Jingxuan Liu

Jingxuan Liu has a diverse range of work experience in the engineering field. Jingxuan started their career as an Intern Application Engineer at Applied Dynamics International, where they implemented a 6-DOF dynamic simulation of a business jet and built a navigation algorithm. Jingxuan then worked as an Undergraduate Research Assistant at the Autonomous Aerospace Systems Lab, where they built a dynamic model for a parafoil and wrote PID control to stabilize the flight of the parafoil robot.

Afterward, Jingxuan joined Eaton as a Corporate Research and Technology (CRT) Intern, where they invented a new stress detection method for VCB and filed a patent disclosure. Jingxuan also worked as a Graduate Student Research Assistant at the Automotive Research Center, where they improved the dynamic prediction accuracy of an unmanned ground vehicle on rainy and snowy roads.

Jingxuan then joined Cummins Inc. and held various roles. As a Product Validation Engineer, they gained experience in generator set control and familiarized himself with engine control algorithms. Jingxuan also worked as a Control and Diagnostic Research Engineer, where they developed knock detection algorithms for natural gas engines and contributed to the development of the next-generation engine control.

Most recently, Jingxuan joined TuSimple as a Control Research Engineer for Vehicle control algorithm. Overall, their work experience showcases their expertise in control systems, diagnostics, research, and algorithm development in the automotive and engineering industries.

Jingxuan Liu has a solid education background in engineering. Jingxuan obtained a Bachelor's Degree in Mechanical Engineering from Shanghai Jiao Tong University in 2014. During their time at the same university, they also pursued a Bachelor's degree in Aerospace Engineering at the University of Michigan from 2012 to 2014.

Jingxuan later pursued further education and completed an M.S.E Degree in Aerospace Engineering from the University of Michigan between 2014 and 2016. Additionally, they also obtained an M.S.E Degree in Mechanical Engineering from the same institution during the same period.

Apart from their formal education, Jingxuan has also acquired various certifications. Jingxuan obtained a C++ certificate from Udacity in March 2021. In 2019, they completed the Self-Driving Car Engineer Nanodegree program offered by Udacity. Before that, in 2018, Jingxuan obtained certifications in the Deep Learning Specialization, Sequence Models, and Convolutional Neural Networks from Coursera. In 2017, they completed courses in Structuring Machine Learning Projects, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, and Neural Networks and Deep Learning, also from Coursera.

Overall, Jingxuan Liu's education history showcases their commitment to technical disciplines, particularly in the areas of Aerospace and Mechanical Engineering, along with their continued professional development through online certifications in programming and machine learning.