Ding Chen

My research:

I recently completed my PhD in physics at UC Riverside. My research with the STAR experiment at RHIC focused on large-scale heavy-ion collision data analysis, anisotropic flow measurements, detector calibration, and comparisons with physics models.

My expertise is:

Scientific computing with C++ and Python, ROOT-based data analysis, distributed batch workflows, statistical analysis, data validation, and reproducible research.

A problem I’m grappling with:

How can we turn complex scientific analysis workflows into scalable, reliable, and reproducible data and machine-learning systems?

I’ve got my eyes on:

AI for science, machine-learning systems, scalable data pipelines, and scientific software that helps researchers test ideas more rapidly.

I want to know more about:

Parallel programming, performance optimization, modern HEP software workflows, collaborative development, and practical machine-learning tools for scientific computing.