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
- Convex and Non-Convex Optimization
- Bayesian Statistics
- Medical Data Analysis
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)
- Ground truth labels challenge the validity of sepsis consensus definitions in critical illnessJournal of Translational Medicine, 20(6), 27, 2022
@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} }
- Validity, Reliability, and Significance: Empirical Methods for NLP and Data ScienceSynthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, 2022
@book{riezler2022, author = {Riezler, Stefan and Hagmann, Michael}, title = {Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science}, publisher = {Morgan \& Claypool Publishers}, series = {Synthesis Lectures on Human Language Technologies}, editor = {Hirst, Graeme}, year = {2022}, isbn = {9781636392714}, doi = {10.2200/S01137ED1V01Y202110HLT055}, url = {https://www.cl.uni-heidelberg.de/statnlpgroup/empirical_methods/} }