E5, U. Waterloo
My research interests lie in using elements of Control Theory, Formal Methods, Machine Learning and Optimization to solve problems in Safe Autonomy of Robotic and other Cyber-Physical Systems, and Human-Robot Interaction. [Curriculum Vitae]
Prior to joining the University of Waterloo, I was a Postdoctoral Fellow in the EECS Department at UC Berkeley. As a part of the VeHICaL project, I was mentored by Professors Sanjit Seshia (primarily) and Bjoern Hartmann.
I received my PhD in Electrical Engineering from the University of Pennsylvania, where I worked on developing Robust Predictive Methods for Planning and Control of Autonomous Systems as a part of Prof. Rahul Mangharam’s Real-Time and Embedded Systems Lab.
Computationally tractable solutions, that potentially solve real problems, are a recurring theme across my research. More often that not, the algorithms we develop are implemented on actual Cyber-Physical Systems, some of which are: multiple Crazyflie 2.0 quad-rotors, a 1/10th scale autonomous car, a hex-rotor platform, a hardware-in-the-loop vehicle ECU simulator, a scaled down hybrid powertrain for Electric Vehicles, and a self-balancing, fully enclosed electric motorcycle.
|Jun 15, 2021||Our paper on a Signal Temporal Logic-based framework for safety of Autonomous UAVs in Urban Air Mobility missions has been accepted for publication in the Transportation Research Part C: Emerging Technologies, special issue on Embracing Urban Air Mobility.|
|Apr 29, 2021||Our paper on decentralized path planning with line-of-sight constrained communication between agents, led by Victoria Tuck, has been accepted at the IEEE Conference on Control Technology and Applications (CCTA), 2021.|
|Feb 4, 2021||Our paper on Learning-based Decentralized Multi-UAV Collision Avoidance will appear in the ACM Transactions on Cyber-Physical Systems.|
|Feb 1, 2021||Two of our papers have been accepted at the 2021 American Control Conference (ACC): one on co-designing the planner and controller for UAVs with Signal Temporal Logic objectives, and another on Counter-Example Guided Synthesis for systems with black-box learning-based perception modules.|
|Oct 18, 2020||Our paper (led by Kuk Jang) won the best paper in session (UAV conflict management) award at DASC 2020.|
- Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic ObjectivesIn Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2018
- Anytime Computation and Control for Autonomous SystemsIEEE Transactions on Control Systems Technology 2020
- Learning-’N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance SchemeACM Transactions on Cyber-Physical Systems 2021