Alvin Tan
Graduate Student Researcher
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolAlvin 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, BerkeleyBachelor of Science - BS Computer Engineering Economics Pure Mathematics, Bachelor of Science - BS Computer Engineering Economics Pure Mathematics at Northwestern University
English, Chinese, German
Github Skills (26)
stem10
signal-processing9
infrared9
imaging9
fft9
microscopy9
stm9
scanning9
statistics8
visualization8
machine-learning7
dlib6
python6
computer-vision5
robot5
Programming languages (5)
Github contributions (5)
chpmk98/TownHall
Feb 2018 - Aug 2020
Contributions:118 commits, 105 pushes, 1 branch in 2 years 6 months
chpmk98/GroupNav
Apr 2021 - Sep 2021
[ICRA19] Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
Contributions:47 pushes, 1 branch in 5 months
deep-reinforcement-learning
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