Azizi A. Seixas, PhD

Dr. Azizi Seixas is an Assistant Professor in the Departments of Population Health and Psychiatry at NYU Langone. Dr. Seixas’ research within the Center for Healthful Behavior Change broadly focuses on three areas: multilevel determinants of sleep and cardiovascular disease disparities; long-term health consequences of cardiovascular disease (CVD) disparities; and,  developing adaptive, group-tailored, and personalized behavior modification interventions, with the use of machine learning analytical tools, to improve health and well-being. In 2020, Dr. Seixas was chosen by Cell Press as one of a hundred most inspiring Black scientists in America. 

Dr. Seixas’ previously funded National Institutes of Health’s National Institute of Neurological Disorders and Stroke (NIH/NINDS) Diversity Supplement Award to the parent project Center for Stroke Disparities Solutions (U54NS081765) investigates the impact neurocognitive and psychosocial impairments and sleep disturbance have on stroke disability among racial and ethnic minorities.

Dr. Seixas’ research further addresses sociocultural and environmental determinants of chronic diseases, such as cardiovascular and cerebrovascular disease and mental illness,  and behaviors that prevent access to adequate care in disparity communities, which are disproportionately burdened by adverse cardiovascular outcomes. More recently, his work identifies barriers hindering diagnosis and treatment of sleep disorders among racial and ethnic minorities and evaluates the efficacy of behavioral models aimed at improving adherence to sleep and CVD recommended therapies. He utilizes machine learning analytical tools and systems science approaches to answer these complex health questions and develops just-in-time (adaptive, group-tailored, and personalized) behavioral approaches to improve adherence to recommended sleep and cardiovascular disease treatment.

Dr. Seixas was recently awarded a National Heart, Lung, and Blood Institute (NHLBI) career award (K01HL135452-01) to investigate whether insufficient sleep and obesity, which are two to three times more prevalent among Black compared to white people, might explain a significant proportion of CVD risk disparity between Blacks and whites, through secondary data analysis of the Sleep Heart Health Study, a NHLBI-funded epidemiological study. Additionally, Dr. Seixas utilizes machine learning and simulation modeling such as Bayesian Network Modeling (graphical representations of Bayesian statistics) and Agent-Based Modeling (a graphical simulation tool) to simulate what combinations of sleep duration and body mass index are associated with the lowest CVD risk for Blacks, whites and all genders. These techniques are used to help forecast what combination of health behaviors will reduce CVD risk disparity over time. In addition to his work focusing on population health insights, he is also working on developing precise and personalized behavioral medicine solutions that uses artificial intelligence to optimize behavior change and adherence to pro-health behaviors.