Bastian Rieck

Technische Universität München

Names

How do you usually write your name as author of a paper? Also add any other names you have authored papers under.

Bastian Rieck (Preferred)
,
Bastian Alexander Rieck
,
Bastian Grossenbacher-Rieck
,
Bastian Alexander Grossenbacher-Rieck

Emails

Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments.

****@bsse.ethz.ch
,
****@rieck.me
,
****@gmail.com
,
****@helmholtz-muenchen.de
,
****@helmholtz-munich.de

Education & Career History

Enter your education and career history. The institution domain is used for conflict of interest detection and institution ranking. For ongoing positions, leave the end field blank.

Junior Fellow
Technische Universität München (tum.de)
2022Present
 
Principal Investigator
Institute of AI for Health, Helmholtz Munich (helmholtz-muenchen.de)
2021Present
 
Senior Assistant
Swiss Federal Institute of Technology (ethz.ch)
20202021
 
Postdoc
Swiss Federal Institute of Technology (ethz.ch)
20182019
 

Advisors, Relations & Conflicts

Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.

Coauthor
Michael Bronstein
****@cs.ox.ac.uk
2023Present
 
Coauthor
Joshua Southern
****@imperial.ac.uk
2023Present
 
Coauthor
Smita Krishnaswamy
****@yale.edu
2020Present
 
Coauthor
Guy Wolf
****@umontreal.ca
2020Present
 
Coauthor
Roland Kwitt
****@sbg.ac.at
2020Present
 
Coworker
Celia Hacker
****@epfl.ch
2020Present
 
Postdoc Advisor
Karsten Borgwardt
****@bsse.ethz.ch
20182022
 
Coauthor
Filip Sadlo
****@uni-heidelberg.de
20162018
 
PhD Advisor
Heike Leitte
****@cs.uni-kl.de
20112017
 

Expertise

For each line, enter comma-separated keyphrases representing an intersection of your interests. Think of each line as a query for papers in which you would have expertise and interest. For example: deep learning, RNNs, dependency parsing

rkhs
Present
 
kernel methods
Present
 
graph kernels
Present
 
spectral methods
Present
 
computational biology
Present
 
graphs
Present
 
persistent homology
Present
 
computational topology
Present
 
wasserstein distance
Present
 
optimal transport
Present
 
topological data analysis
Present
 
topology
Present
 
fmri
Present
 
autoencoders
Present
 
topological machine learning
Present
 
geometric deep learning
Present