Professor Peter K. Friz is Einstein Professor of Mathematics at Technische Universität Berlin and is affiliated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and financial mathematics, where he has made significant contributions to pathwise approaches to stochastic differential equations. He completed his PhD at New York University’s Courant Institute under S. R. S. Varadhan and has previously held positions at the University of Cambridge and the Radon Institute. He is the recipient of major European Research Council grants and currently serves as spokesperson of the DFG Collaborative Research Centre on “Rough Analysis, Stochastic Dynamics, and Related Fields”.
Lecture 1: Rough Stochastic Differential Equation: An Introduction with Applications
3:00pm-4:00pm , Thursday 28 May 2026
Room 340, Huxley Building
ABSTRACT:
Rough stochastic differential equations (RSDEs) are common generalisations of Itō SDEs and Lyons RDEs. Since their introduction in 2021 (Hocquet-Lê-F) they have emerged as a powerful tool in several areas of applied probability, including non-linear stochastic filtering, pathwise stochastic optimal control, volatility modelling in finance and mean-field analysis conditional on common noise. This talk will offer an overview of motivating examples, including some classes of stochastic partial differential equations, before presenting the basic ideas of the theory.
Lecture 2: Rough Stochastic Differential Equation: Randomization, Control and Mean-Field
3:00pm-4:00pm, Friday 29th May 2026
Room 340, Huxley Building
ABSTRACT:
In the second talk, I will come back the motiving examples in greater detail, notably pathwise stochastic control and conditional mean-field analysis and discuss in detail how to adapt general RSDE tools to these problems. From an interacting particle perspective, we make full use of the martingale structure of idiosyncratic noise, while no specification of the law of the (rough) common noise is required. The theory naturally comes with quantitative estimates depending on all data. Time permitting, we shall also present recent work on mean field games with rough common noise.
