Jasmine Jerry Aloor

Jasmine Jerry Aloor is a PhD candidate in aeronautics and astronautics whose research bridges aerospace engineering, robotics, AI, and control. Supported by her second MathWorks Fellowship, she will pursue studies of system- and fleet-level objectives, including fairness and efficiency for highly decentralized execution settings, such as those enabled by multi-agent reinforcement learning (MARL) methods. Her previous projects include studies examining whether agents can learn to be fair, especially in scenarios where agents must safely navigate to perform a coverage task or get into formation. Jasmine demonstrated that by training agents using min-max fair assignments, agents can learn to balance the tradeoff between efficiency and fairness. The next steps of her research involve determining how to effectively combine the safety guarantees provided by control-theoretic techniques with MARL-based planning methods for aircraft trajectory waypoint generation. MathWorks products, including the Aerospace Blockset Toolbox and Simulink, are essential to her work. Jasmine’s research has the strong potential to advance the field of aerial mobility and to help ensure the efficient, safe, and equitable integration of autonomous aircraft into the National Airspace System.