Tatonetti trained in mathematics and molecular biology at Arizona State University before receiving his PhD in biomedical informatics in 2012 from Stanford University. His lab at Columbia is focused on expanding upon his previous work in detecting, explaining, and validating drug effects and drug interactions from large-scale observational data. Widely published in both clinical and bioinformatics journals, Tatonetti is passionate about the integration of hospital data (stored in electronic health records) and high-dimensional biological data (captured using next-generation sequencing, high-throughput screening, and other “omics” technologies). His lab develops algorithms, techniques, and methods for analyzing enormous and diverse data by designing rigorous computational and mathematical approaches that address the fundamental challenges of observational analysis: bias and confounding. Foremost, they integrate medical observations with systems and chemical biology models to not only explain clinical effects, but also to further our understanding of basic biology and human disease. Tatonetti has been featured by the New York Times, GenomeWeb, and Science Careers. His work has been picked up by the mainstream and scientific media and generated hundreds of news articles.