I'm an astrophysicist and data scientist at Lawrence Berkeley National Lab (LBNL), working with the Dark Energy Spectroscopic Instrument (DESI) collaboration. As one of the builders and 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 on the planet.

• Analyze massive time-series-like data to detect features and patterns. Helped build the largest 3D map of our Universe (as part of DESI team)
• Develop efficient, scalable algorithms for:
  • Multi-class classification of noisy data, designing and maintaining parallelized I/O pipelines
  • Parallelizable ML and statistics-based methods to detect patterns and local feature detection
  • 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 CPU clusters
• Study the formation and evolution of galaxies through observational and computational probes

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 developer and 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
• desihub: Primary codebase of DESI survey

Thanks for visiting! Have a great day!