Imran Razzak

Imran Razzak is an Associate Professor of Computational Biology and lead of GenMI research lab at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, UAE. Before joining MBZUAI, he served as an Associate Professor in Human-Centered Machine Learning at the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. Imran’s research focuses on integrating language and vision to enhance the interpretation of multidimensional data, spanning machine learning, computer vision, and natural language processing, with a particular emphasis on healthcare applications. He has developed advanced ML frameworks for analyzing and interpreting various medical imaging modalities, including ultrasound, mammograms, CT scans, PET scans, MRI, and fMRI, aiming to improve patient outcomes.

He has secured over $15 million in funding for more than 14 research projects and industrial partnerships, including grants from the Australian Research Council (ARC), the National Health and Medical Research Council (NHMRC), and the NSW Government. He has received many awards for his research work, including best paper awards in some top computer science conferences.

PhD Machine Learning, University of Technology, Sydney Australia
Graduate Certificate of Higher Education (Learning and Teaching), Deakin University

• IEEE M 2022 Andrew P. Sage Transactions paper award (IEEE TCSS)
• IEEE MDM 2022 best demo award
• CIKM nomination for Best Resource paper
• IEEE DICTA Best Industry Paper Award
• Winner of several MICCAI and ISBI Challenges
• MNGHA-Researcher and MNGHA-Young Research Award

Imran’s research interests span deep learning, medical image analysis, computational biology, natural language processing (NLP), and multimodal AI.

Dr. Imran Razzak has authored over 250 peer-reviewed publications in leading journals and conferences, including CVPR, ACL, MICCAI, IEEE TMI, Med Image Analysis, and IEEE TNNLS. He has received several research awards and secured over $15 million in funding. He has developed advanced ML frameworks for analyzing and interpreting various medical imaging modalities, including ultrasound, mammograms, CT scans, PET scans, MRI, and fMRI, aiming to improve patient outcomes.

He is currently leading a research lab “Generative AI for Genomics and Medical Imaging (GenMI), which focuses on human-centered AI, combining multiomics, biomedical imaging, visualization, and information technologies to develop innovative clinical applications that advance patient health and well-being. GenMI research addresses critical healthcare challenges by developing foundational theories in information technologies and computer science to advance Personalized, Preventive, and Predictive Medicine. To gain comprehensive insights into disease entities, we integrate multi-layered data, including single-cell RNA sequencing, methylomics, and medical imaging. In close collaboration with clinicians at Charité, we develop clinical decision support systems for cancer treatment and solutions for routine clinical tasks, such as the automated annotation of radiological images. Through partnerships with leading industry players—such as major hospitals and healthcare companies—we translate cutting-edge research into real-world applications, spanning algorithm development, prototype creation, and commercialization. Breakthroughs in these fields lead to significant advancements in precision and personalized medicine, improving patient care and healthcare infrastructure. Our innovations enhance multimedia patient record systems, advanced computer-assisted surgery, AI-driven treatment planning, and telehealth solutions for remote patient monitoring, ultimately increasing healthcare accessibility, efficiency, and outcomes.

Key Research Areas:

• • Medical Image Segmentation
  • Multi-Modal Large Language Model
  • Embodied AI
  • Genomic Sequence Analysis
  • Early Diagnosis and Prognosis
  • Explainable AI