Oyendrila Dobe

Papers

Total Citations

H-Index

About

Oyendrila Dobe is a rising researcher in formal methods and probabilistic verification, with a focus on hyperproperties—a class of specifications that relate multiple executions of a system. Her most-cited work, "Probabilistic Hyperproperties with Rewards" (2022), introduces a novel framework for reasoning about quantitative aspects of probabilistic systems, such as expected costs or rewards, across different runs. This contribution bridges the gap between probabilistic model checking and hyperproperty analysis, enabling more expressive verification of security, privacy, and performance guarantees. With 6 citations to date, this paper has already sparked interest in the formal methods community for its innovative approach to combining probabilistic reasoning with multi-execution specifications. Dobe’s research is particularly impactful for applications in randomized algorithms, cyber-physical systems, and machine learning, where understanding trade-offs between reward and risk across system behaviors is critical. Her work stands out for its clarity and practical relevance, making her a promising voice in the next generation of verification researchers.