Ingo Scholtes

Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at the Center for Artificial Intelligence and Data Science (CAIDAS), Julius-Maximilians-Universität Würzburg, where he holds the Chair of Computer Science XV. He has held prior academic positions at the University of Zürich, Bergische Universität Wuppertal, ETH Zürich, and KIT. He is actively involved in the academic community as founding co-chair of the Computational Social Science Section of the German Informatics Society (GI), associate editor of EPJ Data Science, and member of the European Lab for Learning and Intelligent Systems (ELLIS). He also serves as deputy spokesperson of CAIDAS.

Research Interests: His research lies at the intersection of machine learning, network science, graph mining, and computational social science. He specializes in higher-order models of complex systems, temporal networks, and deep graph learning. His work aims to develop novel analytical frameworks for understanding dynamic, real-world systems through data-driven network modeling.

The recent publications and preprints highlight a strong focus on advancing theoretical and practical aspects of temporal and graph-based machine learning. Key themes include the expressivity of temporal graph neural networks, the development of temporal graph isomorphism, and leveraging network science to improve deep learning on graphs.

• Scientific Awards:
• Junior-Fellowship from the German Informatics Society (2014)
• SNSF Professorship (CHF 1.5 million) from the Swiss National Science Foundation (2018)

He leads the BMBF-funded research project COMFORT (Compression Methods for Robustness and Transferability), which received nearly €2 million in funding. His academic leadership includes organizing workshops, delivering invited talks (e.g., at NetSci, Dagstuhl, and university seminars), and mentoring through research supervision. His research group is engaged in foundational and applied work in AI and data science, with strong interdisciplinary links to physics and social sciences.

He is involved in several research teams and labs, including the CAIDAS center and his own research group on machine learning for complex networks. His work is supported by national and international funding bodies, and he collaborates with researchers such as Prof. Petra Mutzel and Dr. Jonas Sauer.