My main research interest lies at the intersection of operations research and finance, and specifically in problems of decision-making under uncertainty in financial markets. The methodological base of my research is stochastic optimization, in particular stochastic programming, dynamic programming, and approximate dynamic programming (closely related to reinforcement learning).
The aim of my research is to support improved decisions in practice, both through practically relevant applications and through methodological contributions to stochastic optimization. Applications in my work include financial risk management and portfolio choice in the presence of transaction costs. On the methodological side, my work includes scenario generation through importance sampling and the study of multi-stage stochastic programming models. Parts of this work have been carried out in collaboration with John R. Birge (University of Chicago Booth).
Since 2022, I work most of my time as a senior quantitative analyst in model validation at a bank, where I independently validate front-office valuation, market risk, counterparty credit risk, and xVA models. This practitioner role informs how I teach financial mathematics to students.
Background
I hold a PhD in Financial Mathematics from the division of Production Economics at Link枚ping University, and Master degrees in Industrial Engineering and Management as well as Economics. I have been a visiting PhD student at Chicago Booth School at Business.
