# 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.

Contact

ETH Zurich, Department of Mathematics

Rämistrasse 101, HG E 62.1

CH-8092 Zurich, Switzerland

Email: alexander.schell at math.ethz.ch

Publications and Preprints

A.Schell, R. Alaifari: Nonparametric Regression of Stochastic Processes via Signatures, 2024.

A.Schell: A Robustness Analysis of Blind Source Separation, 2023.

Under review.A.Schell, H.Oberhauser: Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time Signals, 2023.

Ann.Stat. 51(2):487-518.A.Schell: Nonlinear and Robust Independent Component Analysis for Stochastic Processes, 2022.

DPhil Thesis, Oxford.

Teaching

ETH Zurich:

Head Assistant for High-Performance Computing for CSE (Fall 2024) and Numerical Analysis of Stochastic Differential Equations (Autumn 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)