Probability Theory

Antonio Ocello tests the idea that heavy-tailed noise improves diffusion and flow-based generative models by matching heavy-tailed data and encouraging diverse samples. Replacing Gaussian noise also changes the estimation task. A combined theoretical and experimental study derives sampling-error bounds for representative heavy- and light-tailed diffusion models, finding that heavy-tailed noise makes statistical estimation harder and produces less favourable bounds. Experiments with synthetic and real data recover the predicted trade-off. The results question whether heavy-tailed initialization reliably improves exploration of rare regions. This is joint work with Hamza Cherkaoui and Hélène Halconruy. Online via Microsoft Teams. Friday 2 October 2026 at 15:30 CEST / 13:30 UTC / 14:30 BST (Europe/Paris). Follow the Microsoft Teams link beneath this occurrence on the seminar page; the public route offers browser or app access. Ocello is affiliated with ENSAE Paris. Organized by GAMEX — Generative AI Modeling for Extreme Events, with GLE²N and CIRCE support; the series welcomes scientific exchange beyond the network.

The Society for Industrial and Applied Mathematics brings together applied mathematicians, probabilists, statisticians, computer and data scientists, economists and industry practitioners to examine mathematical and computational methods in quantitative finance. Themes include algorithmic trading, agentic and generative AI, climate finance, digital assets, credit and cyber risk, financial data science, insurance mathematics, market microstructure, stochastic control, systemic risk and volatility modelling. Minisymposium proposals are due 17 November 2026, followed by contributed lecture, poster and minisymposium-presentation abstracts on 15 December 2026.

Optimal transport is a mathematical method to define a distance between probability distributions. This is particularly useful in various domains, including physics, biology, machine learning, and economics, among others. After introducing the Optimal Transport (OT) problem at finite temperature, we show how it can be formulated as a statistical physics problem. This approach allows us to derive very efficient algorithms to effectively compute the distance between two probability distributions. The a priori unrelated Schrödinger bridge (SB) problem is presented, and it is shown to be a dynamical version of the optimal transport problem. Indeed, the Schrodinger bridge looks for the most probable path in probability distribution space, which connects two given probabilities. The Schrodinger bridge problem, originally devised for freely diffusing particles, can be generalized to the case of interacting particles. It can be formulated in terms of functional integrals over bosonic fields, which allows us to derive partial differential equations that characterize the most probable paths in probability space. CARL VAN VREESWIJK MEMORIAL LECTURE 2026. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-04-15. Recording duration: 00:55:05.

Develop and apply quantum resource theory for Extended Wigner’s Friend scenarios, where the presence of another observer can affect measurement outcomes. The work involves analytical quantum information and probability theory, together with numerical simulation, and relates to Bell nonlocality. This FAPESP-funded doctoral position is expected to start in March 2027. Apply by 16 November 2026 with a CV, a motivation letter of up to two pages and contact information for two academic referees, following the official advertisement.

Antonio Ocello tests the idea that heavy-tailed noise improves diffusion and flow-based generative models by matching heavy-tailed data and encouraging diverse samples. Replacing Gaussian noise also changes the estimation task. A combined theoretical and experimental study derives sampling-error bounds for representative heavy- and light-tailed diffusion models, finding that heavy-tailed noise makes statistical estimation harder and produces less favourable bounds. Experiments with synthetic and real data recover the predicted trade-off. The results question whether heavy-tailed initialization reliably improves exploration of rare regions. This is joint work with Hamza Cherkaoui and Hélène Halconruy. Online via Microsoft Teams. Friday 2 October 2026 at 15:30 CEST / 13:30 UTC / 14:30 BST (Europe/Paris). Follow the Microsoft Teams link beneath this occurrence on the seminar page; the public route offers browser or app access. Ocello is affiliated with ENSAE Paris. Organized by GAMEX — Generative AI Modeling for Extreme Events, with GLE²N and CIRCE support; the series welcomes scientific exchange beyond the network.

The Society for Industrial and Applied Mathematics brings together applied mathematicians, probabilists, statisticians, computer and data scientists, economists and industry practitioners to examine mathematical and computational methods in quantitative finance. Themes include algorithmic trading, agentic and generative AI, climate finance, digital assets, credit and cyber risk, financial data science, insurance mathematics, market microstructure, stochastic control, systemic risk and volatility modelling. Minisymposium proposals are due 17 November 2026, followed by contributed lecture, poster and minisymposium-presentation abstracts on 15 December 2026.

Develop and apply quantum resource theory for Extended Wigner’s Friend scenarios, where the presence of another observer can affect measurement outcomes. The work involves analytical quantum information and probability theory, together with numerical simulation, and relates to Bell nonlocality. This FAPESP-funded doctoral position is expected to start in March 2027. Apply by 16 November 2026 with a CV, a motivation letter of up to two pages and contact information for two academic referees, following the official advertisement.