Alexander Schell

Postdoctoral Researcher 

Department of Mathematics, ETH Zurich

I am currently a postdoctoral researcher at the Seminar for Applied Mathematics at the Swiss Federal Institute of Technology in Zurich

My research focuses on probabilistic and statistical machine learning, mathematical statistics, stochastic analysis, and applied analysis.
I am particularly interested in stochastic dynamics, statistical inverse problems, and inference from multidimensional stochastic processes, and my work frequently uses rough paths theory to bridge these areas. I am especially drawn to research at the intersection of machine learning and stochastic analysis, where I aim to combine diverse mathematical concepts and techniques to address statistical problems with substantial practical applications.  

Previously, I was a postdoctoral researcher at the Department of Statistics at Columbia University. I am also an associate member of the DataSıg Research Group. I received my PhD in Mathematics from the University of Oxford in autumn 2022, under the supervision of Harald Oberhauser. Prior to this, I completed an MSc in Pure Mathematics at Imperial College London and a BSc and MSc in Mathematics with a minor in Theoretical Physics at Ulm University in Germany.

A detailed CV is available on request.


ETH Zurich, Department of Mathematics 

Rämistrasse 101, HG E 62.1

CH-8092 Zurich, Switzerland 

Email: alexander.schell at


Publications and Preprints


ETH Zurich:
Head Tutor for High-Performance Computing for CSE (Fall Semester 2024)

University of Oxford:
Tutor for Stochastic Differential Equations (Maths C8.1, Michaelmas 2021); Teaching Assistant for Probability, Measure and Martingales (Maths B8.1, Michaelmas 2020) and Functional Analysis II (Maths B4.2, Hilary 2019)

Ulm University:
Head Tutor for Ordinary Differential Equations ('Gewöhnliche Differentialgleichungen', Summer Semester 2018)