734-615-0618

Applications:
Biological Sciences, Informatics
Methodologies:
Artificial Intelligence, Data Integration, Data Mining, Graph-Based Methods, Machine Learning
Relevant Projects:

NIH/NHGRI, NIH/NIAID, NIH/NIMH, NIH/NIDCR, Chan-Zuckerberg Initiative


Joshua Welch

Assistant Professor

Computational Medicine and Bioinformatics

Our research aims to address fundamental problems in both biomedical research and computer science by developing new tools tailored to rapidly emerging single-cell omic technologies. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease. As computational method developers, we seek to both employ and advance the methods of machine learning, particularly for unsupervised analysis of high-dimensional data. We have particular expertise in manifold learning, matrix factorization, and deep learning approaches.