Wanxin Jin
Assistant Professor at Arizona State University
Tempe, Arizona, United States
Summary
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Senior🎓
Top SchoolWanxin Jin is a tenure-track Assistant Professor at Arizona State University developing robot interactive intelligence that enables seamless human-robot interaction and dexterous object/environment manipulation. With a PhD in Control and Autonomy from Purdue and a decade of research experience spanning the University of Pennsylvania and Technical University of Munich, she blends model-based control and optimization with data-driven machine learning to get the best of both worlds. Her work sits at the intersection of control theory and modern learning methods, focusing on provable, practical approaches for real-world robotic behavior. She brings a strong experimental and theoretical toolkit, translating complex control algorithms into systems that operate robustly around people. Based in Tempe, AZ, she is building a research program that emphasizes interaction fidelity and manipulation competence rather than incremental improvements in isolated benchmarks.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Control and Autonomy, Doctor of Philosophy - PhD, Control and Autonomy at Purdue University
Stackoverflow
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Github Skills (46)
imitation-learning10
dynamical-systems10
reinforcement-learning-environments10
trajectory-optimization10
control-systems10
motion-planning10
system-identification10
symbolic-manipulation9
reinforcement-learning9
octave9
constrained-optimization9
nonlinear9
matlab9
safety-critical9
code-generation9
Programming languages (5)
Github contributions (5)
wanxinjin/Safe-PDP
May 2021 - Jul 2022
Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.
Contributions:10 commits, 8 pushes, 1 branch in 1 year 1 month
differentiable-programmingsafety-criticalsafe-reinforcement-learningsafe-controlsafety-critical-systems
wanxinjin/Pontryagin-Differentiable-Programming
Jan 2020 - Jun 2022
A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.
Contributions:34 commits, 36 pushes, 4 branches in 2 years 6 months
differentiable-programmingend-to-end-learninginverse-reinforcement-learninglearning-controltrajectory-optimization
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