People

All people are listed in chronological order of joining the lab or starting a collaboration with us.

Bastian Rieck
Bastian Rieck
Principal Investigator
GitHub
Pseudomanifold
ORCID
0000-0003-4335-0302
Twitter
@Pseudomanifold
Web
https://bastian.rieck.me
When it comes to machine learning, Bastian is interested in all things geometrical and topological, with a particularly strong focus on biomedical applications.
Julius von Rohrscheidt
Julius von Rohrscheidt
Ph.D. Student
Twitter
@JRohrscheidt
Web
https://rohrscheidt.com
Julius is particularly interested in the development and investigation of robust topological representation learning procedures. This includes the construction of new feature extraction algorithms, as well as the investigation of limitations and performance of these techniques.
Jeremy Wayland
Jeremy Wayland
Ph.D. Student
GitHub
jeremy-wayland
ORCID
0000-0002-8766-8737
Twitter
@jeremy_wayland
Web
https://jeremy-wayland.me
Jeremy is interested developing practical and usable topology-based methods for improving our understanding of various generative models. Additionally, he enjoys applying topological methods in collaborative projects that focus on predictive medicine and/or climate change.
Katharina Limbeck
Katharina Limbeck
Ph.D. Student
GitHub
LimbeckKat
Twitter
@LimbeckKat
Web
https://limbeckkat.github.io/
Katharina is interested in using and developing topological machine learning methods to analyse complex structures in biomedical data, in particular single cell data.
Ernst Röell
Ernst Röell
Ph.D. Student
Web
https://ernstroell.github.io
Ernst is interested in building scalable topological descriptors that can be used as machine learning layers. Furthermore, he is interested in the application of abstract algebra and sheaves to understand data structures and machine learning algorithms.
Salome Kazeminia
Salome Kazeminia
Ph.D. Student
Twitter
@KazeminiaSalome
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.
Pia Baronetzky
Pia Baronetzky
M.Sc. Student
GitHub
peppey
Pia is a Mathematics master student at TUM. She is interested in Machine Learning und Topological Data Analysis, and in how Data Science can be applied to solve meaningful problems.
Daniel Bin Schmid
Daniel Bin Schmid
Research Assistant
GitHub
https://github.com/danielbinschmid
Web
https://danielbinschmid.com
Daniel is interested in gaining a deeper understanding in the intrinsic mechanics of machine learning models, with a particular focus on statistical learning theory and symbolic deep learning. He also has a keen interest in building topological datasets to study the expressivity of topological deep learning models and graph neural networks. In his spare time he enjoys creating music and its intersection with generative AI.
Rubén Ballester
Rubén Ballester
Ph.D. Student
GitHub
rballeba
ORCID
0000-0001-8172-6652
Twitter
@rballeba
Web
https://rubenbb.com
Rubén is interested in developing the foundations of topological and categorical deep learning models. On the topological side, Rubén enjoys working with models that leverage the topological and combinatorial structures of the input. On the categorical side, Rubén is especially interested in studying the fundamental blocks of deep learning by means of category theory and in developing logic and symbolic neural networks backed by categorical methods.
Emily Simons
Emily Simons
Fulbright Study/Research Student
Emily recently received her Bachelor’s degree in mathematics from Bowdoin College in Maine, USA, and is originally from the Washington, D.C. area. Funded by a Fulbright Study/Research grant, she will be joining the AIDOS Lab as a visiting student until July 2025, during which she will be based in Munich. Outside the office, Emily enjoys spending her time hiking and rock climbing, solving and writing crossword puzzles, cooking (and eating) good food, and improving her German. She is always keen to play some soccer and is hoping to pick up the flute again.
Richard von Moos
Richard von Moos
Ph.D. Student
GitHub
richardvonmoos
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.
Mathieu Alain
Mathieu Alain
Collaborator
Twitter
@miniapeur
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.
Rayna Andreeva

Rayna Andreeva

Irene Cannistraci

Irene Cannistraci

Corinna Coupette

Corinna Coupette

Pragya Singh

Pragya Singh

Your name is missing here! Learn more about joining us.

Alumni & Alumnae

  • Marek Cerny (now a Ph.D. student with Floris Geerts at the University of Antwerp)
  • Katharina Hagedorn
  • Ferdinand Hölzl (now a master’s student at University of Hamburg)
  • Barış Onarıcı
  • Franz Srambical (now building AGI at p(doom))
  • Tejas Srinivasan
  • Kalyan Varma Nadimpalli (now a research assistant at IIT Madras)