Statistical Challenges for Complex Brain Signals and Images (23w5030)

Organizers

(Lancaster University)

Mark Joseph Fiecas (University of Minnesota)

(King Abdullah University of Science and Technology)

Description

The Casa Matemática Oaxaca (CMO) will host the “Statistical Challenges for Complex Brain Signals and Images” workshop in Oaxaca, from April 30 - May 5, 2023.


This workshop will focus on developing novel statistical methodology to analyze brain signals, which is crucial to understanding normal brain function and alterations associated with neurological and mental diseases. Brain signals are complex and are a reflection of the complexity of the unobserved brain processes. Thus, the primary considerations for developing statistical models are flexibility, generalizability, and incorporation of known biology. In this workshop, we will discuss the most recent challenges in this area and how the current methods need to be improved to better describe the observed brain signals.


We will have three brainstorming sessions focused on the following tracks: Track 1: Challenges in developing high dimensional models for brain signals. Track 2: Computational challenges for pre-processing, model implementation, visualization, and software development. Track 3: Machine Learning algorithms and approaches to complement statistical techniques.


The Casa Matemática Oaxaca (CMO) in Mexico, and the Banff International Research Station for Mathematical Innovation and Discovery (BIRS) in Banff, are collaborative Canada-US-Mexico ventures that provide an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station in Banff is supported by Canada’s Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta’s Advanced Education and Technology, and Mexico’s Consejo Nacional de Ciencia y Tecnología (CONACYT). The research station in Oaxaca is funded by CONACYT