People
All people are listed in chronological order of joining the lab or starting a collaboration with us.
Bastian Rieck
Principal Investigator
Principal Investigator
- GitHub
- Pseudomanifold
- ORCID
- 0000-0003-4335-0302
- @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
Ph.D. Student
Ph.D. Student
- @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
Ph.D. Student
Ph.D. Student
- GitHub
- jeremy-wayland
- ORCID
- 0000-0002-8766-8737
- @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
Ph.D. Student
Ph.D. Student
- GitHub
- LimbeckKat
- @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
Ph.D. Student
Ph.D. Student
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
Ph.D. Student
Ph.D. Student
- @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
M.Sc. Student
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
Research Assistant
Research Assistant
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
Ph.D. Student
Ph.D. Student
- GitHub
- rballeba
- ORCID
- 0000-0001-8172-6652
- @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
Fulbright Study/Research Student
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
Ph.D. Student
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
Collaborator
Collaborator
- @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.
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)