With the widespread availability of huge datasets, increased computing power, and the development of efficient software implementations, machine learning is currently having a huge impact on optimal control. Indeed, academics and practitioners alike are exploring the far-reaching potential of these methods to several of the key problems of the field.
This is a highly interdisciplinary research area, at the intersection of computing, applied mathematics, and statistics. In view of the huge demand from industry, machine learning techniques are starting to become a key part of our toolkit. At the same time, the often surprising effectiveness of the methods but also their pitfalls and limitations present intriguing challenges both academic researchers and practitioners.
The goal of this workshop will be to assemble an interdisciplinary mix of leading researchers and industry representatives to provide an overview of the latest developments in this area.
The workshop is supported by the Quantitative Sciences Research Institute
Anastasia Borovykh (University of Warwick)
Dante Kalise (Imperial College London)
Christoph Reisinger (University of Oxford)
Max Reppen (Boston University)
Zhenjie Ren (Université Paris Dauphine)
Simon Scheidegger (HEC Lausanne)
Christian Schmidt (Millenium Management)
Markus Wulfmeier (Google DeepMind)