Professor for Applied Data Science and Applied Data Science
| E-Mail: | [email protected] |
| Phone: | +49 6221 6799 767 |
Professor
Prof. Dr. Judith Cerit
About me
Prof. Dr. Judith Cerit (née Pérez-Velázquez) has been Professor of Applied Data Science at SRH University, Munich campus, since October 2026. She studied mathematics at the Universidad Nacional Autónoma de México (UNAM) and received her PhD from the University of Nottingham (UK) in 2006 for work on mathematical models of tumour growth. After a research fellowship at the University of Warwick (UK), she worked from 2009 to 2018 at Helmholtz Zentrum München and the Technical University of Munich, where she completed her habilitation in mathematics in 2019. In 2018 she was Assistant Professor at UNAM in Mexico City.From 2020 to 2022 she was Senior Researcher and project lead in the BMBF joint project GAIA-X 4 KI at Technische Hochschule Ingolstadt, and subsequently, until 2026, Senior Data Scientist & AI Officer at Stadtwerke Ingolstadt. She combines many years of research in mathematical modelling with practical experience in data science and artificial intelligence. She received the Laura Bassi Award (2016) and is a mentor in programmes supporting women in STEM. At SRH University she teaches in the master's programme Applied Data Science and Analytics, including Deep Learning and Biomedical Image Analysis.
- Mathematical oncology: mechanistic models of tumour growth, metastasis and treatment resistance
- Mechanistic learning: combining mechanistic models with machine learning, e.g. for predicting breast cancer relapse (in collaboration with TOBB University of Economics and Technology, Ankara)
- Computational pathology and biomedical image analysis
- Machine learning for reliability analysis and predictive maintenance
- Mathematical modelling of bacterial communication (quorum sensing)
- Safety and trustworthiness of AI systems
I have contributed to these publications
- Pérez-Velázquez, J., Gölgeli, M., Guido, C.A.R., Silva-Carmona, A. (2025): Challenges in Computational Pathology of Biomarker-Driven Predictive and Prognostic Immunotherapy. In: Handbook of Cancer and Immunology. Springer.
- Pérez-Velázquez, J., Gölgeli, M., Ruiz Guido, C.A. (2022): Machine Learning for Failure Analysis: A Mathematical Modelling Perspective. In: Research in Mathematics of Materials Science. Springer.
- Zippelius, A., Hanß, A., Schmid, M., Pérez-Velázquez, J., Elger, G. (2022): Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks. Microelectronics Reliability 129.
- Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., Elger, G. (2021): Predictive Maintenance enabled by Machine Learning: Use cases and challenges in the automotive industry. Reliability Engineering and System Safety.
- Pérez-Velázquez, J., Rejniak, K.A. (2020): Drug-Induced Resistance in Micrometastases: Analysis of Spatio-Temporal Cell Lineages. Frontiers in Physiology 11.
- Cerit, J., Dindarian, A., Cilo-van Norel, N. (2020): Women, AI, and the Power of Supporting Communities: A Digital Gender-Support Partnership. Patterns 1(1).
- Pérez-Velázquez, J. (2016): Mathematical Modelling of Bacterial Quorum Sensing: A Review. Bulletin of Mathematical Biology 78.
- King, J.R., Pérez-Velázquez, J., Byrne, H.M. (2009): Singular Travelling Waves in a Model for Tumour Encapsulation. Discrete and Continuous Dynamical Systems – Series A 25(1).