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
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.
March 2023. One paper accepted to MIDL 2023.
June 2022. One paper accepted to MICCAI 2022.
Sept 2021. Joined Intuitive Surgical as Computer Vision Scientist.