Statistical and Computational Challenges Arising from Ubiquitous Molecular Measurements (Cancelled) (20w5199)

Organizers

(Johns Hopkins Bloomberg School of Public Health)

(Hospital for Sick Children/UofT)

Sara Mostafavi (University of Washington)

Elizabeth Purdom (UC Berkeley)

Description

The Casa Matemática Oaxaca (CMO) will host the "Statistical and Computational Challenges Arising from Ubiquitous Molecular Measurements" workshop in Oaxaca, from August 9 to August 14, 2020.


The cost of collecting molecular measurements about our DNA, RNA, and their abundances has dropped dramatically over the last five years. Genomic data is now ubiquitous across biology and medicine. But while the data have become ever cheaper to collect and store, efficiently extracting useful information from these measurement still remains a significant challenge. Solving this challenge could better allow us to use new knowledge about our genomes to understand our ancestry, how our bodies develop, and how we can predict and treat diseases.

This workshop brings together experts in biology, computational biology, machine learning, and statistics to answer some of the large, unsolved problems presented by the explosion of molecular information we are experiencing. They will help to create ideas for extracting, understanding, and testing the information we get from these experiments to benefit the biological and medical sciences.


The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides 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 is located at The Banff Centre in Alberta and 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).