Martin Norgaard, Assistant Professor, University of Copenhagen

I am a biomedical engineer and neuroscientist working to understand and improve the reliability and reproducibility in medical image analyses. I have a particular interest in understanding how methodological variability affects the interpretations of neuroimaging data, and how machine learning, open science and modern software practices can contribute to this. I completed my bachelor's and master's degree in biomedical engineering in (2013 and 2015, respectively) at the Technical University of Denmark (DTU), specializing in advanced signal processing, medical image analysis and machine learning. Next, I did my PhD in Neuroscience with Gitte M. Knudsen, Melanie Ganz and Claus Svarer from the Neurobiology Research Unit, Rigshospitalet, University of Copenhagen and Stephen C. Strother from the University of Toronto (2016-2019). I finished my PhD (Optimizing Preprocessing Pipelines in PET/MR Neuroimaging) in only 2.5 years, and during my PhD I spent 5 months in the Laboratory for Computational Neuroimaging at the Martinos Center at MGH/Harvard/MIT working with Doug Greve. After my PhD I was a DFF International Postdoctoral Fellow with Russ Poldrack at Stanford University (2020-2022), affilliated with 1) The Department of Psychology, 2) The Center for Reproducible Neuroscience, and 3) The Stanford Data Science Center for Open and Reproducible Science.
As of 2023, I am working as an Assistant Professor at the Department of Computer Science, University of Copenhagen, where I teach the courses Data Analysis and Machine Learning and Mathematical Analysis and Probability Theory for Computer Scientists. I am also affiliated faculty in The Danish Pioneer Centre for Artificial Intelligence (AI) and senior scientific consultant to The Molecular Imaging Branch and The Data Science/Sharing Team at the National Institute of Mental Health (NIMH), Bethesda, USA.
Feel free to contact me via email at martin dot noergaard at di dot ku dot dk or on Twitter @martinnorgaard_