Blanka Horvath is a Lecturer at King's College London in the Financial Mathematics group, and an Honorary Lecturer in the Department of Mathematics at Imperial College London.

Blanka holds a PhD in Financial Mathematics from ETH Zurich, a postgraduate degree (Diplom) in Mathematics from the University of Bonn, and an MSc in Economics from The University of Hong Kong. In her research she lays a particular emphasis on the applicability of her research and maintains close collaborations with the industry, including: JP Morgan, Deutsche Bank, Zeliade Systems and AXA.

Her research interests are in the area of Stochastic Analysis and Mathematical Finance. They include (but not limited to)

Numerical methods as well as machine learning techniques for option pricing, forecasting and simulation. Laplace methods on Wiener space and heat kernel expansions. Smile asymptotics for local- and stochastic volatility models with a particular emphasis on rough volatility models and also SABR-type models.

Dr. Youssef Elouerkhaoui is a Managing Director and the Global Head of Markets Quantitative Analysis at Citi. His department supports quant modelling, product development and data science activities across all markets businesses. Before his current role, he ran the Credit and Commodities Quantitative Analysis teams and he was in charge of CVA, Funding and Regulatory Capital for his businesses. Prior to Citi, he was a Director in the Fixed Income Derivatives Quantitative Research Group at UBS, where he was in charge of model development for the Structured Credit Desk. And before UBS, Youssef was a Quantitative Research Analyst at Credit Lyonnais supporting Interest Rates Exotics. He is a graduate of Ecole Centrale Paris and he holds a PhD in Mathematics from Paris-Dauphine University. He is well-known for his work on credit correlation modelling with the Marshall-Olkin copula and has published a book on the topic: “Credit Correlation: Theory and Practice”. His current research interests include: Machine Learning applications in Finance, and in particular, using Deep Learning methods in traditional derivatives pricing models.