Ingo Scholtes
University of Würzburg, University of Zurich, Universitat Trier, Bergische Universität Wuppertal
0000-0003-2253-0216
About
I am a Professor for Machine Learning for Complex Networks at the Center for Artificial Intelligence and Data Science of Julius-Maximilians-Universität Würzburg. I am further SNSF Professor in the Department of Informatics at University of Zurich, where I head the Data Analytics Group (DAG).
My research addresses open questions at the intersection between machine learning, network science, graph mining, and computational social science. A summary of my foundational works on higher-order graph analytics for time series data was recently published in Nature Physics.
In 2014 I was awarded a Juniorfellowship from the German Informatics Society. In 2018 I was awarded an SNSF Professorship with a volume of CHF 1.5 Mio from the Swiss National Science Foundation. Since 2021 I hold the Chair of Computer Science XV - Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg.
I am founding co-chair of the Computational Social Science Section of the German Informatics Society (GI e.V.) and associate editor of EPJ Data Science. I am also member of the European Lab for Learning and Intelligent Systems (ELLIS)
Employment
-
University of Würzburg Professor of Machine Learning for Complex Networks2021 - Present
-
Bergische Universität Wuppertal Professor for Data Analytics2019 - 2021
-
University of Zurich SNF Professor2018 - Present
-
Karlsruher Institut für Technologie Vertretungsprofessor2016 - 2016
-
ETH Zürich Senior researcher2011 - 2018
Education
-
Universitat Trier Dr. rer. nat.2006 - 2011
Projects & Funding
Projects & funding information is unavailable.
Publications (71)
- Generating Temporal Contact Graphs Using Random Walkers Save
- Inference of time-ordered multibody interactions Save
- Predicting variable-length paths in networked systems using multi-order generative models Save
- Sequential motifs in observed walks Save
- Big Data = Big Insights? Operationalising Brooks' Law in a Massive GitHub Data Set Save
- De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs Save
- Learning the Markov Order of Paths in Graphs Save
- One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium Save
- Similarity-based Link Prediction from Modular Compression of Network Flows Save
- Higher-order models capture changes in controllability of temporal networks Save