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Ahmed, Nisar Razzi Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Professor Nisar Ahmed’s research explores new algorithms and models for probabilistic reasoning that promote cooperative intelligence in mixed teams of humans and autonomous robotic vehicles. The COHRINT Lab blends this cutting-edge theory with real-world robotic software and hardware. Key problem areas for aerospace applications include: integrated sensing, perception, planning and control in human-robot teams; learning and prediction of human/autonomy decision making and task performance; and fusion of complex information in dynamic sensor networks for applications such as GPS-limited navigation and cooperative target tracking under uncertainty.

keywords

  • human-robot interaction, human-machine systems, probabilistic modeling, Bayesian methods, autonomous systems, estimation theory, sensor fusion, machine learning, system identification

Publications

selected publications

Teaching

courses taught

  • ASEN 3128 - Aircraft Dynamics
    Primary Instructor - Spring 2018 / Spring 2019 / Fall 2019
    Develops the fundamental concepts of aircraft dynamics. Covers flight mechanics, performance, dynamics and control of aircraft and how they impact aircraft design. Offered spring only.
  • ASEN 5044 - Statistical Estimation for Dynamical Systems
    Primary Instructor - Fall 2018
    Introduces theory and methods of statistical estimation for general linear and nonlinear dynamical systems, with emphasis on aerospace engineering applications. Major topics include: review of applied probability and statistics; optimal parameter and dynamic state estimation; theory and design of Kalman filters for linear systems; extended/unscented Kalman filters and general Bayesian filters for non-linear systems.
  • ASEN 6519 - Special Topics
    Primary Instructor - Spring 2019
    Reflects upon specialized aspects of aerospace engineering sciences. Course content is indicated in the online Schedule Planner. May be repeated up to 9 total credit hours. Recommended prerequisite: varies.

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