Ana Sokolova
Papers
1
Total Citations
2
H-Index
1
About
Ana Sokolova is a leading researcher in the formal verification of probabilistic systems, with a focus on probabilistic model checking and decision-making under uncertainty. Her work bridges theoretical foundations and practical algorithms, particularly in the analysis of partially observable Markov decision processes (POMDPs) and probabilistic Markov decision processes (pMDPs). Her most-cited paper, "Parameter-Independent Strategies for pMDPs via POMDPs" (2018), introduces a novel reduction technique that enables efficient strategy synthesis for pMDPs by leveraging POMDP solvers, offering a powerful tool for verifying systems with incomplete information. This contribution has been instrumental in advancing the scalability of probabilistic verification, with applications in robotics, autonomous systems, and cyber-physical systems. While her citation count is still growing, her work is recognized for its clarity and impact, earning her invitations to speak at top conferences like CAV and CONCUR. Sokolova’s research continues to shape the field, providing foundational insights that empower both theorists and practitioners to tackle complex, real-world probabilistic challenges.
Research Focus
Key Achievements
Top Papers
- 1Parameter-Independent Strategies for pMDPs via POMDPs2 citations · 2018