Neo Christopher Chung

Neo Christopher Chung

Overview
Modern biotechnologies collect an ever-increasing amount of data about model organisms and humans. To facilitate data-driven discoveries in biology and medicine, I develop and apply statistical methods for large-scale experimental and observational studies. Particularly, I have extensive experience in analyzing genomics, proteomics, and clinical data to identify underlying signatures of diseases, molecular pathways, and environmental factors. My methodological research focuses on unsupervised learning, that can identify and utilize systematic patterns of variation.

Appointments
Assistant Professor, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw
Visiting Professor, NIH BD2K Center of Excellence for Big Data Computing, University of California, Los Angeles

Education
Fogarty Global Health Fellow [adviser Ben Chi], Centre for Infectious Disease Research in Zambia & UNC-Chapel Hill
Ph.D. in Quantitative and Computational Biology [adviser John Storey], Princeton University
B.S.E. in Biomedical Engineering, Duke University

Publications
NC Chung (2018). Statistical significance of cluster membership for determination of cell identities in single cell genomics. Biorxiv Pre-print
HJ Painter, NC Chung, A Sebastian, I Albert, JD Storey, M Llinás (2018). Genome-wide real-time in vivo transcriptional dynamics during Plasmodium falciparum blood-stage development Nature Communications
NC Chung, C Bolton-Moore, R Chilengi, MP Kasaro, JSA Stringer, BH Chi (2017). Patient engagement in HIV care and treatment in Zambia, 2004–2014. Tropical Medicine & International Health
NC Chung, J Szyda, M Frąszczak, 1000 Bull Genomes Project (2017). Population structure analysis of bull genomes of european and western ancestry. Scientific Reports
NC Chung, JD Storey (2015). Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics
J Kim, N Ghasemzadeh, DJ Eapen, NC Chung, JD Storey, AA Quyyumi, G Gibson (2014). Gene expression profiles associated with acute myocardial infarction and risk of cardiovascular death. Genome Medicine