Willem Diepeveen
Portrait
Logo Hedrick Assistant Adjunct Professor, Department of Mathematics, UCLA

I am a Hedrick Assistant Adjunct Professor in the Department of Mathematics at UCLA, hosted by Deanna Needell and Andrea Bertozzi, where I also work closely with Oscar Leong. Previously, I completed my PhD at University of Cambridge, supervised by Carola-Bibiane Schönlieb, and hold a double MSc from TU Delft and TU Berlin, and a double BSc from TU Delft.

My research lies at the interface of Riemannian geometry, signal processing, and machine learning, with a focus on developing geometric algorithms for inverse problems, scientific data analysis, and high-dimensional inference. My work combines theoretical rigor with practical performance, while emphasizing robustness and interpretability in settings where black-box models fall short.

I am currently on the academic job market for faculty positions in mathematics, applied mathematics, and related interdisciplinary areas.

Beyond research, I enjoy teaching mathematics at UCLA, mentoring graduate and undergraduate students, and engaging in interdisciplinary collaborations. I am also an avid hiker, swimmer, sailor, skier and snowboarder, and a classics and poetry enthusiast.

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Education
  • University of Cambridge
    University of Cambridge
    PhD Mathematics
    Thesis: Riemannian Geometry for Inverse Problems in Cryogenic Electron Microscopy
    Advisor: Carola-Bibiane Schönlieb
    Oct 2020 – Jun 2024
  • TU Delft & TU Berlin (double degree)
    TU Delft & TU Berlin (double degree)
    MSc Applied Mathematics & MSc Scientific Computing
    Sep 2018 – Sep 2020
  • TU Delft (double degree)
    TU Delft (double degree)
    BSc Applied Mathematics & BSc Applied Physics
    Sep 2014 – Aug 2017
Experience
  • University of California, Los Angeles
    University of California, Los Angeles
    Hedrick Assistant Adjunct Professor, Department of Mathematics
    Host: Deanna Needell and Andrea Bertozzi
    Jul 2024 - Present
  • Harvard University
    Harvard University
    Visiting Postdoctoral Fellow, John A. Paulson School of Engineering and Applied Sciences
    Host: Melanie Weber
    Jun 2025 - Sep 2025
  • Institute for Pure and Applied Mathematics
    Institute for Pure and Applied Mathematics
    Fellow, Long Program on "Computational Microscopy"
    Oct 2022 - Dec 2022
News
2026
Aug 18
I organized the minisymposium "Optimization with Learned Geometric Structure" at SIAM OP26 with Melanie.
Jun 02
New preprint out with Deanna on Riemannian archetypal analysis for interpretable non-linear data analyis.
May 27
New preprint out with Oscar on generative modeling and Riemannian manifold learning from corrupted data.
Jan 27
2025
I gave an invited talk at the Oberwolfach mini-workshop Approximation of Manifold-Valued Functions.
Dec 07
Our paper with Joyce and Deanna curvature-corrected submanifold approximation has been accepted to Information and Inference.
Dec 06
New preprint out with Melanie on arguing for iso-Riemannian geometry as the language for optimization on learned manifolds.
Oct 24
Our paper with Jon and Andrea on interpretable latent dynamics modeling has been accepted to Applied Mathematics Letters.
Jul 23
For the summer, I joined Harvard University as a Visiting Postdoctoral Fellow in the Geometric Machine Learning Group.
Jul 01
New preprint out with Deanna on iso-Riemannian geometry for Riemannian manifold learning.
May 13
Selected Publications (view all )
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Riemannian Archetypal Analysis: Interpretable Non-linear Data Analysis on Deformed Star Distributions

Willem Diepeveen, Deanna Needell

Preprint 2026

[Paper] [Code]

Data-driven geometry Representation learning
Iso-Riemannian Optimization on Learned Data Manifolds

Willem Diepeveen, Melanie Weber

Preprint 2025

[Paper] [Code]

Manifold optimization
Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows

Willem Diepeveen*, Georgios Batzolis*, Zakhar Shumaylov, Carola-Bibiane Schonlieb (* equal contribution)

ICML 2025

[Paper] [Code]

Data-driven geometry
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry

Willem Diepeveen, Deanna Needell

Preprint 2025

[Paper] [Code]

Data-driven geometry
Pulling Back Symmetric Riemannian Geometry for Data Analysis

Willem Diepeveen

Preprint 2024

[Paper] [Code]

Data-driven geometry Representation learning
Riemannian Geometry for Efficient Analysis of Protein Dynamics Data

Willem Diepeveen, Carlos Esteve-Yague, Jan Lellmann, Ozan Oktem, Carola-Bibiane Schonlieb

Proceedings of the National Academy of Sciences 2024

[Paper] [Code]

Data-driven geometry Applications
All publications