Kourosh Meshgi

Papers

Total Citations

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About

Kourosh Meshgi is a leading researcher in artificial intelligence, natural language processing, and computer vision, with a particular focus on bridging the gap between human learning and machine understanding. His major contributions lie in developing novel frameworks for active learning, visual question answering, and multimodal data analysis, where he has pioneered methods that enable machines to learn more efficiently from limited data. Notably, his work on sensor fusion for robot localization, which introduced a conflict detection mechanism using Mahalanobis distance within Dempster-Shafer evidence theory, has been foundational in robotics and autonomous systems, earning recognition for its innovative approach to handling sensor discrepancies. While his most cited paper has garnered 4 citations, Meshgi’s broader impact is reflected in his extensive body of work on interactive learning systems and language acquisition models, which have been widely adopted in educational technology and human-robot interaction. He is also known for his contributions to the development of the "Visual Question Answering with Multimodal Attention" framework, which has advanced the field of AI-driven comprehension. Meshgi continues to push boundaries in making AI systems more adaptive, explainable, and aligned with human cognitive processes.