Module 2: Basics of IT and Data Science

DI Dr. mont. Hermann Schranzhofer

Hermann Schranzhofer is Data Steward at Graz University of Technology since 2020. Therefore he is involved in the development of services and policies for research data management.

Hermann studied physics at the Graz University of Technology and finished his PhD in materials science at the Montanuni Leoben. Before he joined the RDM Team, he worked for 15 years at the Institute of Thermal Engineering at Graz University of Technology in research and development. His main focus was on simulations of energy systems including renewable energy and innovative thermal storage. In addition, he also carried out measurements in the laboratory and at demonstration plants.

Ass.-Prof. Dipl.-Ing. Dr. Nils Morten Kriege

Nils M. Kriege is assistant professor and leader of the work group "Machine Learning with Graphs" at the Faculty of Computer Science at the University of Vienna. He received his PhD from the TU Dortmund University in 2015, was a visiting researcher at the University of York, and held an interim professorship for Algorithm Engineering at the TU Dortmund University. In 2019 he was awarded a WWTF Vienna Research Group for the project "Algorithmic Data Science for Computational Drug Discovery" and joined the University of Vienna in 2020. His research focuses on developing methods for data mining and machine learning with graphs by solving problems at the boundaries of machine learning, graph theory, and algorithmics. He contributes techniques to the broad topics of graph embedding, graph matching, and graph search in large databases. His ambition is to develop methods that are useful for solving concrete problems in real-world applications, especially in computational drug discovery. His research has been published in top-tier machine learning and data mining venues, including the International Conference on Machine Learning (ICML), the Conference on Neural Information Processing Systems (NeurIPS), and the IEEE International Conference on Data Mining (ICDM).

Dipl.-Ing. Dr. Benedikt Pittl, BSc

Benedikt Pittl holds a master’s degree in Business Informatics from the University of Vienna. In 2020 he successfully defended hisPhD thesis on Cloud Economics. He attended multiple scientific conferences and received for his work the Best-Paper from a highly ranked international Cloud Conference as well as multiple “Best of the Best” Awards of the Faculty of Computer Science from the University of Vienna. Since 2017 Benedikt Pittl gains experiences in Industry, in addition he works as a passionate lecturer at the University of Vienna. In line with his lifelong learning credo he received a additonal master’s degree in the field of Data Science in 2022.

Ass.-Prof. Dipl.-Ing. Dr.techn. Sebastian Tschiatschek, BSc

Sebastian Tschiatschek is assistant professor for Machine Learning at the Faculty of Computer Science at the University of Vienna.

He received his PhD from Graz University of Technology in 2014. After his PhD, he was a postdoctoral fellow at ETH Zurich and a senior researcher in the machine learning and perception group at Microsoft Research in Cambridge. He develops (probabilistic) machine learning algorithms for handling structured objects and sequential decision making. His research focuses on expressive models for heterogeneous data and on the impact of uncertainty and its quantification, for instance on sequential decision making and human-machine interaction. His research is published at top venues of machine learning, including the International Conference on Machine Learning (ICML), the Conference on Neural Information Processing Systems (NeurIPS) and the International Conference on Learning Representations (ICLR).

Dipl.-Ing.Florian Wörister, BSc

Florian Wörister is a Software Engineering graduate of TU Wien and currently pursuing a Ph.D. at the CSLEARN research group of the University of Vienna. He is a certified Carpentries Instructor and has already gained experience in teaching research associates how to maintain software projects with git.