Joint SIAM-BFS Mathematical Finance Online Seminar
What started during the pandemic, as the Bachelier Finance Society One World Seminars (online), is still active.
We have now joined forces with the Society for Industrial and Applied Mathematics (SIAM) to implement an online seminar series jointly operated by the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME) (http://wiki.siam.org/siag-fm/index.php/Current_events) and BFS. The online seminar will still be announced in the current way.
The online talks will take place on a Thursday each month (with a few exceptions).
Find the list of the Joint SIAM-BFS Mathematical Finance Online Seminar below.
2026
Date: Thursday, 14 May 2026
Speaker: Ruimeng Hu (UCSB)
Title: Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning
Abstract: Machine learning has become an increasingly useful tool for solving high-dimensional stochastic control and game problems that are difficult to handle with classical numerical methods. In this talk, I will begin with a general overview of several learning-based approaches for stochastic control and games, including direct policy parameterization, PDE-based methods, and BSDE-based methods, and discuss how these ideas extend to multi-agent and mean-field settings. I will then focus on recent joint work on a new learning framework for mean-field games, called mean-field actor-critic flow. The method combines actor-critic ideas from reinforcement learning with an optimal transport-based update of the population distribution, leading to a coupled learning dynamic for the value function, policy, and mean-field law. I will describe the main algorithmic ideas, discuss a global exponential convergence result under suitable time-scale separation, and present numerical examples illustrating the method.
Thursday, 14 May 2026, 19:00 (GMT +2)
Link to registration:
https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg
Date: Thursday, 11 June 2026
Speaker: Sergio Pulido (ENSIIE)
Title: TBA
Abstract: TBA
Thursday, 11 June 2026, 19:00 (GMT +2)
Link to registration:
https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg
Date: Thursday, 9 April 2026
Speaker: Yufei Zhang (Imperial College London)
Title: An alpha-potential game framework for dynamic N-player games
Abstract: Game theory has a long history, yet identifying Nash equilibria in dynamic non-cooperative games remains a fundamental challenge with significant computational and conceptual complexity. Over the past decade, mean field game theory has emerged as a pivotal framework, offering important theoretical insights and computational advances for the analysis of large-scale stochastic games. However, mean field games require homogeneity and weak interactions among players and focus only on the limiting behavior when the number of players goes to infinity.
In this talk, we present a new paradigm for dynamic N-player games, called alpha-potential games, where the change of a player’s objective function resulting from a unilateral deviation of her strategy is equal to the change of an alpha-potential function up to an error alpha. Within this framework, the problem of computing approximate Nash equilibria reduces to a stochastic control problem for the alpha-potential function, significantly simplifying both analysis and computation. The parameter alpha also reveals important structural properties of the game, such as the population size, the intensity of player interactions, and the degree of heterogeneity across players. We will discuss through simple examples some recent theoretical and algorithmic developments, along with a few open problems for this new game framework.
Thursday, 9 April 2026, 19:00 (GMT +2)
Link to registration:
https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg
Date: Thursday, 12 March 2026
Speakers: Eduardo Abi Jaber (Ecole Polytechnique)
Title: Path-Signatures: Memory and Stationarity
Abstract: We explore the interplay between path-signatures, memory, and stationarity, highlighting their implications for machine learning, representation of stochastic processes and applications in mathematical finance. In a first part, we provide explicit series expansions to certain stochastic path-dependent integral equations in terms of the path signature of the time augmented driving Brownian motion. Our framework encompasses a large class of stochastic linear Volterra and delay equations and in particular the fractional Brownian motion with a Hurst index H in (0, 1). Our expressions allow to disentangle an infinite dimensional Markovian structure. In addition they open the door to: (i) straightforward and simple approximation schemes that we illustrate numerically, (ii) representations of certain Fourier-Laplace transforms in terms of a non-standard infinite dimensional Riccati equation with important applications for pricing and hedging in quantitative finance. In a second part, we introduce a time-invariant version of the signature: the fading-memory signature, with powerful algebraic, analytic and probabilistic properties and applications to learning stationary relationships in time series. This is based on joint works with Paul Gassiat, Louis-Amand Gérard, Yuxing Huang, Dimitri Sotnikov.
Thursday, 12 March 2026, 18:00 (GMT +1) – the US is switching to daylight saving time on 8 March
Link to registration:
https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg
Date: 12 February 2026
Speaker: Christian Bayer (WIAS Berlin)
Title: Global and local regression: a signature approach with applications
Abstract: The path signature is a powerful tool for solving regression problems on path space, i.e., for computing conditional expectations $\mathbb{E}[Y | X]$ when the random variable $X$ is a stochastic process — or a time-series. We provide new theoretical convergence guarantees for two different, complementary approaches to regression using signature methods. In the context of global regression, we show that linear functionals of the robust signature are universal in the $L^p$ sense in a wide class of examples. In addition, we present a local regression method based on signature semi-metrics, and show universality as well as rates of convergence. Based on joint works with Davit Gogolashvili, Luca Pelizzari, and John Schoenmakers.
Thursday, 12 February 2026, 19:00 (GMT +1)
Link to registration:
https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg
