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I'm a Master's student in the Intelligent Control Lab at Carnegie Mellon University Robotics Institute. I am extremely fortunate to be advised by Professor Changliu Liu and Professor John Dolan. I work on safe control and motion planning, with quadruped and drone applications. My goal is to create safe and efficient autonomous systems that can operate in complex and dynamic environments. Prior to CMU, I earned my B.S. (2023) from UC Berkeley with a major in Mechanical Engineering and a minor in EECS. During my time at Berkeley, I did safe reinforcement learning research under Professor Koushil Sreenath in the Hybrid Robotics Group. Outside of school, I do kendo, scuba diving, and rock climbing. Email / CV / Google Scholar / Github |
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In reverse chronological order:
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Simin Liu*, Kai S. Yun*, John M. Dolan, Changliu Liu Published in European Control Conference (ECC), 2024. IEEE | Arxiv We investigate controller synthesis for dynamical systems with uncertain parameters. We designed an optimization algorithm for generating robust-adaptive safe controllers that can guarantee safety in the presence of uncertainties, without being overly conservative. Our controller performs 55% better compared to popular robust controllers. |
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Tianhao Wei, Luca Marzari*, Kai S. Yun*, Hanjiang Hu*, Peizhi Niu*, Xusheng Luo, Changliu Liu Under review at International Conference on Computer Aided Verification (CAV), 2024. Arxiv | GitHub We introduce a new comprehensive toolbox for formally verifying deep neural networks. ModelVerification.jl is a Julia package (with Python interface) that provides a wide range of state-of-the-art verification algorithms for various deep neural networks. This toolbox is designed to be user-friendly and efficient, and it is open-source. |
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Jack C. Harms, Ethan M. Grames, SirkHoo Yun, Bushra Ahmed, Leah C. O'Brien, James J. O'Brien Journal of Molecular Spectroscopy, 2019. Journal of Molecular Spectroscopy The previous literature on the electronic spectrum of Copper-63 Oxide is extended to Copper-65 Oxide. We combine the analysis of A-X and Y-X electronic systems of Copper-65 Oxide, using the mass-independent Dunham fit with PGOPHER software to obtain molecular constants. Moreover, Copper-isotope field-shift is corrected to the electronic exictation energy required in the fit of Y-X system. |
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Tesla, Inc., Vehicle Dynamics Team
Vehicle Dynamics / Software Engineering Intern • May 2022 to August 2022 |
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NeuroCore.ai., Reinforcement Learning Team
Reinforcement Learning Research Intern • October 2020 to July 2021 |
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