Summary
Alvin Tan is a fifth-year EECS PhD student at UC Berkeley who designs communal cyber-physical systems that improve quality of life with minimal attention overhead. With a BS (summa cum laude) in computer engineering, economics, and mathematics from Northwestern and nine years of hands-on experience, his work spans privacy-preserving energy monitoring, uncertainty-aware social robot navigation, context-enabled ML, and secure ubiquitous sensor networking. He has publications at IEEE S&P, ICRA, ICRA arts gallery, ICRA/HumanSys, and ICRA/ICRA-adjacent venues, reflecting a blend of technical rigor and interdisciplinary curiosity. Past internships at JHU APL and Oak Ridge delivered applied ML and high-performance computing results, including NeurIPS workshop and 150x HPC speedups. Alvin plans to continue research-focused roles in industry, national labs, or academia, and is notable for treating low-fidelity, power-constrained data as an opportunity for honest, human-centered system design.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Electrical Engineering and Computer Science , Doctor of Philosophy - PhD Electrical Engineering and Computer Science at University of California, Berkeley
Bachelor of Science - BS Computer Engineering Economics Pure Mathematics, Bachelor of Science - BS Computer Engineering Economics Pure Mathematics at Northwestern University
English, Chinese, German