Norbert Siegmund

Norbert Siegmund is Professor and Chair of Software Systems at Leipzig University's Institute of Computer Science in Germany. His research bridges software engineering and artificial intelligence, focusing on configurable software systems, performance analysis, and energy consumption optimization. He has established himself as a leading researcher in the field of configurable systems through extensive publications in top-tier software engineering venues.

His research interests span multiple interconnected domains:

• Configurable, complex systems that evolve over time
• Non-functional properties, particularly performance and energy consumption
• Sampling, learning, and optimization methods from AI applied to software engineering
• Contemporary empirical methods in software engineering
• MLOps and DevOps practices
• Social aspects of AI-production teams

His publication record reveals a consistent focus on creating accurate performance models for configurable software systems, with increasing attention to energy consumption aspects in recent years. The research trajectory shows evolution from basic performance modeling to incorporating machine learning techniques, transfer learning across environments, and addressing energy efficiency concerns.

His scientific achievements have been recognized through several prestigious awards:

• ACM SIGSOFT Distinguished Paper Award at ICSE 2015
• SPLC Best Paper Award in 2012
• Multiple keynote invitations including at QAVS 2021
• Open-Source Award from the state of Thuringia (2020)
• Best PhD award from University of Magdeburg (2013)

Prof. Siegmund has secured significant research funding including DFG projects like 'Green Configuration' (€590,000) and 'Pervolution' (€290,000), as well as BMBF projects related to AI and software engineering. He has mentored numerous students through research projects and has served on program committees for major conferences including ICSE, ASE, ESEC/FSE, and SPLC. His teaching portfolio includes courses on Software Engineering for AI-Enabled Systems, Configurable Software Systems, and Automated Software Engineering.