Bild: Katrin Glückler
Forschungsinteressen
- Multimodales maschinelles Lernen
- Deep Learning für tabellarische Daten
- Data Science für das Gesundheitswesen
- Aktivitätserkennung
Lebenslauf
| Seit 2021 | Wissenschaftlicher Mitarbeiter am Institut für Enterprise Systems |
|---|---|
| 2018–2021 | Master of Science in Data Science an der Universität Mannheim |
| 2014–2018 | Bachelor of Arts in Soziologie an der Universität Mannheim |
Publikationen
- Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2024). Investigating demographic features and their connection to performance, predictions, and fairness in EDM models. Journal of Educational Data Mining, 16, 177–213.
- Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. und Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 113, 3633-3652.
- Rink, J., Szabo, K., Hoyer, C., Saver, J. L., Nour, M., Audebert, H. J., Kunz, W. G., Froelich, M. F., Heinzl, A., Tschalzev, A., Hoffmann, J., Schoenberg, S. O. und Tollens, F. (2024). Mobile stroke units services in Germany: A cost-effectiveness modeling perspective on catchment zones, operating modes, and staffing. European Journal of Neurology, 1–9.
- Rink, J., Tollens, F., Tschalzev, A., Bartelt, C., Heinzl, A., Hoffmann, J., Schoenberg, S. O., Marzina, A., Sandikci, V., Wiegand, C., Hoyer, C. und Szabo, K. (2024). Establishing an MSU service in a medium-sized German urban area — clinical and economic considerations. Frontiers in Neurolgy, 15, 1–9.
- Tschalzev, A., Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). A data-centric perspective on evaluating machine learning models for tabular data. In , The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (S. 1–35). , NeurIPS: Vancouver, BC.
- Tschalzev, A., Nitschke, P., Kirchdorfer, L., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). Enabling mixed effects neural networks for diverse, clustered data using Monte Carlo methods. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence: Jeju, 03–09 August 2024 (S. ). , International Joint Conferences on Artificial Intelligence: Jeju, South Korea.
- Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.