Tommaso Dreossi

Papers

Total Citations

172

H-Index

About

Tommaso Dreossi is a leading researcher at the intersection of formal methods, machine learning, and cyber-physical systems, with a particular focus on ensuring the safety and reliability of autonomous systems. His most impactful contribution is the development of **Scenic**, a probabilistic programming language for scenario specification and data generation. This powerful tool, detailed in his highly cited 2022 paper (83 citations), enables engineers to systematically design, test, and train systems—especially those based on machine learning—to be robust against rare but critical events. Dreossi’s work directly addresses the challenge of verifying complex autonomous systems, bridging the gap between formal verification and practical deployment. His earlier foundational paper on combining model checking with runtime verification for safe robotics (75 citations) laid crucial groundwork for this approach. By providing a formal yet practical framework for generating diverse, safety-critical test scenarios, Dreossi has given the autonomous vehicle and robotics communities a vital tool for debugging and validation. His research is essential reading for anyone working on the verification, testing, or safe design of learning-enabled cyber-physical systems.