2020 – present
TGW Logistics Group, Developer Cognitive Systems

2019 – 2020
TGW Logistics Group, Data Analyst

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The Julius Maximilians University, Würzburg
PhD candidate, Computer Science

Johannes Kepler Universität, Linz
Dipl. Ingenieur, Industrial Mathematics & B.Sc. Technical Math

2022 – present
TGW Logistics Group, Manager of Artificial Intelligence Competences

2019 – 2022
STIWA Group, Data Scientist

2017 – 2019
IBM, Data Scientist

LinkedIn

Technische Universität, Wien
PhD, Theoretical and Mathematical Physics

Basics of Collective AI Development vs. Industrial Domain Specific Development

Do’s and Don’ts when dealing with big data and data engineering

Insight into the conception of data driven products – presentation of two methodologies

Introduction to the basic technical knowledge of data driven approaches

Explanation of terms and concepts:

• Supervised learning, unsupervised learning, reinforcement learning, regression, decision tree
• Insight into hypothesis testing and general statistics

Possible applications of big data in combination with AI or without

• Anomaly Detection
• Predictive Maintenance
• Which methods are sufficient to solve a problem but without AI
• Time series analysis
• Image recognition and computer vision (Rovolution, Rovoflex, Autonomous Driving)
• LLMs opportunities and risks for data-driven applications

Developing a deep understanding of data-driven solutions

More extensive knowledge of the different sub-forms of artificial intelligence and machine learning

Differentiation competence of AI algorithms versus non-AI algorithms

Technical understanding & methodical application skills when designing data-driven concepts

Establishment of a permanent, self-organized learning club on data-driven solution approaches

Additional goal: Learning club has a long-term impact across the entire TGW to spread understanding and application skills on big data, AI and deep learning