Sangjae Bae
Principal Scientist at Honda Research Institute USA, Inc.
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolSangjae Bae is a Principal Scientist at Honda Research Institute USA with 10 years of experience applying optimization, control, and learning to robotics and automated driving. He progressed internally from research intern to principal scientist, leading work in motion planning, risk- and cooperation-aware decision making, and system modeling that bridges human behavior and autonomous systems. His academic work at UC Berkeley combined smart-grid and EV market strategies with practical tools—like a mobile virtual test-drive app—and informed his approach to uncertainty and optimal control. Based in the San Francisco Bay Area, he blends rigorous PhD-level research with hands-on implementation in industry, frequently translating theoretical advances into deployable algorithms. An early career in energy systems and a stint in the Republic of Korea Air Force add uncommon perspective on large-scale system dynamics and disciplined engineering practice.
10 years of coding experience
9 years of employment as a software developer
M.S./Ph.D, Systems Engineering, M.S./Ph.D, Systems Engineering at University of California, BerkeleyBachelor of Science (BS), graduated with highest honor (1st/250+), Electronics Engineering, Bachelor of Science (BS), graduated with highest honor (1st/250+), Electronics Engineering at Kyung Hee UniversityExchange program, Electrical and Electronics Engineering, Exchange program, Electrical and Electronics Engineering at Linköping University
Korean
Github Skills (18)
energy10
logistics9
simulation9
agent-based-modeling9
mobility8
modeling8
modelica8
vehicle-routing-problem8
operation7
station6
autonomy4
generative-adversarial-network2
generative2
pytorch1
visualization1
Programming languages (2)
Github contributions (5)
bsj1216/bsj1216.github.io
Aug 2016 - Nov 2017
Contributions:94 pushes, 1 branch in 1 year 3 months
bsj1216/sgan
Jun 2019 - Jul 2019
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
Contributions:51 pushes in 1 month
pytorchdeep-learningadversarialgenerative-adversarial-networkgan
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