My research interests:
Machine Learning applied to finance and insurance
Robust optimization in causal models (e.g. portfolio optimization in causal factor models)
Neural Optimal Transport and Wasserstein barycenters for fair regression and fair insurance pricing
Generative models for stress testing of high-dimensional equity/forex/credit portfolios
ML-based calibration of market-implied risk measures from bid/ask spreads
Analytical techniques for large credit portfolio losses
Mod-Poisson approximation schemes for CDO pricing
Beyond mixture models: concentration inequalities and large deviations theory for Markov Random Fields (MRFs)
Counterparty risk and CVA in financial networks
Propagation of losses and risks on financial networks (impact of climate change on EU balance sheets)
Continuous-time, endogenous valuation in financial networks (network CVA)
My teaching experience is in Machine Learning, Probability Theory and Financial Mathematics. Find out more in the Teaching section.