Tamar Krishnamurti, PhD,

Tamar Krishnamurti, PhD is an Assistant Professor of Medicine and Clinical and Translational Science at the University of Pittsburgh. She received a Bachelor of Science degree in Biological Anthropology and a Ph.D. in Behavioral Decision Research from Carnegie Mellon University. At the University of Pittsburgh, Dr. Krishnamurti leads the Behavioral Health arm of the Center for Research on Behavioral Health, Media, & Technology and is a co-founder of the FemTech Collaborative, housed within the Center for Women's Health Research and Innovation.

Tamar Krishnamurti, PhD is an Assistant Professor of Medicine and Clinical and Translational Science at the University of Pittsburgh. She received a Bachelor of Science degree in Biological Anthropology and a Ph.D. in Behavioral Decision Research from Carnegie Mellon University. At the University of Pittsburgh, Dr. Krishnamurti leads the Behavioral Health arm of the Center for Research on Behavioral Health, Media, & Technology and is a co-founder of the FemTech Collaborative, housed within the Center for Women's Health Research and Innovation.

Dr. Krishnamurti’s research interests include risk perception and communication, medical decision making, mHealth, and the design of decision support tools, and behavioral interventions. She uses (and develops) methods in the social and decision sciences to examine problems that meet at the intersection of health, risk, technology, and the environment. Dr. Krishnamurti is the PI or Co-I on multiple interdisciplinary foundation or university grants focused on the creation of digital decision tools and has been invited to participate in workshops or panels on patient-centered risk communication at the FDA, FTC, and NASEM. Her current research focuses primarily on risk perception and communication and the development of mobile health strategies to identify and intervene on pregnancy and postpartum-related risks.

Dr. Krishnamurti is also a co-founder of Naima Health, a company that develops health tools using behavioral decision science and machine learning to engage patients in the clinical care.