Date/Time : 28 Jan 2026 16:00
Venue : S17 #04-04 (Seminar Room 3)
Speaker : Jakub Skrzeczkowski, University of Oxford

We consider a nonlocal approximation of the quadratic porous medium equation where the pressure is given by a convolution with a mollification kernel. It is known that when the kernel concentrates around the origin, the nonlocal equation converges to the local one. However, the question of how fast it converges is not well-explored in the literature. In one spatial dimension, for a particular choice of the kernel, and under mere assumptions on the initial condition, we quantify the rate of convergence in the 2-Wasserstein distance. First, we reprove the recent result of Amassad and Zhou (2025) yielding the rate of √ε using a simpler technique based on the Evolutionary Variational Inequality for both nonlocal and local equations. Next, using numerical simulations, we observe that the rate of √ε is not optimal. Therefore, we obtain a new formula for the Wasserstein distance between two abstract gradient flows which, when applied to the problem, together with Aronson-Benilian estimates, yields the rate of ε, suggested to be optimal by numerics. This is joint work with J.A. Carrillo, S. Fronzoni (Oxford), C. Elbar (Lyon), P. Gwiazda (Warsaw).

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Date/Time : 27 Jan 2026 16:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Ivan Au Yeung , Nvidia

This talk explores solving nonlinear p-Laplacian problems using NVIDIA PhysicsNeMo, a GPU-accelerated framework for building and training physics AI models at scale. The p-Laplacian is motivated through applications such as nonlinear diffusion and variational problems, with emphasis on the numerical difficulties that arise for large values of p and in complex 2D and 3D geometries. The core of the talk is a PhysicsNeMo-based workflow that recasts the p-Laplacian into Physics-Informed Neural Network and operator-learning formulations, enabling end-to-end training directly from PDE specifications and boundary conditions while leveraging multi-GPU hardware. Numerical experiments compare these neural solvers with traditional mesh-based methods in terms of accuracy, robustness for high p. The talk concludes with practical guidelines for setting up p-Laplacian problems and tuning architectures and loss terms within PhysicsNeMo.

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Date/Time : 27 Jan 2026 15:00
Venue : Conference room (S17 Level 4)
Speaker : Deborah Hendrych, Zuse Institute Berlin

Mixed Integer Nonlinear Problem (MINLP) solvers have traditionally relied on Outer Approximation schemes. Boscia, a recent framework, takes a different approach by leveraging the Frank-Wolfe algorithm—a first-order method for convex optimization—within a branch-and-bound structure. This approach eliminates the need for additional cutting planes while maintaining the oracle-based flexibility of Frank-Wolfe and capitalizing on recent advances in mixed-integer programming solvers. This talk present the intuition behind the framework. highlights the main advantages and showcase the usage and modelling through two examples: portfolio optimization and D-optimal experiment design.

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Date/Time : 26 Jan 2026 15:00
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Ernest Tan, National University of Singapore

Research in quantum information theory, particularly for quantum cryptography or quantum computing, often makes use of numerical methods and software for linear algebra. In earlier literature, most work was based around well-established methods such as semidefinite programming. However, more recent developments have highlighted the need to handle broader classes of convex optimization problems, involving a family of generalizations of the Kullback-Leibler divergence or relative entropy, for which many questions remain open. Furthermore, results from convex analysis have also proven useful in deriving powerful techniques for statistical estimation in quantum information, connected to large-deviation theory. I will present an introductory overview of some active areas of research and open problems in the field, in particular the areas that could benefit from further insights in convex optimization.

