Roozbeh Farhoodi is a software engineer with 10 years of experience applying machine learning across academia and industry, currently optimizing ML accelerators for large models at Google Cloud. His background spans applied mathematics, deep learning, and computational neuroscience—he deployed large-scale visual models on cloud platforms to process 200TB+ datasets and improved runtimes by about 10% during his UCSF postdoc. At Celestial AI he bridged ML and hardware, developing benchmarks and memory-optimization modules that informed chip design and data-center appliances for LLMs and recommendation systems. He combines rigorous research (multiple postdocs and seven papers) with hands-on engineering, quickly learning new domains to deliver measurable performance gains. Outside work he’s an avid hiker and musician (piano, violin, tenor), a detail that underscores his mix of analytical precision and creative discipline.
10 years of coding experience
6 years of employment as a software developer
Ph.D. Research Intern Applied Mathematics Machine Learning & Neuroscience, Ph.D. Research Intern Applied Mathematics Machine Learning & Neuroscience at Northwestern University
Ph.D. Applied Mathematics, Ph.D. Applied Mathematics at Sharif University of Technology
Moment of truth for reliability of staining methods!
Contributions:9 commits, 8 pushes, 1 branch in 10 months
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