Chris is a PhD student in Quantitative and Computational Biosciences whose research focuses on developing agentic AI systems for extracting the knowledge of multi-omic interactions from scientific literature and orchestrating them into knowledge graphs, leveraging that knowledge by integrating it into deterministic and differential genetic diagnostic frameworks.
His current research aims towards integrating multi-omic data to diagnose rare diseases with high precision, and to progress personalized medicine by identifying the heterogeneous contributions of genetic variants in patients with neurodegenerative diseases.
Before starting his PhD, Chris worked as a laboratory specialist at the University of Utah College of Pharmacology and Toxicology. His work centered around genetic engineering using CRISPR-based animal models. He contributed to the development of a reverse-genetic screening platform, MIC-Drop-Seq, for scalable mapping of gene functions during vertebrate development at cellular resolution. He also contributed to the development and validation of deep-learning-based zebrafish behavior profiling tools, such as ZeChat and Fishbook. He developed an app of statistical tools, called Fintelligence, for high-throughput behavioral analysis to discover drug candidates for cardiovascular, metabolic, and nervous system disorders in zebrafish experimental models; also elucidating the disease mechanisms that drive those disorders.
In addition to method development for rare disease research and diagnosis, his interests in future research include creating “virtual cell” or “digital twin” models that can be used to understand disease etiology and diagnose diseases on a personalized medicine basis.