Eliciting Structure in Genomics Data

August 30-September 3, 2021

Methods for dimension reduction play a critical role in a wide variety of genomic applications. Indeed, as technology develops, and datasets grow in both size and complexity, the need for effective dimension reduction methods that help visualize and distill the primary structures remains as essential as ever. The development and provision of effective methods for dimension reduction involves connecting a series of areas of expertise: from theory to algorithms, implementations and applications.

Opening Workshop: An introduction to the area of distributed solutions

This conference will consist of three series of lectures, the aim of which is to present the main issues at stake in the analysis of distributed solutions to complex societal problems and to describe some mathematical tools to handle these questions. Applications range from collective behavior in economy and finance to crowd analysis and the spread of diseases, and from machine learning to stochastic optimization and artificial intelligence.

Mean-field approaches in Machine Learning and Statistics

The aim of this workshop is to gather specialists from machine learning and statistics to applied probability and analysis who share a common interest in mean-field models. Potential applications range from mean-field games to stochastic algorithms and simulations, neural networks and frequentist or Bayesian statistical inference for interacting systems.

Aggregate dynamics in models with heterogeneous agents

This conference invites participants to present and discuss current research on models with the following features. The heterogeneous agents feature refers to agents solving dynamic problems subject to idiosyncratic random shocks, each agent with non-trivial interactions with the remaining agents. The “aggregate dynamics” feature refers to the focus on the understanding and characterization of the dynamics of the entire system, either itself subject to aggregate shock or as a deterministic system, using analytical or numerical techniques. Examples of such models are variants of Mean Field Games. Models will have applications in several fields in economics and intersections with other disciplines.

Mean-Field Models for interacting agents

Interacting particle models are a powerful mathematical tool to model the behavior of large groups in economics as well as in the life and social sciences. Understanding the dynamics of these systems on different levels is of great importance, as it gives insights into the emergence of many complex phenomena. In this workshop we will focus on recent developments and emerging challenges in the derivation and analysis of these micro- and mean-field models. It will feature different perspectives and approaches to these challenges, by bringing together applied mathematicians working at the interfaces between statistics, social sciences and the life sciences.

Applications of Mean Field Games: from Models to Practice

The paradigm of Mean Field Games (MFG) has become a major connection between distributed decision-making and stochastic modeling. Starting out in the stochastic control literature, it is gaining rapid adoption across a range of industries. The objective of this workshop is to give a clear vision of how MFG tools are being used in practical settings, both in complement and in contrast to the usual methodologies. The workshop will gather researchers both from industry and universities and will focus on diverse application areas.