Abdullah Jamal
Contact
Email: abdullahjml11@gmail.com
Phone: (407)-538-6366
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.
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.