I am an associate professor in computer vision and biomedical image analysis with the KTH Royal Institute of Technology and the Science for Life Laboratory on the Karolinska Institutet campus. Previously, I worked at several different institutions in Switzerland including ETH Zürich, EPFL, IDIAP, and University of Basel.
I am interested how machine intelligence can help solve pressing questions in biology and medicine, in particular through the understanding and quantification of patterns in images. Recently, an explosion of digitalized data, groundbreaking advances in the life sciences, and the development of cutting-edge algorithms capable of understanding biomedical data, especially from images, have generated terrific excitement around AI and healthcare. My research aims at developing intelligent systems that can precisely diagnose conditions, reduce patient risk, choose effective treatments, and further our understanding of biological systems.
The questions I am interested in lay at the intersection of computer vision, machine learning, biology, and medicine.
PhD, postdoc, and engineering positions with my group are announced on the KTH jobs page. Please apply through the job portal, do not contact me directly.
Students enrolled at KTH interested in working on their MSc thesis are invited to contact me. Possible subjects for the MSc thesis with my research group include:
- Analysis of medical imaging data (diagnostic classification, segmentation, etc)
- Inverse problems such as denoising or super resolution in microscopy data
- Outlier detection in biomedical data
- Risk prediction
Science for Life Laboratory
17121 Solna, Sweden
17165 Solna, Sweden
|KTH Main Campus
(I am not here)
Lindstedtsvägen 3,5 (Plan 4)
100 44 Stockholm, Sweden
Applied Programming and Computer Science, Part 2 (DD1324), examiner, course responsible | Course web
Deep Learning Methods for Biomedical Image Analysis (FDD3020), examiner, course responsible | Course web
Survey group on select topics in computer science (FDD3021), examiner, course responsible | Course web