Members

Members are listed in chronological order of joining the lab.

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
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
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
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
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
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.

Sebastian Birk
Ph.D. Student

Web

https://sebastianbirk.com

Franz Srambical
Research Assistant

GitHub

emergenz

Twitter

@lemergenz

Web

https://srambical.fr

Franz is interested in leveraging known topological/physical constraints to solve problems in the natural sciences, including – but not limited to – the realm of drug discovery. He is also interested in representation learning for EEG data.

Pragya Singh
M.Sc. Student

GitHub

pragyasingh7

Pragya is an incoming MSc student at Imperial College London. She is interested in geometrical and topological machine learnng, especially its application in natural sciences.

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.

Katharina Hagedorn
B.Sc. Student

GitHub

KatharinaHagedorn

Katharina is doing her bachelor’s thesis in computer science at TUM. She is interested in machine learning approaches in biomedicine and healthcare, especially in the field of antimicrobial resistance prediction.

Irene Cannistraci
Visiting Researcher

Twitter

@ire_cannistraci

Web

https://irene.cannistraci.dev

Irene is interested in studying and analyzing how and why independent neural networks encode information similarly. This includes leveraging geometrical and topological techniques to manipulate the latent space and enable the adaptation and reutilization of different neural models.

Collaborators

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Alumni & Alumnae

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