Changan Chen
Co-Founder And Chief Research Officer at Rhoda AI
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChangan Chen is an AI researcher and entrepreneur with a decade of experience bridging academic rigor and product-focused research in computer vision, robotics, and machine learning. As Co-Founder and Chief Research Officer at Rhoda AI and a former FAIR researcher and Stanford postdoc with a PhD from UT Austin, he combines deep research credentials with hands-on engineering. His contributions to crowd-aware robot navigation — including implementing CrowdSim and ORCA policies for reinforcement-learning based navigation — reflect practical expertise in simulation, training, and evaluation of embodied agents. Based in Palo Alto, he excels at turning state-of-the-art research into deployable systems and is comfortable operating at the intersection of research, open-source projects, and startup strategy.
10 years of coding experience
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Simon Fraser UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at AustinBachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Zhejiang UniversityPostdoctoral Researcher, Postdoctoral Researcher at Stanford University
English, Chinese
Github Skills (3)
machine-learning10
reinforcement-learning10
python10
Programming languages (9)
Github contributions (5)
vita-epfl/CrowdNav
Jul 2018 - Mar 2021
[ICRA19] Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 147 commits, 6 PRs in 2 years 8 months
Contributions summary:Changan implemented and modified core components of a crowd-aware robot navigation system based on deep reinforcement learning. They added a new environment class (`CrowdSim`) that defines the simulation environment for agents (pedestrians and a navigator). Further work included adding an `ORCA` policy for robot navigation, and a testing setup, encompassing the creation of testing scenarios and the implementation of reward functions, indicating involvement in the training and evaluation aspects of the navigation models.
robot-navigationreinforcement-learningdeep-reinforcement-learningrobotcollision-avoidance
facebookresearch/sound-spaces
Aug 2020 - Jan 2023
A first-of-its-kind acoustic simulation platform for audio-visual embodied AI research. It supports training and evaluating multiple tasks and applications.
Contributions:3 releases, 82 commits, 1 PR in 2 years 5 months
embodied-aisimulationaudiotrainingacoustic
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