Hank Sha is the personal brand of an AI and machine learning engineer and UC Santa Barbara Statistics and Data Science graduate, focused on applied AI, forward-deployed engineering, and data science. He helps organizations build and ship LLM features and machine learning models, and develops agentic systems and AI products. His work spans research, startups, and independent projects, with an emphasis on practical implementation, responsible evaluation, and clear-eyed decisions about when machine learning is appropriate.

Hank’s projects include a live biotech intelligence engine that combines SEC filings, clinical trial information, FDA calendars, and news to surface potential catalysts; an NBA contract-value analysis tool; a graph-based note-taking app; and a command-line system of specialist agents for autonomous coding and review. His research explores AI safety, graph learning, and graph unlearning. Through internships and research roles, he has optimized model inference, built LLM-based developer-evaluation systems, and analyzed clinical trial data. He also shares technical writing, project work, and expertise in LLM engineering, machine learning, data analysis, and full-stack development.