Christopher E. Gillies, Ph.D., is an Assistant Research Scientist in the Department of Emergency Medicine. Dr. Gillies joined the University of Michigan in 2013 after graduating with his Ph.D. from Oakland University. His Ph.D. focused on the application of machine learning for bioinformatics data (including gene expression datasets).
Dr. Gillies began his career at UM, working on Statistical Genetics focused on kidney disease, where he published articles in high impact journals including the American Journal of Human Genetics, Nature Genetics, and the New England Journal of Medicine. Dr. Gillies has over 20 peer-reviewed publications applying statistical and machine learning models to bioinformatic datasets.
In 2018, he joined the Michigan Center for Integrative Research in Critical Care (MCIRCC) as a Data Scientist. At MCIRCC, Dr. Gillies has been working on building machine learning models of patient deterioration using electronic health records (EHR). He has performed work on diseases, including sepsis and acute respiratory distress syndrome (ARDS). His work at MCIRCC is not limited to based machine learning using EHR data. Dr. Gillies has also been developing advanced models using both blood and breath metabolomics datasets.
Dr. Gillies is currently pursuing funding for building new models of patient deterioration, including EHR, and waveform data applied to sepsis. Additionally, he has applied for funding for developing advanced methodologies for handling missing data for metabolomics datasets. In terms of methodological approaches, Dr. Gillies is interested in graphical models, Bayesian analysis, and deep learning.