Bioinformatics, Genetics, Genomics
Artificial Intelligence, Deep Learning, Graph Theory and Graph-based Methods, Heterogeneous Data Integration, High-Dimensional Data Analysis, Machine Learning, Sparse Data Analysis
Relevant Projects:

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

Joshua Welch

Assistant Professor

Computational Medicine and Bioinformatics


Computer Science and Engineering

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.