Dr Parpas is a Senior Lecturer in the Computational Optimisation Group of the Department of Computing at Imperial College London. Before joining Imperial College he was a research fellow at MIT (2009-2011).
Before that he was a quantitative associate at Credit-Suisse (2007-2009). He completed his PhD in computational optimization at Imperial College in 2006. He is interested in the development and analysis of algorithms for large scale optimisation problems and exploiting the structure of large scale models arising in applications such as machine learning and finance.
The application of deep learning to high dimensional models in finance
– Reformulate deep learning as an optimisation problem
– Discuss the importance of stability for robust solutions.
– Illustrate the use of deep learning to solve high dimensional (more than 100 dimensions) nonlinear parabolic PDEs (Black&Scholes, Hamilton-Jacobi Belman)
– Provide code and some examples for participants to experiment with.