KJC (she/her)
Mesbah Lab @ UC Berkeley. NASA Ames Research Center. Berkeley, CA, USA.

website update in progress
Kimberly is from a suburb just south of Atlanta, Georgia. She graduated with a Bachelor of Science from the Georgia Institute of Technology with Highest Honors, majoring in Chemical & Biomolecular Engineering and minoring in Scientific and Engineering Computing (under the College of Computing).
At Georgia Tech, Kimberly worked as an undergraduate teaching assistant, a peer tutor, and an undergraduate researcher. As an undergraduate researcher, she worked in Professor Martin Maldovan’s group with Dr. Juan Manuel Restrepo-Florez, where she worked on modeling a pipe-in-pipe reactor using COMSOL for the production of syn gas.
Kimberly is now attending the University of California, Berkeley to pursue a Doctor of Philosophy in Chemical & Biomolecular Engineering. She works with Professor Ali Mesbah where her research focuses on predictive control, optimization, and machine learning for cold atmospheric plasma jets in biomedical applications. Details found under Projects
In January 2023, Kimberly also started as a Pathways Intern at the National Aeronautics and Space Administration (NASA) Ames Research Center. At Ames, she works with Dr. Jeremy Coupe on machine learning methods with graph representations for air traffic management. Details found under Projects
news
Nov 29, 2023 |
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Aug 12, 2022 |
Kimberly passed her qualifying exam and will be advancing to candidacy ![]() ![]() |
Jan 19, 2020 | Kimberly started working with Prof. Ali Mesbah at the University of California, Berkeley to complete her PhD in Chemical and Biomolecular Engineering. |
Nov 7, 2015 | A long announcement with details |
selected publications
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Safe explorative Bayesian optimization – Towards personalized treatments in plasma medicineIn Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), 2023
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Towards personalized plasma medicine via data-efficient adaptation of fast deep learning-based MPC policiesIn Proceedings of the American Control Conference (ACC), 2023
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Data-driven adaptive optimal control under model uncertainty: an application to cold atmospheric plasmasIEEE Transactions on Control Systems Technology, 2022
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Deep learning-based approximate nonlinear model predictive control with offset-free tracking for embedded applicationsIn Proceedings of the American Control Conference (ACC), 2021