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Faculty of Mathematics

 

Career

2020-currently: PhD student in Mathematics of Information, University of Cambridge, United Kingdom

2018-2020: MSc. in Applied Mathematics and Scientific Computing, Delft University of Technology, The Netherlands, Berlin University of Technology, Germany

2014-2017: BSc. in Applied Mathematics and Applied Physics, Delft University of Technology, The Netherlands

 

Research

I am particularly interested in the intersection of inverse problems, optimization and differential geometry with applications to imaging. Currently, I am working on several inverse problems in Cryo-EM microscopy in collaboration with the Laboratory for Molecular Biology (LMB) in Cambridge. In Cryo-EM part of the reconstruction problem often comes down (or can be written as) a manifold-valued optimisation problem. Already in the most basic cases one needs to retrieve the optimal SO(3)-valued orientations of the projections in order to reconstruct the potential of the macro-molecule of interest. But also in general, non-linear aspects of imaging problems often have some underlying manifold structure which could be exploited.

Publications

Riemannian geometry for inverse problems in cryogenic electron microscopy
W Diepeveen
(2024)
Pulling back symmetric Riemannian geometry for data analysis
W Diepeveen
(2024)
Riemannian geometry for efficient analysis of protein dynamics data
W Diepeveen, C Esteve-Yagüe, J Lellmann, O Öktem, C-B Schönlieb
(2023)
Regularizing Orientation Estimation in Cryogenic Electron Microscopy Three-Dimensional Map Refinement through Measure-Based Lifting over Riemannian Manifolds
W Diepeveen, J Lellmann, O Oktem, CB Schonlieb
– SIAM Journal on Imaging Sciences
(2023)
16,
1440
Curvature corrected tangent space-based approximation of manifold-valued data
W Diepeveen, J Chew, D Needell
(2023)
Spectral decomposition of atomic structures in heterogeneous cryo-EM
C Esteve-Yagüe, W Diepeveen, O Öktem, CB Schönlieb
– Inverse Problems
(2023)
39,
034003
Regularising orientation estimation in Cryo-EM 3D map refinement through measure-based lifting over Riemannian manifolds
W Diepeveen, J Lellmann, O Öktem, C-B Schönlieb
(2022)
An Inexact Semismooth Newton Method on Riemannian Manifolds with Application to Duality-Based Total Variation Denoising
W Diepeveen, J Lellmann
– SIAM Journal on Imaging Sciences
(2021)
14,
1565

Research Groups

Cambridge Image Analysis
Mathematics of Information (Applied)

Room

FL.06

Telephone

01223 760366