Prof. Dr. rer. nat.
Manuel Fritz
Contact
Room
W2.2.26
Colloquium
Mittwoch, 11:30-13.00 Uhr, Terminvereinbarung über: https://cal.com/manuel-fritz
Phone
(07231) 28-6693Prof. Dr. Manuel Fritz is Professor of Business Information Systems at the Business School of Pforzheim University. His teaching and research focus on the intersection of Artificial Intelligence, Data Governance, and Business Transformation.
After completing his PhD in Machine Learning and Meta-Learning, he worked at ZEISS SMT as Product Owner for the central Business Intelligence Platform, where he led an interdisciplinary team of data engineers and data scientists. In this role, he built bridges between data strategy, technical implementation, and business value creation, and initiated collaborations with universities and research institutions.
At Pforzheim University, he aims to develop trustworthy, auditable, and sustainable AI systems and to prepare students for the data-driven economy of the future. His research focuses on Data & AI Governance, MLOps, Process Intelligence, and AI-driven Business Transformation.
His approach combines practice-oriented research, technological expertise, and business impact – from data sources to production-ready AI systems.
Prof. Dr. Manuel Fritz focuses on the governance, evaluation, and operationalization of artificial intelligence in organizations. His research addresses the question of how AI systems can be designed to be reliable, transparent, and compliant – from the data source to productive deployment. This includes concepts of Data & AI Governance, model monitoring, and the automated traceability of data and model decisions.
Another key area of his work is the integration of AI into business decision-making and transformation processes. This involves approaches from MLOps, Process Intelligence, and AI-driven Business Transformation, aiming to enable sustainable and transparent value creation through data.
In collaboration with industry partners, Prof. Dr. Fritz investigates practical methods for measuring AI effectiveness, such as return-on-investment analyses, model fairness, and data quality. Furthermore, he explores how companies can technically and organizationally meet the requirements of the EU AI Act to successfully implement trustworthy AI systems.
Business Intelligence , Business Process Management , Data & AI Governance , Trustworthy & Explainable AI , MLOps & Model Monitoring , AI Compliance & Auditability , Business Transformation through AI , Meta-Learning & Adaptive Models , Process Intelligence & Analysis , Data Quality & Lineage , Applied AI & Industry Collaboration
2023 - Master of Business Administration
United States
2021 - Dr. rer. nat., Computer Science
Universität Stuttgart
Germany
2016 - Master of Science, Business Application Architectures
Furtwangen University
Germany
2014 - Bachelor of Science, Applied Computer Sciences
DHBW Stuttgart
Germany
Article in Proceedings
FRITZ, M., OPPOLD, S. (2025). Data Contracts to Leverage (De-)centralized Data Management in Manufacturing Industries: An Experience Report. Datenbanksysteme für Business, Technologie und Web.
FRITZ, M., HAUG, M., AZAMNOURI, A., WAGNER, S. (2025). MLOps Adoption in the Manufacturing Industry: A Case Study with Zeiss SMT. Service-Oriented Computing.
FRITZ, M., TREDER-TSCHECHLOV, D., SCHWARZ, H., MITSCHANG, B. (2024). Ensemble Clustering Based on Meta-Learning and Hyperparameter Optimization. International Conference on Very Large Data Bases.
FRITZ, M., TREDER-TSCHECHLOV, D., SCHWARZ, H., MITSCHANG, B. (2023). ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis. SIGMOD. ACM on Management of Data.
FRITZ, M., BEHRINGER, M., SCHWARZ, H., MITSCHANG, B. (2022). DATA-IMP: An Interactive Approach to Specify Data Imputation Transformations on Large Datasets. Cooperative Information Systems.
FRITZ, M., BEHRINGER, M., TSCHECHLOV, D., SCHWARZ, H. (2022). Efficient exploratory clustering analyses in large-scale exploration processes. VLDB JOURNAL.
FRITZ, M., TSCHECHLOV, D., SCHWARZ, H. (2021). AutoML4Clust: Efficient AutoML for Clustering Analyses. International Conference on Extending Database Technology.
FRITZ, M., TSCHECHLOV, D., SCHWARZ, H. (2021). Efficient Exploratory Clustering Analyses with Qualitative Approximations. International Conference on Extending Database Technology.
FRITZ, M., SHAO, G., SCHWARZ, H. (2021). Automatic Selection of Analytic Platforms with ASAP-DM. International Conference on Scientific and Statistical Database Management.
FRITZ, M., BEHRINGER, M., HIRMER, P., SCHWARZ, H. (2020). Empowering Domain Experts to Preprocess Massive Distributed Datasets. Business Information Systems.
FRITZ, M., TSCHECHLOV, D., SCHWARZ, H. (2020). Learning from Past Observations: Meta-Learning for Efficient Clustering Analyses. Big Data Analytics and Knowledge Discovery.
FRITZ, M., BEHRINGER, M., SCHWARZ, H. (2020). LOG-Means: efficiently estimating the number of clusters in large datasets. International Conference on Very Large Data Bases.
FRITZ, M., BEHRINGER, M., SCHWARZ, H. (2019). Quality-driven early stopping for explorative cluster analysis for big data. Software-Intensive Cyber-Physical Systems.
FRITZ, M., MUAZZEN, O., BEHRINGER, M., SCHWARZ, H. (2019). ASAP-DM: a framework for automatic selection of analytic platforms for data mining. Software-Intensive Cyber-Physical Systems.
FRITZ, M., SCHWARZ, H. (2019). Initializing k-Means Efficiently: Benefits for Exploratory Cluster Analysis. On the Move to Meaningful Internet Systems.
FRITZ, M., ALBRECHT, S., ZIEKOW, H., STRÜKER, J. (2017). Benchmarking Big Data Technologies for Energy Procurement Efficiency. America’s Conference on Information Systems.
FRITZ, M., ALBRECHT, S., STRÜKER, J., ZIEKOW, H. (2016). Targeting customers for an optimized energy procurement. A Cost Segmentation Based on Smart Meter Load Profiles. Springer.