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Date/Time : 23 Jan 2026 12:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Qiang Liu, Shanghai University of Finance and Economics

We study the estimation of leverage effect and volatility of volatility by using high-frequency data with the presence of jumps. We first construct spot volatility estimator by using the empirical characteristic function of the high-frequency increments to deal with the effect of jumps, based on which the estimators of leverage effect and volatility of volatility are proposed. Compared with existing estimators, our method is valid under more general jumps, making it a better alternative for empirical applications. Under some mild conditions, the asymptotic normality of the estimators is established and consistent estimators of the limiting variances are proposed based on the estimation of volatility functionals. We conduct extensive simulation study to verify the theoretical results. The results demonstrate that our estimators have better performance than the existing ones, especially when the jump is of infinite variation. Besides, we apply our estimators to a real high-frequency dataset, which reveals nonzero leverage effect and volatility of volatility in the market. 

Register here: https://forms.office.com/r/Pa3vvSfTLu

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Date/Time : 21 Jan 2026 17:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Andre Nies, University of Auckland

Let R be the structure of r.e. many-one degrees. Lachlan showed that each finite distributive lattice is embeddable in R as an initial segment, thereby showing its theory is hereditarily undecidable. In this retrospective of some of my results from the 1990s, I will discuss how this proof in fact leads to undecidability of the Sigma-3 theory. I showed that the whole theory is Turing equivalent to true arithmetic. The technique used there paved the way for the similar result for the theory of the lattice of r.e. sets (with Harrington).

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Date/Time : 21 Jan 2026 14:00
Venue : S17 #04-04 (Seminar Room 3)
Speaker : Ming Hong Tee, Boston College

Given any Jordan domain U in the Riemann sphere equipped with the hyperbolic metric, one can construct the Epstein surface in hyperbolic 3 space corresponding to it. One important property of the Epstein surface is that its principal curvatures are related to the Schwazian derivative of the uniformization map of U. In this talk, we will exploit this property to relate the bending lamination of a pleated surface with the Schwazian derivative. Furthermore, if U is a domain for a Weil-Peterson curve, we will show that the hyperbolic metric and the quasi-hyperbolic metric are arbitrary closed and the two corresponding Epstein surfaces are arbitrary closed.

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Date/Time : 21 Jan 2026 16:30
Venue : S17 #05-11 (Seminar Room 5)
Speaker : Kim Jeongho, Kyung Hee University

In this talk, I will present two different topics on numerical methods for solving PDEs.

In the first part, I will focus on the numerical methods for two-dimensional quantum mechanical systems interacting with electromagnetic fields. Precisely, we will discuss finite difference time domain method and time splitting Fourier Pseudospectral methods for Chern-Simons-Schrodinger/Dirac equations. In the second part, I will move on to the adaptive finite difference method for solving the Poisson equation. I will introduce the quad/octree structure on which the Poisson equation is solved. This provides a stable Hodge decomposition step in the projection method for incompressible flow.

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Date/Time : 19 Jan 2026 16:00
Venue : S17 #04-06 (Seminar Room 1)
Speaker : Jasper Lee, University of California, Davis

Mean estimation is perhaps the most fundamental task in statistics, yet there is a divide between the standard theory and practice. The standard theory recommends taking the sample mean, but whose brittleness forces users to apply ad-hoc outlier removal techniques in practice, leading to documented cases of wrong experimental conclusions. On the other hand, theoretical algorithms designed specifically to mitigate such brittleness (e.g. median-of-means and high-dimensional generalizations) have poor empirical performance that they cannot/should not be adopted either.

In this talk, I’ll discuss our efforts to fully resolve the mean estimation problem, achieving (minimax) estimation error that is sharp even in the leading multiplicative constant. I will present a new multivariate mean estimator that has sharp estimation error in any (small) constant dimensions (in preparation), generalizing our previous 1-dimensional mean estimator (FOCS 2021, ICML 2025). I will also discuss a very simple multivariate mean estimator that achieves sharp error guarantees in the “very high-dimensional” regime (ITCS 2022).

The constant-dimensional mean estimator is joint work with Suraj Mathashery, Paul Valiant and Kun Wang, in preparation. The very high-dimensional mean estimator is joint work with Paul Valiant, which appeared in ITCS 2022.

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