Martin Singull

Martin Singull is a Professor and Head of Division in Applied Mathematics at the Department of Mathematics, Faculty of Science and Engineering, Linköping University. He has held academic positions at Linköping University since 2012, progressing from Assistant Professor to full Professor in 2020, and serving in leadership roles since 2016. His research is centered on mathematical statistics, particularly statistical inference for complex, high-dimensional data with repeated measurements.

His research interests include:

• Statistical inference for repeated measurements and growth curve models
• Classification and discriminant analysis
• Multivariate statistical analysis
• High-dimensional data modeling
• Applications in public health, cardio-oncology, and development contexts

Martin Singull's recent publications focus on likelihood-based classification, Edgeworth-type expansions for distribution approximation, residual analysis in GMANOVA-MANOVA models, and the estimation of misclassification probabilities. His work often involves collaboration with Dietrich von Rosen and other researchers, applying advanced statistical techniques to both theoretical and applied problems. A strong trend in his research is the development of efficient classifiers using temporal and spatial information in longitudinal data.

His scientific service includes:

• Director for the Research School in Interdisciplinary Mathematics (2024–)
• Chair of the organizing committee for IWMS 2023 and LinStat2014
• Team Leader for Sida-funded bilateral programs in mathematics with universities in Africa
• Member of the ISP Mathematics Reference Group
• Board member of the Faculty of Science and Engineering

Martin Singull has supervised numerous PhD students, both at Linköping University and in collaborative programs in Rwanda, Uganda, Tanzania, Mozambique, and Cambodia. His advising spans theoretical statistics and applied interdisciplinary research. He leads research collaborations aimed at strengthening mathematical capacity in low-income countries, particularly in Africa. His work is supported by funding from Sida and ISP, and he is actively involved in international academic networks such as the International Workshop on Matrices and Statistics (IWMS).

He is also associated with research initiatives in computational cardio-oncology, where statistical models are used to predict cardiovascular complications in cancer survivors, and has contributed to projects on fistula prevention and health outcomes in low-resource settings.