Summary
Jelani Nelson is a leading theoretical computer scientist and academic leader based in Berkeley, CA, specializing in algorithms for massive data—particularly sketching, streaming, random projections, and privacy in distributed settings. As Department Chair and Professor in EECS at UC Berkeley and a research scientist at Google, he blends deep theoretical insight with practical impact on large-scale linear algebra and machine learning systems. His career spans top institutions (MIT PhD, Harvard faculty, IAS, MSRI) and industrial research stints, reflecting rare fluency between pure theory and applied research. Nelson is known for making memory- and communication-efficient algorithms that scale to real-world data streams, and for advancing local differential privacy models—work that often informs practical systems beyond academia. An eight-year industry-academic hybrid with a track record of leadership, he brings both administrative stewardship and hands-on research rigor to large, interdisciplinary teams.
8 years of coding experience
7 years of employment as a software developer
Ph.D. Computer Science, Ph.D. Computer Science at Massachusetts Institute of Technology
Grade School, Grade School at All Saints Cathedral School