CV

Education

• PhD in Machine Learning & Statistics, University of Oxford, 2023 (expected)
• MSc in Mathematics with minor in Economics, Karlsruhe Institute of Technology, 2020
• MSc in Statistical Science, University of Oxford, 2019
• BSc in Mathematics with minor in Economics, Karlsruhe Institute of Technology, 2017

Work Experience

• Head Teaching Assistant for Statistics I & II, Oct 17 - Sep 18 & Oct 19 - Sep 20
  • Karlsruhe Institute of Technology, Department of Economics
  • Supervision of 15 undergraduate teaching assistants
  • Organisation of tutorials and exams for up to 800 Students
• Head Teaching Assistant for Programming in R, Oct 16 - Sep 17
  • Karlsruhe Institute of Technology, Department of Economics
  • Introducing groups of up to 100 to programming in R

Skills

• Languages: German (native) and English (fluent)
• Coding: Python (expert), Java (advanced), R (advanced), Matlab (basic)

Publications

Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes

Veit Wild, Motonobu Kanagawa and Dino Sejdinovic (2010). "Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes." arXiv preprint arXiv:2106.01121 .

Bayesian Kernel Two-Sample Testing

Qinyi Zhang, Veit Wild, Sarah Filippi, Seth Flaxman and Dino Sejdinovic. "Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning." Journal of Computational and Graphical Statistics.

Variational Gaussian Processes: A Functional Analysis View

Veit D. Wild and George Wynne (2022). "Variational Gaussian Processes: A Functional Analysis View." International Conference on Artificial Intelligence and Statistics.

Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning

Veit D. Wild, Robert Hu and Dino Sejdinovic (2022). "Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning." arXiv preprint arXiv:2205.06342.

Talks

Gaussian Wasserstein Inference: Gaussian Measures meet Bayesian Deep Learning

May 25, 2022

Talk at Alan Turing Institute, Data-Centric Engineering Seminar, London, UK