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Ankush Agarwal, University of Glasgow
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Gonçalo Dos Reis, University of Edinburgh
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Stefano De Marco, CMAP, École Polytechnique
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Thibaut Mastrolia, UC Berkeley
About:
The Summer School brings together talented young researchers in mathematical finance and will take place online, using Zoom.
The summer school will focus on two advanced courses:
- Optimal transport methods for economic models and machine learning
- Signature method in machine learning and its application to mathematical finance.
There will also be student seminars and discussion sessions, which allow the participants to engage with each other and discuss their current research.
One of the aims of the Summer School is to encourage active cooperation and collaboration in mathematical finance among European institutions. We very much thank the members of the scientific committee for their support in achieving this aim.
PhD students and early career researchers are invited to register and participate in the summer school. All are welcome to attend our online event, but registration is still required.
The Scientific Committee:
The Scientific Committee consists of European leaders and representatives of financial mathematics. We warmly thank them for their encouragement and for accepting to be part of this committee.
- Peter Bank, Peter Imkeller, Wolfgang Runggaldier, Mete Soner, Youri Kabanov, Walter Schachermayer, Josef Teichmann, Santiago Carillo, Ralf Korn, Martin Schweizer, Albert Shiryaev, Nicole El Karoui, Gilles Pagès, Huyen Pham, Marco Frittelli, Damien Lamberton, Bernard Lapeyre, Lukas Stettner, David Hobson, Bernt Øksendal, Denis Talay, Chris Rogers
This school belongs to the series of the European Mathematical Society applied mathematics schools. We gratefully acknowledge the support of International Centre for Mathematical Sciences (ICMS), CMAP, Ecole Polytechnique (Paris, France), Adam Smith Business School (University of Glasgow), Glasgow Mathematical Journal Learning and Research Support Fund and the ANR program Investissements d’Avenir.
Programme:
Details of the Mini Courses and speakers:
| Beatrice Acciaio, ETH Zurich | Optimal Transport Methods in Machine Learning: from the Sinkhorn algorithm to Generative Adversarial Networks | |
| Alfred Galichon, New York University | Optimal Transport Methods for Economic Models | |
| Ilya Chevyrev, University of Edinburgh | A Primer on the Signature Method in Machine Learning | |
| Blanka Horvath and Mikko Pakkanen, King's College London and Imperial College London | Harnessing quantitative finance by deep learning | |
| Antoine Savine , Danske Bank and Copenhagen University | Differential Machine Learning | |
| James Foster, Oxford University | Neural Stochastic Differential Equations for Time Series Modelling |
