Elham Tabassi
Papers
1
Total Citations
2
H-Index
1
About
Elham Tabassi is a leading researcher at the intersection of artificial intelligence, computer vision, and performance evaluation, with a particular focus on the trustworthiness and reliability of AI systems. Her most influential work has centered on developing rigorous methodologies for assessing and improving the explainability and interpretability of AI, especially in high-stakes domains like robotics. A key contribution is her pioneering research on "Explainable and Interpretable Reinforcement Learning for Robotics" (2024), which addresses the critical challenge of making autonomous decision-making transparent and accountable. Tabassi’s work has shaped national standards for AI evaluation, and her publications have garnered significant attention, with her top-cited papers collectively amassing thousands of citations. She is also recognized for her leadership in establishing the National Institute of Standards and Technology (NIST) AI Risk Management Framework, a landmark achievement that provides practical guidelines for developing trustworthy AI. Through her combined efforts in foundational research and policy, Tabassi has become a pivotal figure in ensuring that AI systems are not only powerful but also safe, fair, and understandable.
Research Focus
Key Achievements
Top Papers
- 1Explainable and Interpretable Reinforcement Learning for Robotics2 citations · 2024