Past Events

This talk introduces a newly developed computational method for rigorously evaluating the Hadamard derivative of Laplacian eigenvalues, which plays an important role in studying shape optimization problems. To evaluate the Hadamard derivative, this...

Transportation of measure underlies many powerful, contemporary tools for Bayesian inference, density estimation, and generative modeling. The essential idea is to couple the target probability distribution with a simple, tractable ``reference''...

In my talk I will discuss the notion of local volume for singularities. For the special case of toric singularities this turns out to be closely related to the notion of Mahler volume in convex geometry. This opens a connection between algebraic...

The directed landscape is a random directed metric on the plane that arises as a scaling limit of a classical metric models in the KPZ universality class. In this talk, we will discuss a functional large deviation principle (LDP) for the entire...

The directed landscape is a random directed metric on the plane that arises as the scaling limit of classical metric models in the KPZ universality class. In this talk, we will discuss a functional large deviation principle (LDP) for the entire...

We analyse the metastable behaviour of the disordered Curie-Weiss-Potts (DCWP) model subject to a Glauber dynamics. The model is a randomly disordered version of the mean-field q-spin Potts model (CWP), where the interaction coefficients between...

Speaker list From Paris From UBC From UAlberta From U. Washington From POSTECH AI center Luca Nenna Khanh Dao Duc Jiajin Li Jakwang Kim Andrew Warren Rentian Yao Wenjun Zhao Forest Kobayashi Brendan Pass Daniyar Omarov Soumik Pal Namkyeong Cho...

By using Grand Canonical Optimal Transport we provide a counter-example showing that the ground state energy of electrons in an external Coulomb potential is not always a convex function of the number of electrons. This property has been conjectured...

A classical problem in linear algebra is to understand what are the linear subspaces of the space of $m\times n$ matrices such that no matrix in the space has full rank. This problem has connections to theoretical computer science, more precisely...

Recently, Machine Learning (ML) approaches to data assimilation and modelling have been very successful in interpreting large amounts of data, such as human behavior prediction, marketing, etc. However, direct applications of machine learning methods...