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About

Parasara Sridhar Duggirala is a leading researcher in the verification and safe operation of cyber-physical systems (CPS), with a particular focus on autonomous vehicles and robotics. His work bridges formal methods, runtime monitoring, and machine learning to ensure that complex, timing-sensitive systems behave correctly even under uncertainty. A major contribution is his foundational work on the static and dynamic analysis of timed distributed traces (2012, 25 citations), which introduced algorithms for checking global predicates from potentially inaccurate timestamps—a critical capability for real-world CPS like mobile phones and robots. He has also advanced statistical verification techniques for autonomous controllers under timing uncertainties (2024) and developed interpretable frameworks that trade off robot task accuracy for compute efficiency (2021, 5 citations). More recently, his research on safety-driven DNN sizing for vehicular CPS (2025) addresses the challenge of using deep neural networks for perception in autonomous vehicles while maintaining safety guarantees. Duggirala co-authored the influential tutorial on hybrid systems tools C2E2, HyST, and TuLiP (2016, 6 citations), which has become a key resource for practitioners. With a growing citation record and a focus on practical, scalable solutions, his work is shaping the next generation of safe and efficient autonomous systems.