Dr. Xiaoli Bai is an Associate Professor in the department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey.
Dr. Bai obtained her PhD degree of Aerospace Engineering in 2010 from Texas A&M University. One consequence of her dissertation is a set of methods which significantly enhances and accelerates the fundamental processes underlying the creation and maintenance of space debris catalogs. Her current research interests include astrodynamics and Space Situational Awareness with a focus on the unstable and inactive space debris that are out of control and have uncertain origins; spacecraft guidance, control, and space robotics; and Unmanned Aerial Vehicle navigation and control.
Dr. Bai was a recipient of
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Ph.D. Aerospace Engineering
Texas A&M University
M.S. Navigation, Guidance, and Control
Beijing University of Aeronautics and Astronautics
B.E. Automatic Control
Beijing University of Aeronautics and Astronautics
Supported by NASA Early Career Faculty (ECF) Award.
We have received a new award from AFOSR (Air Force Office of Scientific Research). The project focuses on two fundamental questions: …
Supported by Air Force Fiscal Year 2016 Young Investigator Research Program.
Supported by Office of Naval Research.
Our lab is equipped with 8 Vicon Bonita 10 Camera: The B10 camera captures at 250 fps with one megapixel of resolution, featuring a …
Professor Bai has been elected to the grade of Associate Fellow for the Class of 2021 in the American Institute of Aeronautics and Astronautics (AIAA).
AIAA Associate Fellows are individuals of distinction who have made notable and valuable contributions to the arts, sciences, or technology of aeronautics or astronautics.
See the AIAA news: https://www.aiaa.org/news/news/2020/09/28/aiaa-announces-class-of-2021-associate-fellows
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Here is the link to the article: Rutgers professor wins award from NASA
We have received a new award from AFOSR. The project focuses on two fundamental questions: How can we quantify the performance uncertainty of the learning-integrated orbit predictions? How can we fuse the newly developed machine learning (ML) approach with the classical orbit prediction? Success of this project will create performance-guaranteed learning strategies, and enhance our SSA capabilities with safer, more robust, and higher accuracy orbit predictions. We appreciate the AFOSR for the continuous support!
Professor Bai is selected by NASA as one of nine Early Career Faculty at US Universities for Space Tech Research. Xiaoli Bai, Rutgers University in New Brunswick, New Jersey A Holistic Bayesian Framework for Intelligent Calibration of Constellations of Sensors Bai aims to enable autonomous space sensor calibration, both individually and collectively as a sensing network. This capability will provide richer information at higher resolutions than using an individual sensor on one spacecraft.
Today, Liyang successfully passed his Ph.D. defense for his dissertation, “Advanced Guidance and Navigation of Small UAVs under GPS-denied Environment with Experimental Validations”.
Congratulation Dr. Wang! We are proud of you.
You are encouraged to contact Professor Bai through xiaoli.bai@rutgers.edu
directly once you are ready.
Graduate assistant positions are available. Qualified candidates with degrees in aerospace engineering, electrical engineering, applied mathematics, automation, or closely related fields are encouraged to apply.
Preference will be given to students who:
Preferred knowledge includes:
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More information can be found at Rutgers Graduate and Professional Admissions website.
We gratefully acknowledge research support from the following sponsors.