Matroid-Constrained Approximately Supermodular Optimization for Near-Optimal Actuator Scheduling
Published in the proceedings of 2019 IEEE 58th Conference on Decision and Control (CDC), 2019
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Approximately Supermodular Scheduling Subject to Matroid Constraints
Published in arXiv preprint arXiv:2003.08841, 2020
Resilient Control: Compromising to Adapt
Published in 2020 IEEE 59th Conference on Decision and Control (CDC), 2020
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Matroid-Constrained Approximately Supermodular Optimization for Near-Optimal Actuator Scheduling
Published in the proceedings of 2019 IEEE 58th Conference on Decision and Control (CDC), 2019
Approximately Supermodular Scheduling Subject to Matroid Constraints
Published in arXiv preprint arXiv:2003.08841, 2020
Resilient Control: Compromising to Adapt
Published in 2020 IEEE 59th Conference on Decision and Control (CDC), 2020
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teaching
ESE 421: Control for Autonomous Robots
Undergraduate course, University of Pennsylvania, 2019
This course introduces the students to the hardware and software technology essential to autonomous ground vehicles. This fast paced course covers a breadth of robotics topics including kinematic modelling, feedback control, state estimation and sensing, and motion planning. Lectures are reinforced by hands-on laboratories where students apply the lessons from lecture to the implementation of their own self-driving cars.
ESE 605: Modern Convex Optimization
Graduate Course, University of Pennsylvania, 2020
This second year graduate course introduces students to the theory needed to recognize and solve convex optimization problems, particularly those that arise in engineering. The first half of this course covers essential theory including convex analysis, the identification of linear, quadratic, geometric, and semidefinite programs, and duality. The second half of the course emphasizes the practice of transforming a broad range of engineering objectives into convex programs such as statistical estimation, approximation, and control as well standard first and second order algorithms to solving both unconstrained and constrained convex optimization programs.