Michael Hagmann

Michael Hagmann

Michael Hagmann, MSc.

I am a graduate research assistant at the StatNLP group, supervised by Prof. Dr. Stefan Riezler. My current research focus are the application and adapation of empirical methods for machine learning. I am also interested in differentialy private machine learning techniques and their application to medical data.

Research Interests

  • Differential Privacy
  • Empirical Methods in ML
  • Time Series Modelling
  • Bayesian Statistics
  • Medical Data Analysis

News

  • 2022 SEP: Copresent a tutorial on empirical methods for ML at ECML 2022
  • 2022 JUL: Copresent a tutorial on empirical methods for ML at ICML 2022

Teaching

WS 2022 Formale Grundlagen der Computerlinguistik:
Mathematische Grundlagen		 (material coming soon)

Curriculum Vitae

Education

2016 Master in Statistics (with distinctions), University of Vienna
2016 Bachelor in Psychology, University of Vienna
2013 Bachelor in Statistics, University of Vienna

Employment History

09/19 Graduate Research Associate
StatNLP group, Department for Computational Linguistics, Heidelberg University
10/18 - 08/19 Research Associate
Heinrich-Lanz-Zentrum, Medical Faculty Mannheim, Heidelberg University
04/16 - 09/18 Research Associate
Department for Medical Statistics, Medical Faculty Mannheim, Heidelberg University
06/12 - 03/16 Consultant Statistician
Section for Medical Statistics, CeMSIIS, Medical University of Vienna

Awards

  • 2017 Award for the best Master thesis in applied statistics by the Austrian Statistical Society (ÖSG) pdf

Selected Publications (full list)

  1. Michael Hagmann, Philipp Meier and Stefan Riezler
    Towards Inferential Reproducibility of Machine Learning Research
    The Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
    pdf | bib 
    @inproceedings{hagmann2023towards,
  author = {Hagmann, Michael and Meier, Philipp and Riezler, Stefan},
  title = {Towards Inferential Reproducibility of Machine Learning Research},
  journal = {The Eleventh International Conference on Learning Representations},
  journal-abbrev = {ICLR},
  year = {2023},
  city = {Kigali},
  country = {Rwanda},
  url = {https://arxiv.org/abs/2302.04054}
}
  2. Shigehiko Schamoni, Michael Hagmann and Stefan Riezler
    Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis
    Proceedings of Machine Learning Research, 182, PMLR, Durham, NC, USA, 2022
    pdf | bib 
    @inproceedings{schamoni2022,
  author = {Schamoni, Shigehiko and Hagmann, Michael and Riezler, Stefan},
  title = {Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis},
  booktitle = {Proceedings of the 6th Machine Learning for Healthcare Conference},
  year = {2022},
  city = {Durham, NC},
  country = {USA},
  volume = {182},
  series = {Proceedings of Machine Learning Research},
  month = {05--06 Aug},
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v182/schamoni22a/schamoni22a.pdf}
}
  3. H. A. Lindner, S. Schamoni, T. Kirschning, C. Worm, B. Hahn, F. S. Centner, J. J. Schoettler, M. Hagmann, J. Krebs, D. Mangold, S. Nitsch, S. Riezler, M. Thiel and V. Schneider-Lindner
    Ground truth labels challenge the validity of sepsis consensus definitions in critical illness
    Journal of Translational Medicine, 20(6), 27, 2022
    pdf | bib 
    @article{lindner2022,
  author = {Lindner, H. A. and Schamoni, S. and Kirschning, T. and Worm, C. and Hahn, B. and Centner, F. S. and Schoettler, J. J. and Hagmann, M. and Krebs, J. and Mangold, D. and Nitsch, S. and Riezler, S. and Thiel, M. and Schneider-Lindner, V.},
  title = {Ground truth labels challenge the validity of sepsis consensus definitions in critical illness},
  journal = {Journal of Translational Medicine},
  year = {2022},
  volume = {20},
  number = {6},
  pages = {27},
  doi = {10.1186/s12967-022-03228-7},
  url = {https://doi.org/10.1186/s12967-022-03228-7}
}
  4. Stefan Riezler and Michael Hagmann
    Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science
    Synthesis Lectures on Human Language Technologies, Springer Cham, 2022
    link | bib 
    @book{riezler2022,
  author = {Riezler, Stefan and Hagmann, Michael},
  title = {Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science},
  publisher = {Springer Cham},
  series = {Synthesis Lectures on Human Language Technologies},
  editor = {Hirst, Graeme},
  year = {2022},
  isbn = {9783031010552},
  doi = {https://doi.org/10.1007/978-3-031-02183-1}
  url = {https://doi.org/10.1007/978-3-031-02183-1}
}