Past Events

We construct a supercritical interacting measure-valued diffusion with representative particles that interact with the center of mass by using the historical stochastic calculus of Perkins to modify a super Ornstein-Uhlenbeck process. In doing so we...

Summer School The summer school will be a mix of theoretical and practical courses, which will be taught by ten world experts in the field. It will be organized in modules (2×3 hours of lectures) over 5 days. The range of topics that will be covered...

In joint work with Tom Salisbury, we study a class of random directed graphs on Z^d that includes site percolation and oriented percolation. Motivated by the study of random walks in these random environments, we focus on those models in two...

Probabilistic and decision-theoretic graphical models, such as Bayesian networks, provide compact yet powerful formalisms for building intelligent systems, known as agents in the field of artificial intelligence, that must function in partially...

The wave concept is arguably the most widely used single concept in all of physical science. It links together such diverse disciplines as geophysics, oceanography, meteorology, astrophysics, and physiology. The wave concept is one that unifies many...

The Summer School will feature three 10-Lecture Series: 1. The structure of nuclear C*-algebras (Brown and Toms) 2. KK-theory and the Baum-Connes conjecture (Emerson and Meyer) 3. C*-dynamical systems (Laca and Neshveyev)

Probably the most thoroughly studied mechanism that can explain the evolution and maintenance of costly cooperation among selfish individual is population structure. In the past years, hundreds of papers have mathematically modeled how cooperation...

The main goal of this presentation is to describe an architecture for solving large hybrid Bayesian networks (BNs) with discrete, continuous and deterministic variables. In the presence of deterministic variables, we have to deal with non-existence...

Solving problems regarding the optimal control of partial differential equations (PDEs) - a type of PDE-constrained optimization - is a frontier area of numerical analysis. Of particular interest is the problem of flow control, where one would like...

The main goal of this presentation is to describe an architecture for solving large hybrid Bayesian networks (BNs) with discrete, continuous and deterministic variables. In the presence of deterministic variables, we have to deal with non-existence...