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Urdu“We are excited to have Dr. Tatonetti join our growing department,” said Jason Moore, PhD, professor and chair of the Department of Computational Biomedicine. “As an innovator in biomedical data science, he will help our team develop new methods of analysis that will lead to improved health throughout our community and beyond.”
Tatonetti said he’s excited to work on incorporating biomedical data into medical practice.
“Cedars-Sinai has a vision and has invested in making these advanced computational techniques part of how we practice medicine,” he said. “That’s why I’m here. It makes me feel like everything is possible.”
Tatonetti joins Cedars-Sinai from Columbia University, where he served as associate professor of Biomedical Informatics, director of Clinical Informatics in the Institute for Genomic Medicine, co-director of Bioinformatics in the Department of Biomedical Informatics and chief officer for Cancer Data Science in the Herbert Irving Comprehensive Cancer Center.
Prior to his tenure at Columbia University, Tatonetti was a Department of Energy Office of Science Graduate Fellow in the Biomedical Informatics Training Program at Stanford University. He earned his doctorate in biomedical informatics from Stanford University.
“As associate director of Computational Oncology for Cedars-Sinai Cancer, Dr. Tatonetti will be a key player in our convergent science and precision medicine platforms such as the Molecular Twin project,” said Dan Theodorescu, MD, PhD, director of Cedars-Sinai Cancer and the PHASE ONE Distinguished Chair. “His expertise in the analysis of multimodal data will help us move many of our strategic initiatives forward, and his own research further and significantly cross-pollinates and synergizes with our continuing efforts to address cancer problems of the diverse community of patients we serve.”
Tatonetti’s main research focus is understanding adverse drug reactions that affect underrepresented and minority populations.
“Historically, many populations have been left out of the drug discovery process and that means that very little knowledge or information is available about how they’ll respond to drugs,” Tatonetti said.
Groups such as women, children and racial minorities may be underrepresented in clinical trials and, therefore, drug safety data. Tatonetti is working on machine learning and data science methodologies that may help increase investigators’ understanding of drug interactions in these groups.
“Exploring how to push those methods into healthcare is new and very exciting,” Tatonetti said. “I think what we’ve seen is that these models are much more capable than we ever expected. Figuring out ways to apply them and make an impact is what I’m most excited about now.”
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