The center is lead by:

Rasmus Nielsen, Stefan Sommer, Christy Hipsley and Mads Nielsen.
Christine Andersen serves as project manager.

PhDs and postdocs:


  • Lily Bao, Shanghai University, China
  • Liwei Hu, University of Electronic Science and Technology of China

Stefan Horst Sommer

Professor, DIKU, UCPH. Expertise in geometric methods for statistical analysis of nonlinear data with application to shape spaces, functional data analysis, and image registration. This includes foundational and algorithmic aspects of statistics on manifold valued data, and computational modelling and statistical analysis of deformations occurring in computational anatomy.
Of relevance in the context of the centre are his studies in the transition of deterministic, smooth shape models towards modelling stochastic variation and in performing parameter estimation for discretely observed stochastic shape processes, coupling differential geometry with stochastic process theory and deep learning. These achievements make inference over stochastically varying shapes possible necessary for the centre’s research aims.

Rasmus Nielsen

Professor, GLOBE, Section for Geongenetics, UCPH, and Professor of Computational Biology, Departments of Integrative Biology and of Statistics, the University of California, Berkeley.
Expertise in statistical and computational aspects of evolutionary theory and genetics as they relate to evolutionary biology.  Of central interest has been what happens at the molecular levels as one species is transformed into another over evolutionary time. To address this, he has developed computational methods and applied them to large scale genomic data and applied statistical methods in other aspects of population genetics, medical genetics, phylogenetics, molecular ecology, and molecular evolution.
Of relevance in the context of the centre are his studies of population genetic inferences using phylogentic methods for detecting natural selection. He uses Brownian motion and related processes to model evolutionary change of gene-expression, leading to discoveries of the genetic basis of biological adaptations in Inuit, Tibetans and Bajau people. His methods are widely incorporated into popular techniques for testing phylogentic hypotheses, such as PAML, allowing detection of the molecular signatures of natural selection. 

Mads Nielsen

Professor, DIKU, UCPH. Expertise in computer vision, machine learning, artificial intelligence and image analysis.
Of relevance in the context of the centre are his studies of the relationship between optimization and evolution approaches to image analysis, showing their equivalence in special cases and constructed shape space of tree-like structures with reparameterization invariant metrics; work that has also been applied to phylogenetic trees.

Christy Hipsley

Associate Professor, Department of Biology, Section for Ecology & Evolution, UCPH. Expertise in integrative techniques to reconstruct morphological transformations in the 500-million year history of vertebrate life. Of particular interest is the application of 3D bioimaging to fossil and modern specimens to quantify phenotypic variation in space and time.  Morphological and taxonomic variation are analysed to identify the contexts under which biodiversity is generated, shaped, and destroyed.
Of relevance in the context of the center are her studies of the evolutionary processes shaping morphology, e.g. evolutionary impacts of climate change in extinct and extant fauna.

Libby Baker

PhD fellow at DIKU, Image Section UCPH. She is part of the CCEM working on a project for stochastic modelling of shape evolution. The aim of this project is to create a model for Brownian motion on full shapes to be used for phylogenetic trees. Libby completed her MSc in mathematics at Bonn University, Germany, majoring in geometry. She then worked in a software development company for a couple of years. Here she worked on two projects applying machine learning to problems in medicine and traffic observation.

Lili Bao

PhD student from the Department of Mathematics, Shanghai University, China, and currently a visiting student at DIKU, UCPH. Her research interests include image processing include image registration and segmentation. She is now working on the project of the medical image registration with sliding motion to get the morphological evolution of the lung image. Lili finished her MSc “Object(s)-of-interest segmentation for images with inhomogeneous intensities based on curve evolution” in Mathematics from Shanghai University in July 2016.

Liwei Hu

PhD student in computer science and technology at the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, China. During the year of 2023 he is a visiting PhD student in Department of Computer Science, Copenhagen University. His research fields include aerodynamic data modeling, deep learning and Riemannian geometry. Before pursuing his PhD studies, he received a B.S. degree in software engineering from the School of Software, Hebei Normal University, Shijiazhuang, Hebei, China, in 2014, as well as an M.S. degree in computer technology from the UESTC, in 2018.

Michael Lind Severinsen

PhD Fellow at Globe, Section for Geogenetics, UCPH and in the context of CCEM he is working on the project “Stochastic Morphometry: Applying stochastic processes to model infinite dimensional shape evolution”. In this project he is going to apply statistical models to test evolutionary hypotheses on empirical shapes ranging from butterfly wing symmetri to the evolutionary change of skulls in wolfs and dogs. Michael finished his MSc “Quantitative Biology and Disease Modelling” from the Technical University of Denmark in summer 2022, and since he saw Mr. DNA Sequence in Jurrassic Park (1993) explaining about how genes control the body had a particular interest in biological systems, evolutionary change and statistics/modelling these.

Sofia Stroustrup

PhD Fellow at Globe, Section for Geogenetics, UCPH . As a part of CCEM Sofia works with developing a method for doing inference on shape models evolving on a phylogenetic tree.  The aim of the method is to learn statistically sound posterior distributions over ancestral shapes as well as over parameters governing the shape models. Sofia holds a bachelor’s degree in Molecular Biomedicine from UCPH and a master’s degree in Bioinformatics and Systems Biology from the Technical University of Denmark (DTU). Sofia has previously worked with deep generative models of scRNA sequencing (UCPH), development of Malaria vaccines (UCPH),  aging in C. elegans (Centre for Genomic Regulation) and modeling of protein evolution (Max Planck Institute of Molecular Cell Biology and Genetics).