Almut Rödder

Ph.D. Candidate, Department of Mathematics, ETH Zurich

Bio

Hi! I am a Ph.D. candidate in Mathematics at ETH Zürich, working under the supervision of Prof. Afonso Bandeira and Prof. Yuansi Chen. My research interests lie in high-dimensional probability, Markov Chain Monte Carlo (MCMC), concentration of measure, and statistical machine learning.

My current work investigates sampling from high-dimensional probability measures, with a focus on settings where the target measure exhibits symmetry under a group action.

Prior to my Ph.D., I was a research intern in the statistical machine learning group at ETH Zürich led by Prof. Fanny Yang, where I worked on minimum norm interpolation in Sobolev spaces under the supervision of Gil Kur.

Previously, I completed my M.Sc. in Mathematics at the University of Mannheim, where my thesis on Mathematical Foundations and Asymptotic Theory for Neural Estimators was supervised by Prof. Sebastian Engelke (University of Geneva). I also hold a double B.Sc. in Economics and in Mathematics in Business and Economics from the University of Mannheim.

  

News

                                        
                
                    
                          
  • August 2026: We are organizing a summer school on the Mathematics of Randomized Linear Algebra Techniques.
  • July 2026: I will attend the the workshop on Synergies between Geometry, Probability, and Computation in High Dimensions at ICERM (Brown University).
  • September 2025: I will attend the the summer school on the Mathematical Aspects of Data Science at EPFL.
  • June 2025: I will attend the HSM special topic school on Statistical Mechanics of spin glasses, neural networks and learning at the University of Bonn.
  • November 2024: I started my PhD at ETH Zürich.
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Preprints & Other Work

Most recent publications on Google Scholar.

Randomstrasse101: Open Problems of 2025

Afonso S. Bandeira, Daniil Dmitriev, Kevin Lucca, Petar Nizić-Nikolac, Almut Roedder

Available online. 2026.

Paper

Theoretical guarantees for neural estimators in parametric statistics

Almut Roedder, M. Hentschel, S. Engelke

Preprint. 2025.

Paper

Randomstrasse101: Open Problems of 2024

A. S. Bandeira, A. Kireeva, A. Maillard, Almut Roedder

Available online. 2025.

Paper

Randomstrasse101: Open Problems of 2025

Afonso S. Bandeira, Daniil Dmitriev, Kevin Lucca, Petar Nizić-Nikolac, Almut Roedder

Available online. 2026.

Paper

Theoretical guarantees for neural estimators in parametric statistics

Almut Roedder, M. Hentschel, S. Engelke

Preprint. 2025.

Paper

Randomstrasse101: Open Problems of 2024

A. S. Bandeira, A. Kireeva, A. Maillard, Almut Roedder

Available online. 2025.

Paper

Teaching

Teaching Assistant, ETH Zurich (2024–2026)

  • Further Topics in Mathematics of Data Science (Master's course) — advanced session on the mathematics of data science, with a focus on open problems and recent advances in research.

    Lecture notes.

  • Further Topics in Mathematics of Signals, Networks and Learning (Bachelor's course) — advanced session for undergraduate students and introduction into relevant research topics.

Teaching Assistant, University of Mannheim (2019–2023)

  • Teaching Assistant for Linear Algebra, Stochastics II and Extreme Value Theory.

Blog

Sampling from the Sherrington-Kirkpatrick Model

The KLS Conjecture

Vitae

Full CV in PDF.

Website Design

Thank you Martin Saveski for this awesome template.
Almut Rödder