Wednesday, 15.30 - 16.15, was: WGS|101 now: HFB|C
Optimization, as a way to make "best sense of data" is a common topic and core area in operations research (OR), in theory and applications. Machine learning, being rather on the predictive than on the prescriptive side of analytics, is not so well known in the OR community. Yet, machine learning techniques are indispensible for example in big data applications. We start with sketching some basic concepts in supervised learning and mathematical optimization (in particular integer programming). In machine learning, many optimization problems arise, and there are some suggestions in the literature to address them with techniques from OR. More importantly, we are interested in the reverse direction: where (and how) can machine learning help in improving methods for solving optimization problems and what is it that we can actually learn? We conclude with an alternative view on this presentation's title, namely opportunities where predictive meets prescriptive analytics.
Marco Lübbecke is a full professor and chair of operations research at RWTH Aachen University, Germany. He received his Ph.D. in applied mathematics from TU Braunschweig in 2001 and held positions as assistant professor for combinatorial optimization and graph algorithms at TU Berlin and as visiting professor for discrete optimization at TU Darmstadt.
Marco's research and teaching interests are in computational integer programming and discrete optimization, covering the entire spectrum from fundamental research and methods development to industry scale applications. A particular focus of his work is on decomposition approaches to exactly solving large-scale real-world optimization problems. This touches on mathematics, computer science, business, and engineering alike and rings with his appreciation for fascinating interdisciplinary challenges.