Tue, 20 Jan 2026
16:00
16:00
L6
Joint Moments of CUE Characteristic Polynomial Derivatives and Integrable Systems
Fei Wei
(University of Sussex)
Abstract
In this talk, I will begin by giving some background on the joint moments of the first-order derivative of CUE characteristic polynomials, as well as the polynomials themselves, evaluated inside or on the boundary of the unit disk. I will then introduce some of my recent work on this topic and discuss its connections to Painlevé equations. Finally, I will list a few interesting and largely unexplored problems in this area. This talk draws on collaborative work with Thomas Bothner, on some work with Nicholas Simm, and on additional collaborations with Theodoros Assiotis, Mustafa Alper Gunes, and Jon Keating.
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The Micro-Internship Programme offers Oxford students the chance to gain hands-on experience by observing and supporting real projects across a wide range of sectors, from academia and heritage to law, publishing, tech, and beyond.. These short placements last up to five days, run in Weeks 9 and 10 of Hilary term, and may be in person, remote, or hybrid. Apply by 1 February.
Decoupling generalised configuration spaces on surfaces
Basualdo Bonatto, L
Transactions of the American Mathematical Society
The week will celebrate locally led initiatives and events across departments, colleges, societies, sports teams and more. This year’s theme is “The top 5 things to do for the environment as a [...]".
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Neural networks for learning macroscopic chemotactic sensitivity from microscopic models
Erban, R
SIAM Journal on Life Sciences
Thu, 05 Feb 2026
16:00 -
17:00
L5
Linking Path-Dependent and Stochastic Volatility Models
Cephas Svosve
((Mathematical Institute University of Oxford))
Abstract
We explore a link between stochastic volatility (SV) and path-dependent volatility (PDV) models. Using assumed density filtering, we map a given SV model into a corresponding PDV representation. The resulting specification is lightweight, improves in-sample fit, and delivers robust out-of-sample forecasts. We also introduce a calibration procedure for both SV and PDV models that produces standard errors for parameter estimates and supports joint calibration of SPX/VIX smile.