Bastian Rieck

Welcome to my personal and academic space! I am the Principal Investigator of the AIDOS Lab at the Institute of AI for Health and the Helmholtz Pioneer Campus, focusing on machine learning methods in biomedicine. I also have the honour to be a TUM Junior Fellow and a member of ELLIS.

I am searching for Ph.D. students, so if you like my research and would like to work with me, please reach out to me!

Previously, I was a senior assistant in the Machine Learning & Computational Biology Lab of Prof. Dr. Karsten Borgwardt at ETH Zürich. I obtained my Ph.D.  in computer science from Heidelberg University, which is where I also got my diploma in mathematics. This site contains some things I care about.

Research content

The icons above should cover everything, but in case you prefer text:

• A current academic curriculum vitæ is available.
• My ORCID is 0000-0003-4335-0302.
• I also have a Google Scholar profile, a ResearchGate profile, a publons profile, and a GitHub account.
• Sometimes, I tweet things.

Other content

• If you are a student interested in working with me, please check out my notes for potential collaborations.
• I have a lot of great colleagues and friends who helped me get to where I am today; I keep track of this in a dedicated site about the acknowledgements for my career.

News

• 2022-01-24: Our work on evaluating graph neural networks as well as our work on topology-based graph neural networks was accepted at ICLR 2022!
• 2022-01-08: Here we go again! There will be another instalment of our Workshop on Geometrical and Topological Representation Learning at ICLR 2022.
• 2021-10-29: Really excited to learn that I was awarded the title of TUM Junior Fellow.
• 2021-10-08: Honoured to become a member of ELLIS!
• 2021-10-07: I was given a warm welcome and answered some interview questions for the Helmholtz Pioneer Campus.
• 2021-10-01: A new chapter beckons! I am excited to start as a new principal investigator at the Institute of AI for Health and the Helmholtz Pioneer Campus. I will be focusing on developing novel machine learning methods to tackle challenges in biomedicine.
• 2021-06-06: Stoked to see our work Filtration Curves for Graph Representation being accepted at KDD 2021!
• 2021-05-28: Happy to report that our article Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review was published. Michael provided a marvellous Twitter summary.
• 2021-05-26: Very excited to see our survey A Survey of Topological Machine Learning Methods being published! Check out a Twitter summary thread. This was my first journal publication as a senior author, having the pleasure of serving as a supervisor to Felix Hensel and Michael Moor.
• 2021-04-06: Our work Stable Topological Signatures for Metric Trees through Graph Approximations was accepted for publication in Pattern Recognition Letters. A great Belgian–Swiss collaboration with Robin Vandaele, Yvan Saeys, and Tijl De Bie.
• 2021-04-04: Our work Exploring epithelial-cell calcium signaling with geometric and topological data analysis was accepted as a poster presentation at the Workshop on Geometrical and Topological Representation Learning at ICLR 2021.
• 2021-03-17: Honoured to serve as a technical programme committee member for the Topological Data Analysis and Its Application for Medical Data Workshop at MICCAI 2021.
• 2021-01-08: Our work Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis was accepted for publication in Applied Sciences. Congratulations and thanks to all collaborators on this very timely piece of work!
• 2021-01-03: Excited and honoured to co-organise a Workshop on Geometrical and Topological Representation Learning at ICLR 2021.