Christian Alexander Haider
Researcher · Symbolic Regression
University of Applied Sciences Upper AustriaAbout
Christian Alexander Haider is a researcher at University of Applied Sciences Hagenberg, affiliated with the Research Center Hagenberg and multiple Centers of Excellence including Logistics, Smart Production, Digital Transformation, and ICT. His work focuses on developing advanced computational methods for practical applications in logistics and production systems.
Haider's research interests center around symbolic regression techniques with particular emphasis on shape-constrained models that incorporate domain knowledge. His work addresses critical challenges in constraint evaluation, simulation optimization, and the Physical Internet concept. He develops methods that ensure regression models respect physical constraints while maintaining accuracy, which is crucial for real-world engineering applications where model violations could have serious consequences.
Analysis of his publication trends from 2022-2025 reveals a consistent focus on improving symbolic regression methodologies. His research has evolved from basic constraint evaluation techniques to sophisticated multi-objective approaches that balance multiple competing requirements in model building. The applications span diverse fields including magnetization processes, extrusion manufacturing, and data validation systems, demonstrating the versatility of his methodological contributions.
Haider has been actively involved in multiple research projects including:
- FinCoM - Financial Condition Monitoring (2022-2023)
- Josef Ressel Zentrum für Symbolische Regression (2018-2022)
- DigiVent - Predictive Maintenance für Industrie-Radialventilatoren (2017-2020)
- K.I. für E-Mails (2017-2019)
- ATROPINE - Fast Track to the Physical Internet (2016-2018)
His work is conducted within the Center of Excellence Logistics and Center of Excellence for Smart Production, where he collaborates with researchers across disciplines to develop solutions for complex logistical challenges using advanced computational techniques.
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