Nils Daniel Forkert

Dr. Nils Daniel Forkert is a Full Professor at the University of Calgary holding primary appointments in the Department of Radiology and Department of Clinical Neurosciences within the Cumming School of Medicine, with an adjunct appointment in the Department of Electrical and Software Engineering at the Schulich School of Engineering. He serves as Child Health Data Science Program Director at the Alberta Children's Hospital Research Institute, Theme Lead for Machine Learning in Neuroscience at the Hotchkiss Brain Institute, and is a Member of the Arnie Charbonneau Cancer Institute.

His educational background includes a German Diploma in Computer Science from the University of Hamburg, Master's in Medical Physics from Technical University of Kaiserslautern, Doctorate in Medical Image Processing from the University of Hamburg, and Postdoctoral Fellowship at Stanford University.

Dr. Forkert's research focuses on developing advanced image processing algorithms and machine learning systems for medical data analysis. His work spans Medical Image Analysis, Deep Learning, and Precision Medicine, with emphasis on extracting clinically relevant biomarkers from multi-modal medical data to support computer-aided diagnosis and patient-specific prediction models. His methodologies particularly target neurological applications and pediatric health through the One Child Every Child initiative.

His major recognition includes the Canada Research Chair (Tier 2) in Medical Image Analysis awarded in 2020.

As an active researcher, Dr. Forkert has secured major funding from the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council (NSERC), Heart and Stroke Foundation, Calgary Foundation, and National Institutes of Health (NIH). He currently mentors three postdoctoral fellows, seven PhD students, and six MSc students, demonstrating strong commitment to training data science researchers. His strategic university involvement includes Brain and Mental Health (2015-2021), Child Health and Wellness (2020-2025), Engineering Solutions for Health (2015-2021), and Infections, Inflammations and Chronic Diseases (2015-2021) initiatives.

He leads the Medical Image Processing (MIP) Lab and contributes to the One Child Every Child Comprehensive Data platform, focusing on scalable machine learning solutions for pediatric healthcare challenges through interdisciplinary collaboration across engineering and medical domains.