Seyede Fatemeh Ghoreishi
Papers
1
Total Citations
2
H-Index
1
About
Seyede Fatemeh Ghoreishi is a leading researcher at the intersection of uncertainty quantification, machine learning, and complex engineering systems. Her work focuses on developing efficient, data-driven methods to infer and manage uncertainty in coupled multidisciplinary systems—critical for advancing cyber-physical systems, aerospace, automotive, and robotics. Her most-cited paper, “Efficient learning of uncertainty distributions in coupled multidisciplinary systems through sensory data” (2025, 2 citations), introduces novel approaches for learning uncertainty distributions from sensory data, enabling more accurate analysis and control of these interconnected systems. This contribution addresses a fundamental challenge in engineering: how to make reliable predictions when system components interact and data is limited. Ghoreishi’s research has significant implications for improving the safety, efficiency, and robustness of autonomous systems and smart infrastructure. Her work is recognized for bridging theoretical machine learning with practical engineering applications, making her a notable voice in the growing field of uncertainty-aware AI. As her citation record grows, Ghoreishi continues to shape how engineers and researchers model and manage the unpredictable in complex, real-world systems.
Research Focus
Key Achievements
Top Papers
- 1