Saqib Qamar

I am a researcher working at the intersection of Artificial Intelligence, Deep Learning, and Medical Image Analysis. My work focuses on developing automated deep learning systems that process and interpret complex biomedical imaging data, including MRI, dermoscopy, and microscopy images.

I completed my Ph.D. in Computer Science at Huazhong University of Science and Technology (HUST), China, where I worked on efficient 3D convolutional neural networks for brain MRI segmentation under the supervision of Prof. Hai Jin (IEEE Fellow). During my Ph.D., I developed deep learning models for challenging problems in the medical domain, including brain tumor segmentation and infant brain analysis. My research interests span Machine Learning, Computer Vision, and biologically inspired computation, and I also have experience working with diverse image and multimodal data beyond these areas. I have held postdoctoral positions at KTH Royal Institute of Technology and Umeå University in Sweden, where I advanced research in AI and Computer Vision, secured competitive funding from the Swedish Research Council, and supervised graduate students. Earlier in my career, I served as an Assistant Professor at MITS, India, where I taught and mentored students in computer science. I am a DAAD Postdoc-NeT-AI Fellow, and have published 30+ articles in high-impact venues with over 1,000 citations and an h-index of 16.

My goal is to use artificial intelligence and computer vision to better understand complex visual data in healthcare and beyond in developing intelligent, clinically deployable solutions that support medical professionals and benefit society.