Shmuel Avidan

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role.

His educational trajectory features:

• Ph.D. in Computer Science, Hebrew University of Jerusalem (1999)

Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding.

Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems.

Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions.

Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts.

Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.