Juan P. G. Amboage

Ph.D. Student

Juan is interested in geometric and topological methods in machine learning. His interests also include machine learning for science and model quantization, as well as a broad range of other topics in machine learning and beyond.

Martin Carrasco

ELLIS Ph.D. Student

Martin is interested in topological and geometrical methods in deep learning. He is particularly interested in methods that leverage the structure of data such as graphs.

Inés García-Redondo

Postdoctoral Researcher

Inés completed her PhD at Imperial College London, as part of the London School of Geometry and Number Theory. She is interested in topological and geometric approaches to data analysis and machine learning.

Pankhil Gawade

Ph.D. Student

Pankhil is interested in methods involving genrative AI and topology. He is particularly interested in developing methods that work at the intersection of the both and applying it to design novel drugs.

Nadja Häusermann

Ph.D. Student

Nadja is interested in applying geometry and topology in machine learning. Particularly, she likes to think about magnitude (homology) and graphs at the moment.

Salome Kazeminia

Ph.D. Student

Salome is interested in unsupervised, self-supervised, and weakly supervised deep models, focusing on trustworthy representation and manifold learning to advance AI in health applications.

Katharina Limbeck

Ph.D. Student

Katharina is interested in using and developing topological machine learning methods to analyse complex structures in biomedical data, in particular single cell data.

Olivia Lecomte

Ph.D. Student

Olivia is a neuroscientist plunging into the world of machine learning. She is particularly interested in how topological deep learning methods can be leveraged to aid with the early diagnosis and prognosis prediction of mental health disorders.

Richard von Moos

Ph.D. Student

Richard is interested in topological and geometric ideas to understand high dimensional data and seeks to bring these ideas into action, forming methods to understand data. He’s in particular excited in applications in machine learning, improving and making learning more interpretable.

Bastian Rieck

Principal Investigator

When it comes to machine learning, Bastian is interested in all things geometrical and topological.

Johannes S. Schmidt

ELLIS Ph.D. Student

Johannes is interested in differential geometry and topology and the application on machine learning algorithms. At the moment, Johannes is intrigued by the Euler characteristic transform and its continued development and application on molecular or other structured data.

Kavir Sumaraj

Ph.D. Student

Kavir is interested in algebraic topology, applied topology, and the study of topological structures in machine learning

Elena Xinyi Wang

Postdoctoral Researcher

Elena’s research lies at the intersection of topological data analysis (TDA), computational topology and geometry, and machine learning. She is particularly interested in developing new geometric and topological tools for data analysis.

Mathieu Alain

Mathieu seeks to extend machine learning to topological and combinatorial domains, including manifolds, graphs, and simplicial complexes. This exciting adventure inspires him to explore tools in disciplines as diverse as differential geometry, algebraic topology, quantum mechanics, differential equations, and signal processing. Currently, his research focuses on developing Gaussian processes that leverage the richness of non-Euclidean data.