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Dr. Renato Polimanti is Associate Professor of Psychiatry and of Biomedical Informatics and Data Science at the Yale School of Medicine and Associate Professor of Chronic Disease Epidemiology at the Yale School of Public Health. He is also Associate Director for Scientific and Technical Operations of the Clinical Epidemiology Research Center at the VA Connecticut Healthcare System. Dr. Polimanti’s primary research interest is to apply big data analytics to molecular, clinical, and imaging data to understand the biology and the epidemiology of human traits and disease and to develop tools to improve the healthcare of diverse population groups. His ongoing studies are supported by the National Institutes of Health and the Department of Veterans Affairs. Dr. Polimanti contributed to more than 200 peer-reviewed publications including 42 as first author, 70 as senior author, and two as sole author. His group is also involved in large collaborative projects, including the Psychiatric Genomics Consortium and the Million Veteran Program.

Clinician-scientist, broadly interested in developing approaches for biologically informed diagnosis, subtyping, prognostication, and treatment selection for the mental health field. Human subjects genetic epidemiology, genome-wide expression and association studies. Epidemiological studies of using electronic health records to characterize treatment practices and emulate clinical trials. Application of machine-learning and language processing tools to electronic health records in order to yield phenotypic data for research ResearchGate Profile

Dr. Cabrera-Mendoza joined Dr. Renato Polimanti's group in 2021. She completed her MD and PhD in the National Autonomous University of Mexico as part of the Plan of Combined Studies in Medicine (PECEM by its acronym in Spanish). Her current research focus on genomic changes associated with suicidal behavior and substance use disorders.

I completed my medical training at Aristotle University of Thessaloniki in Greece at the height of the refugee crisis in the Mediterranean. Fascinated by transcultural psychiatry, I spent two years as a resident and rural doctor serving on the Greek island of Leros and the island's refugee reception center. The clinical practice sparked my interest in how healthcare information can be employed to refine psychiatric phenotypes, so I pursued a Master’s in Health Data Analysis at the Aristotle University of Thessaloniki, where I delved into psychiatric genetics. During my post-doctoral fellowship at Yale, I used electronic health records, wearable device data, and multi-omic information to understand the underlying causes of anxiety disorders in diverse populations and to explore the shared and distinct genetic factors across various psychiatric conditions. As a resident in the Yale Neuroscience Research Track, I will focus on using multidimensional patient-related information to improve clinical prediction, emphasizing enhancing representation in healthcare data.

Dr. Jun He joined Dr. Renato Polimanti’s lab in the Yale Psychiatry Division of Human Genetics as a postdoctoral associate in late 2022. His research focuses on the etiology and prevention of major chronic non-communicable diseases using genetic, epidemiological and machine learning approaches. Specifically, he applies genetically informed causal inference analyses to understand the relationship between different brain-related outcomes, including psychiatric disorders, Alzheimer's disease, hearing difficulty, and brain imaging phenotypes. Dr. He is also a research associate of CSP #2006/MVP029: Genomics of Gulf War Illness (GWI) in the Million Veteran Program (MVP), where he investigates genetic and environmental factors that influence the onset of GWI. In 2024, he received a Postdoctoral Research Fellowship Innovation Grant from the American Foundation for Suicide Prevention (AFSP) to fund his research on genetic mechanisms and risk prediction of suicide. Dr. Jun He received his BS, MPH and PhD degrees from Central South University Xiangya School of Public Health. In his PhD, he investigated the effects of socioeconomic status, behavioral factors, and major historical events on the development of chronic diseases, and established a high-performing predictive model for metabolic syndrome using a next-generation machine learning framework.