SCCH

SCCH is an Austrian Competence Center for Excellent Technologies (COMET research center) focussing on data and software science.
SCCH acts as an active node in the Upper Austrian, Austrian and international innovation network and advances the topics of digitalization and artificial intelligence. SCCH is currently coordinating the H2020 ICT-38 project TEAMING.AI, the Austrian COMET module S3AI with international partners, and the Interreg Austria-Czech Republic project PredMAIn and is also contributing to the closely related H2020 project Cogniplant. SCCH’s main research focus is on software and AI systems engineering, comprising: Artificial Intelligence and Machine Learning (knowledge graphs, decentralized optimization, human-centered AI etc.), Engineering and Workflows (software systems engineering, AI/ML system engineering etc.), Security and Safety (privacy preserving machine learning, data owners in control of privacy etc.)

WPs:

WP2

Role/focus:

Leader of WP2 “From data acquisition to digital twin consolidation framework”; Development of Big Data Infrastructure; Development of process analysis tools utilizing knowledge graphs and causal-inference models; Development of predictive models and digital twins for KPI optimization in the industrial use-cases.

Team involved

Georgios Chasparis

PhD / Key Researcher and Research team lead

georgios.chasparis@scch.at

He studied Mechanical Engineering at the National Technical University of Athens with a focus on automatic control. He received his Ph.D. in 2008 from the University of California, Los Angeles, USA, specializing in Systems and Control. From 2008 to 2010, he was a post-doctoral fellow in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, USA, and from 2010 to 2012, he was a post-doctoral fellow in the Department of Automatic Control at Lund University, Sweden. Since 2012, he has been with the Department of Data Analysis Systems at SCCH, conducting research specializing in prediction and control of complex systems, distributed optimization, and evolutionary learning.