Rate My Professor Behzad Bozorgtabar

Bozorgtabar’s research focuses on machine learning, computer vision, and medical image analysis, particularly self-supervised learning, test-time adaptation, multimodal foundation models, and agentic AI for trustworthy deployment in healthcare under domain shifts. He led the EPFL work-package in the EU Horizon 2020 ADAS&ME project involving over 30 partners. Notable roles include Senior Area Chair for NeurIPS 2026 and ICML 2026, Area Chair for CVPR and BMVC 2026, organizer of the Test-Time Updates Workshop at ICLR 2026, and the EPFL Computer Vision Talks series.

His influential publications feature "LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs" (CVPR 2026, Oral), "ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains" (NeurIPS 2025), "UniViT: Unifying Image and Video Understanding in One Vision Encoder" (NeurIPS 2025), "A Simple Framework For Open-Vocabulary Zero-Shot Segmentation" (ICLR 2025), and "CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping" (ICLR 2024, Spotlight). These works advance robust AI systems for real-world applications.