Kevin-Martin Aigner

Kevin-Martin Aigner serves as an Academic Councillor (Akad. Rat) at the Department of Data Science within the Faculty of Science at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is affiliated with the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Liers, focusing on mathematical optimization methods for energy systems and power networks. His work bridges theoretical optimization with practical energy transition challenges.

His research centers on optimization under uncertainty, with key interests in distributionally robust optimization, data-driven methods, and sector-coupled energy system modeling. He develops novel approaches for handling renewable energy uncertainties in power grids, solar feed-in variability, and multi-sector energy integration. His methodologies emphasize explainability in optimization processes and robust decision-making under complex uncertainty structures.

Recent publications reveal consistent focus on power network optimization (2021-2025), with increasing emphasis on distributionally robust frameworks, explainable AI integration, and regional multi-sector energy modeling. His work demonstrates strong interdisciplinary connections between operations research, machine learning, and sustainable energy systems engineering.

Scientific recognition includes:

• GOR Dissertation Award for "Data-driven Optimization under Uncertainty for Power Networks"

Dr. Aigner actively contributes to major research initiatives including:

• CRC TRR 154: Mathematical Modeling, Simulation and Optimization using Gas Networks (2022-2026, DFG-funded)
• ESM-Regio: Multi-sector Coupled Energy System Modeling on Regional Level (2021-2024, BMWE-funded)
• Optimal Control of Electrical Distribution Networks with Uncertain Solar Feed-in (2018-2021)

He participates in the Optimization under Uncertainty & Data Analysis (OUDA) research group and has organized academic events including the TRR 154 Summer School on Optimization, Uncertainty and AI (2024) and Women in Optimization 2024. His teaching includes Discrete Optimization I and specialized seminars on Mixed-Integer Nonlinear Optimization.