I'm an astrophysicist and data scientist at Lawrence Berkeley National Lab (LBNL), working with the Dark Energy Spectroscopic Instrument (DESI) collaboration. As a core member of DESI's data systems, management, and science teams, I explore the cosmos using one of the most ambitious and extensive spectroscopic surveys to date.
- Analyze massive spectroscopic datasets of galaxies and quasars from surveys like DESI
- Develop efficient, scalable algorithms for:
- Spectral reduction and continuum fitting
- Modeling galaxy spectra and redshift estimation
- Statistical inference and physical interpretation
- Combine machine learning (PCA, NMF, regression, statistical learning) with physical models
- Work in Python, Jupyter Notebooks, Bash scripting; experienced with HPC, Slurm, and GPU clusters
- Study the formation and evolution of galaxies through emission and absorption features
I'm always excited to collaborate on data science and astronomy projects — whether academic or applied. I'm particularly interested in cross-disciplinary work where physical modeling meets statistical learning.
I'm an active contributor to the Dark Energy Survey Instrumentation (DESI) GitHub Organization, where I’ve helped resolve issues and open pull requests across multiple key repositories:
- redrock: Spectral redshift fitting for DESI
- desispec: Spectroscopic pipeline and tools for DESI
- desisurveyops: Survey operations, planning, and observing support for DESI
Thanks for visiting! Have a great day!