Researcher

Jeffrey Uhlmann

Also known as J. Uhlmann, J.K. Uhlmann, Jeff Uhlmann, Jeffrey K. Uhlmann

Jeffrey Uhlmann is a robotics researcher whose estimation work supports object tracking and SLAM. He began at the Naval Research Laboratory in 1987, earned a robotics doctorate at Oxford, and joined the University of Missouri in 2000. His research includes the Unscented Transform, Covariance Intersection, stable robotic control, and consistency methods for AI recommender systems. As of October 2026, he is an associate professor in electrical engineering and computer science at Missouri.

Works
169
Citations
22,747
h-index
27

Publication numbers from OpenAlex, as of 15 October 2026.

Timeline

  1. 1987

    Tracking research at NRL

    He begins research at the Naval Research Laboratory in 1987, working on tracking multiple objects. His education includes a robotics doctorate from Oxford, an MSc in computer science, and a BA in philosophy from Mizzou.

  2. 1995

    A nonlinear-filtering paper

    In 1995, he publishes “A New Approach for Filtering Nonlinear Systems” for the American Control Conference.

  3. 1999

    Testing covariance intersection on Mars

    In 1999, he coauthors “NASA Mars Rover: A Testbed for Evaluating Applications of Covariance Intersection.”

  4. 2000

    Joining the University of Missouri

    He joins the University of Missouri in Fall 2000.

  5. 2004

    The Unscented Transform

    In March 2004, he and Simon Julier publish “Unscented Filtering and Nonlinear Estimation” in Proceedings of the IEEE. The Unscented Transform applies nonlinear equations to sigma points to estimate a system’s transformed mean and covariance.

  6. 2007

    Covariance intersection for SLAM

    In 2007, he and Simon Julier publish “Using Covariance Intersection for SLAM” in Robotics and Autonomous Systems.

  7. 2019–2023

    Robotic control and AI recommendations

    In 2019, he and Bo Zhang publish “Applying a Unit-Consistent Generalized Matrix Inverse for Stable Control of Robotic Systems.” In 2023, he and Tung Nguyen describe consistency methods for AI-based SVD recommender systems, with experiments reporting improved RMSE and MAE and properties related to fairness and robustness.

  8. As of October 2026

    Associate professor at Missouri

    He is an associate professor in the David L. Payne Department of Electrical Engineering and Computer Science at the University of Missouri.

Sources and credits

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  1. First-Hand:The Unscented Transform ethw.org. Checked 15 October 2026.
  2. University of Missouri uhlmannj.mufaculty.umsystem.edu. Checked 15 October 2026.
  3. University of Missouri - PUBLICATIONS uhlmannj.mufaculty.umsystem.edu. Checked 15 October 2026.
  4. Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems arxiv.org. Checked 15 October 2026.
  5. Jeffrey Uhlmann engineering.missouri.edu. Checked 15 October 2026.