Abdullah Jamal

Short Bio

Abdullah Jamal is a Staff ML Scientist at Intuitive Surgical. He received his PhD from University of Central Florida under the supervision of Dr. Liqianq Wang  and Dr. Boqing Gong (remote/Google Research). His research interests are broadly in computer vision, and machine learning with a focus on data-efficient learning through meta-learning and domain adaptation. He is specifically interested in developing novel algorithms to efficiently learn beyond human curated datasets ( e.g., few-shot learning, long-tail etc) for visual recognition. 

Before joining UCF, he received B.E. degree in Information and Communication Systems from National University of Sciences and Technology, Pakistan in 2013. 

Recent News

• February 2025. One paper on Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets is accepted to CVPR 2025.

• November 2024. One paper on Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets is accepted to ML4H 2024.

• October 2024. One paper on VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery is accepted to AIM-FM Workshop @ NeurIPS'24.

• November 2023. One paper on Masked Autoencoders for Video Pre-training is accepted to NeurIPS 2023 Workshop: Self-Supervised Learning - Theory and Practice.

• November 2023. One paper on Multi-Modal Masked Autoencoders accepted to WACV 2024.

• June 2023. Gave a talk on "Towards Data-efficient learning for long surgical video analysis" in CVPR 2023 workshop on Medical Computer Vision.