People

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.

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.

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.

Pradeep Mutalik has been a medical scientist at the Yale University School of Medicine for over 35 years. Dr. Mutalik completed his MD in Neurophysiology at All-India Institute of Medical Sciences in New Delhi, India, and was a junior faculty member there until 1986. He then came to Yale as a post-doctoral student in pain research and did additional post-doctoral work at the Yale Center for Medical Research, with which he has been been associated since. From 1989-2007, Dr. Mutalik was a faculty member at the Yale Radiology Department, where he directed the transition to digital technology and performed imaging clinical trials. He published pioneering work in negation detection in electronic medical records in the early 2000's. Since 2011, Dr. Mutalik has been affiliated with the West Haven VA Medical Center, where he has directed the remote primary care education program (Scan Echo) and is currently involved in clinical data mining and virtual trial emulation at the Clinical Epidemiological Research Center using the VA's large electronic medical record (EMR). Dr. Mutalik's main current interest is in using focused natural language processing (NLP) for retrieving accurate clinically relevant data from the EMR, for which he has developed rapid validation and adjudication methods. Over his career, Dr. Mutalik has published research in neurophysiology, behavior, clinical informatics, expert systems, radiology and medical education. He has also been a science and mathematical puzzle columnist for the New York Times and Quanta magazine and written over 200 popular science articles.

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