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Prof. Xiaoli Bai

Principal Investigators

Rutgers University

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

  • The 2019 NASA Early Career Faculty award
  • The 2016 Air Force Office of Scientific Research Young Investigator Research Program award
  • Outstanding Young Aerospace Engineer Award from Texas A&M University in 2018
  • A. Water Tyson Assistant Professor Award from Rutgers in 2018
  • the American Institute of Aeronautics and Astronautics Foundation John Leland Atwood Graduate Award
  • Amelia Earhart Fellowship

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Education

  • 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

Projects


Quantify the performance uncertainty physics-based learning and fuse with classical orbit prediction

We have received a new award from AFOSR (Air Force Office of Scientific Research). The project focuses on two fundamental questions: …

Advanced Orbit Prediction for Resident Space Objects through Physics-based Learning

Supported by Air Force Fiscal Year 2016 Young Investigator Research Program.

Our Lab at Rutgers

Our lab is equipped with 8 Vicon Bonita 10 Camera: The B10 camera captures at 250 fps with one megapixel of resolution, featuring a …

Recent News

Featured by AFOSR

Professor Bai was featured by Air Force Office of Scientific Research (AFOSR).

See the original LinkedIn post here.

Fellow in 2021 Air Force Research Lab Summer Faculty Fellowship Program

Professor Bai has been awarded as a fellow in 2021 Air Force Research Lab Summer Faculty Fellowship Program at AFRL-Space Vehicles. About: The U.S. Air Force Research Lab Summer Faculty Fellowship Program offers hands-on exposure to Air Force research challenges through 8- to 12-week research residencies at participating Air Force research facilities for full-time science, mathematics, and engineering faculty at U.S. colleges and universities. For more information: https://afsffp.sysplus.com/ Awardees 2021: https://afsffp.

Professor Bai Named AIAA Associate Fellow

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

Professor Bai featured in The Daily Targum

The Daily Targum was founded in 1869, and is the second-oldest and among the largest college newspapers in the nation.

Here is the link to the article: Rutgers professor wins award from NASA

New AFOSR Grant

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!

Members

Current

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Daniel Gore

Undergraduate

Electrical engineering, Optics, Mathematical modeling

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Hao Peng

Postdoc

Aerospace engineering, Machine Learning, Artificial Intelligence, Three-body problem, Trajectory design, Optimization

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Rabiul Hasan Kabir

Ph.D. Student

Reinforcement Learning, Navigation and Control, UAV Control

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Siddharth Sambath Ramkumar

Undergraduate

Aerospace Engineering, Space Technology, Artificial Intelligence

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Yiran Wang

Ph.D. Candidate

Aerospace Engineering, Machine Learning, Artificial Intelligence

Graduate Students

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Rabiul Hasan Kabir

Ph.D. Student

Reinforcement Learning, Navigation and Control, UAV Control

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Yiran Wang

Ph.D. Candidate

Aerospace Engineering, Machine Learning, Artificial Intelligence

Undergraduates

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Daniel Gore

Undergraduate

Electrical engineering, Optics, Mathematical modeling

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Siddharth Sambath Ramkumar

Undergraduate

Aerospace Engineering, Space Technology, Artificial Intelligence

Alumni

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Dr. Gaurav Misra

Ph.D.

Space Robotics, Machine Learning, Satellite Navigation and Control

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Hongwei Yang

Associate Professor, Nanjing University of Aeronautics and Astronautics, China

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Jullian Rivera

Graduate Student, The University of Texas, Austin

Machine Learning, Satellite Navigation and Control

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Dr. Liyang Wang

Ph.D.

UAV Control

Join Us


You are encouraged to contact Professor Bai through xiaoli.bai@rutgers.edu directly once you are ready.

Please include your CV with an introduction of your research background and interests.

(Updated 08/28/2021)

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:

  • Have good background and strong interest in space science, astrodynamics, dynamical systems, and control.
  • Are self-motivated to do first-class research.
  • Are committed to getting a Ph.D.

Preferred knowledge includes:

  • Advanced controls, estimation, optimization
  • Analytical dynamics
  • Machine learning, statistics
  • Designing and building, and experiments with UAVs

The group’s current research interests include autonomous, intelligent, and informative observation planning for satellite constellations; space science with a focus on thermospheric density predictions based on solar eruptions; 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.

Sponsors

We gratefully acknowledge research support from these sponsors.