FEAU: Algorithms under Uncertainty

Franziska Eberle & Nicole Megow

Language: English

Credit Points: 1 CP possible

Course description:

Many powerful optimization methods assume that all input data is known in advance, but this is rarely true in modern applications such as logistics, production planning, cloud computing, networking, and energy-aware scheduling. In these settings, processing times, demands, transit times, bandwidth, or energy requirements may be unknown, uncertain, or rapidly changing. This course, introduces algorithmic models and techniques for dealing with such incomplete information. We will study online optimization, where decisions must be made immediately as input arrives, as well as selected stochastic models that use historical data to reason about uncertainty. A particular focus will be on recent approaches that combine classical algorithm design with machine learning, using predictions to improve performance while preserving rigorous guarantees even when these predictions are inaccurate.

Prerequisites:

none

Biography: Franziska Eberle

This is good. Slightly smoother:

Franziska Eberle is the head of an independent MATH+ Junior Research Group at the Institute of Mathematics at Technische Universität Berlin. Before joining TU Berlin in 2023, she was a Research Officer at the London School of Economics and Political Science. She received her PhD in Computer Science from the University of Bremen in 2020 and holds both a B.Sc. in Mathematics and an M.Sc. in Mathematics for Operations Research from the Technical University of Munich. Her research focuses on combinatorial optimization under uncertainty, with a particular emphasis on online and stochastic optimization.

Biography: Nicole Megow

Nicole Megow is Professor of Combinatorial Optimization in the Department of Mathematics and Computer Science at the University of Bremen. She studied mathematics at the Technical University of Berlin and the Massachusetts Institute of Technology and earned her PhD in Berlin. Before joining Bremen, she held positions at the Max Planck Institute for Informatics and the Technical University of Munich. Her research focuses on algorithms and optimization under uncertainty.