Scholar
Ingo Scholtes
Google Scholar ID: pouriVsAAAAJ
Professor of Machine Learning for Complex Networks at University of Würzburg
Citations & Impact
Citations
1,763
H-index
17
i10-index
33
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Research results have been published in leading physics journals such as Nature Physics, Physical Review Letters, and Phys Rev E; top-tier data science and machine learning venues like SIGKDD, NeurIPS, The Web Conference, or Learning on Graphs (LoG); and prestigious software engineering venues such as ICSE, MSR, or Empirical Software Engineering.
Research Experience
- One of the current research focuses is using higher-order graph models to better understand causal structures in time series data on complex systems, with applications in biology, ecology, information systems, and social sciences. This novel direction in network science has significant implications for our understanding of complex systems, both in terms of theoretical foundations and machine learning methods.
Background
- Research interests include developing new data science and machine learning techniques for complex systems that can be modeled as graphs or networks. Additionally, the team uses network science methods to study open questions in biology, empirical software engineering, and computational social science. The research approach is quantitative, data-driven, and interdisciplinary, combining methods from network science, machine learning, mathematics, and physics.
Miscellany
- In addition to developing new methods and applications of machine learning in relational data, the chair addresses issues fundamental for understanding complex systems across disciplines. Those interested in working with the team can check out the current openings.
Co-authors