Nicole Megow

Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich.

Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems.

Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS.

• Dissertation Award by the German Operations Research Society (2007)
• Berlin Science Award for Young Researchers (2013)
• Heinz Maier-Leibnitz Prize (2013)
• Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015)

Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).