Michael Wagner, PhD, works on applications of machine learning techniques to bioinformatics problems such as protein structure prediction, disease classification and protein identification. His research lab currently is investigating machine-learning based scoring algorithms for peptide mass fingerprinting to better understand how to optimally mine mass spectrometry data to make high-confidence predictions of protein identities. The underlying computational engine for many of these problems is a massively parallel implementation of a linear programming solver (PCx), which can solve large-scale support vector regression, support vector machine and linear feasibility problems. 

Dr. Wagner also is involved in collaborations to perform genome-wide association studies, where his work has concentrated on developing an adequate, rapid data flow infrastructure that includes parallelized genotype calling algorithms.

View PubMed Publications