Michael Mühlebach

Background
Optimization plays a central role in engineering, technology, and society, as it is the basis for rational decision making. It lies at the heart of many new technologies, for example (the list is by no means exhaustive): Computer aided diagnoses assist doctors in interpreting medical images. Due to the availability of large datasets (e.g. mammography images) machine learning algorithms are trained to discriminate malignant abnormalities from benign ones, and thus predict cancer with high certainty and reduce the rate of false-positives. Modern air traffic control algorithms optimize flight schedules for improving efficiency (in terms of reducing total flight delays, fuel consumption, delays per passenger, etc.) and minimizing the propagation of delays, while maintaining safety and security. Self-driving cars use an optimization-based approach to plan trajectories that are in accordance with traffic regulations, avoid obstacles, and guarantee safety, while maximizing passenger comfort at the same time. Speech recognition software recognize natural voice and interpret user commands, thereby providing a convenient user interface to mobile devices. Internet platforms such as Amazon, Spotify, Netflix, YouTube, Facebook analyze the activities of individuals and use optimization algorithms to provide personalized recommendations about products, movies, songs, news, etc. The underlying tool enabling all these applications is mathematical optimization. As a matter of fact, the advances in machine learning and automation that we have witnessed in the last decade are largely due to new optimization algorithms and a better understanding thereof. Due to increases in computational power, emerging low-cost sensing capabilities, and advances in data acquisition in general, possible fundamental research findings in optimization have a high impact and are likely to play a dominant role also in the future.