Arjun Krisnan, PhD
- Faculty, Cellular & Molecular, Computational
Assistant Professor, Computational Mathematics, Science, and Engineering
Ph.D., 2010, Virginia Tech
The Krishnan lab develops and applies computational data-driven approaches for resolving, understanding, and tackling the heterogeneity of complex traits and diseases. We build the relevant genome-scale models and generate systematic predictions by leveraging large genomics/biomedical data collections using statistics and machine learning. We are particularly interested in using this strategy to: 1) unravel the genetic strata/subtypes of complex traits/diseases, 2) understand age- and sex-specificity of traits/disease, 3) identify cross-species models for human traits/diseases. We work in synergy with experimental/clinical researchers to motivate our methods and put our predictions to test in human and model systems. Our overarching goal is to: a) gain more nuanced and accurate insights into the genes and networks underlying physiology, complex diseases, and clinical phenotypes, and b) use these insights to mechanistically link an individual's genomic profiles to a precise assessment of her/his physiological/clinical traits, risks, and outcomes.