Alexander Marx

Professor


alexander.marx(at)tu-dortmund.de


Department of Statistics

TU Dortmund



Alexander Marx

Professor


Contact

Alexander Marx

Professor


alexander.marx(at)tu-dortmund.de


Department of Statistics

TU Dortmund




I am a professor at TU Dortmund, leading the Causality group at the Research Center for Trustworthy Data Science and Security and the Department of Statistics, and a member of the ELLIS society. My research is at the intersection of causality and machine learning, focusing on causal discovery, causal representation learning, information theory, and Bayesian deep learning. Prior to my current position, I was a Postdoctoral Researcher Computational Biology Group at ETH Zürich, a Postdoc-Fellow at the ETH AI Center and part of the Medical Data Science Group. I did my PhD in the Exploratory Data Analysis group affiliated with the CISPA Helmholtz Center for Information Security and the Max Planck Institute for Informatics.

Job Advertisement

I am actively searching for PhD students and research assistants. Feel free to reach out! More details are provided here.

Thesis Projects

There are several open projects suitable for a Master's or Bachelor's thesis in topic areas such as causal discovery, (Bayesian) image regression, and mutual information estimation.

News

For more updates access the news arxiv.

Activities

Reviewing for Conferences & Journals

  • Reviewer for the Conference on Neural Information Processing Systems (NeurIPS'23 [Top 10% Reviewer Award],24)
  • Reviewer for the International Conference on Machine Learning (ICML'22,23,24)
  • Reviewer for the International Conference on Artificial Intelligence and Statistics (AISTATS'21,22 [Top 10% Reviewer Award],23[Top 10% Reviewer Award],24, 25)
  • PC Member for the SIAM International Conference on Data Mining (SDM'21)
  • PC Member for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'21)
  • Reviewer for the Machine Learning for Health Symposium (ML4H'22,23)
  • Reviewed for the journal Data Mining and Knowledge Discovery (DAMI'22)
  • Reviewed for the journal IEEE Access in 2020
  • Reviewer for the IEEE International Symposium on Information Theory (ISIT'19,20)
  • PC Member for the workshops AAAI-ITCI'22 and NeurIPS-TS4H

Invited Talks

Publications


Predicting Risk for Nocturnal Hypoglycemia after Physical Activity in Children with Type 1 Diabetes


Heike Leutheuser, Marc Bartholet, Alexander Marx, Marc Pfister, Marie-Anne Burckhardt, Sara Bachmann, Julia E Vogt

Frontiers in Medicine, vol. 11, Frontiers, 2024


Anomaly Detection by Context Contrasting


Alain Ryser, Thomas M Sutter, Alexander Marx, Julia E Vogt

arXiv preprint arXiv:2405.18848, 2024


Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning


Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

ICLR, 2024 May


Blood Glucose Forecasting from Temporal and Static Information in Children with T1D


Alexander Marx, Francesco Di Stefano, Heike Leutheuse, Kieran Chin-Cheong, Marc Pfister, Marie-Anne Burckhardt, Sara Bachmann Brenner, Julia E. Vogt

Frontiers in Pediatrics, vol. 11, 2023


Effective Bayesian Heteroscedastic Regression with Deep Neural Networks


Alexander Immer, Emanuele Palumbo, Alexander Marx*, Julia E Vogt*

NeurIPS, 2023 Dec


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