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My group’s work is on the theoretical and algorithmic aspects of design and verification of autonomous systems. It embraces the fact that autonomy does not fit traditional disciplinary boundaries, and has made numerous contributions in the intersection of formal methods, controls and learning:
  • Formal methods + controls: The methods we have developed address automated synthesis of control protocols that rely on integration of physical laws and software principles to serve in adversarial environments subject to rich temporal-logic-like specifications.
  • Learning + formal methods: The central question is how we can develop autonomy protocols that not only learn from their interactions with the environment and users but also provably satisfy high-level safety and performance specifications.
  • Controls + learning: The main question is how we can guarantee safety and robustness feedback control systems that integrate learning-enable components, e.g., classifiers, in the loop.

In addition to the applications in aerial and ground vehicles (and robots), we interpret autonomy broadly with other emerging applications in networks on large-scale aerospace systems and additive manufacturing.


We create our particular projects often by abstracting problems from one or more pressing needs in autonomous systems. The current needs my group’s work addresses include (i) the verifiability of autonomous systems; (ii) their adaptability using data at design- and run-time; (iii) their co-work with human users; (iv) the explainability of autonomous decisions, potential failure reasons and necessary fixes to users and designers; and (v) the limitations faced by autonomous systems due to imperfect sensing and perception.