipelines, and the infrastructure to connect them.
Scientific ML (ilwikak-ml) Applying ML to inverse problems in astrophysical plasma physics. Current project: using classical ML and Bayesian inference on the Fermi-LAT 4LAC-DR3 blazar catalog to recover physical jet parameters from observational data, revisiting and extending work from Rueda-Becerril et al. 2021.
HPC simulation software Author of Paramo, a Fortran numerical code for particle acceleration and radiation in relativistic plasma jets. Contributor to Tleco, its modern Rust/Python successor.
Atmospheric dispersion modeling Main developer of WindsOfChange, an R/C++ Lagrangian particle dispersion library for forest canopy environments, with OpenMP parallelization.
Languages: Python, C++, Fortran, R, SQL Scientific computing: NumPy, SciPy, JAX, Astropy, Pandas ML: scikit-learn, PyTorch, XGBoost HPC: OpenMP, MPI, SLURM Tools: Git, Docker,
- Rueda-Becerril et al. 2021 - Blazar jets launched with similar energy per baryon, independently of their power. MNRAS 501, 4092. doi:10.1093/mnras/staa3925
- Rueda-Becerril et al. 2017 - The influence of the magnetic field on the spectral properties of blazars. MNRAS 468, 1169. doi:10.1093/mnras/stx476
- Rueda-Becerril et al. 2014 - Synchrotron and synchrotron self-Compton emission from blazar jets. MNRAS 438, 1856. doi:10.1093/mnras/stt2335
Full list: ORCID 0000-0003-1988-1912
LinkedIn | Personal site | Seattle, WA