Dimension reduction for microbiome data

Vast amounts of new data, including sequencing data from microbiome, generated by researchers and industry present challenging problems for extracting meaningful information. The main goal of this project is to develop a convenient software package to reduce the complexity of microbiome data, and more generally any kind of datasets showing similar properties. As this tool will help to efficiently study imperfect and complex datasets which are ubiquitous in particular in biology and health, we will consider other potential applications, e.g. studying health records from intensive care units. A utilization in data collected from sensors present in connected objects could also be valuable. In a nutshell, this project will help analyse data for which there is no real alternative currently, and this applies notably to some of the newest kinds of data, which have important potential in biology, health and industry.

Faculty Supervisor:

Khanh Dao Duc

Student:

Partner:

École Polytechnique

Discipline:

Mathematics

Sector:

Education

University:

The University of British Columbia

Program: