About the PIs
Noa Dagan
Noa Dagan is a public health physician and researcher. She holds an MD and an MPH from the Hebrew University, and a Ph.D. in Computer Science from Ben-Gurion University. She completed her postdoctoral studies in the Department of Biomedical Informatics (DBMI), Harvard Medical School. Dr. Dagan is a lecturer at the department of Software and Information Systems Engineering in Ben-Gurion University.
Dr. Dagan is also the director of data and AI-driven medicine at the Clalit Research Institute – the research institute of Israel's largest healthcare organization, insuring and treating over 50% of the Israeli population. Her responsibilities include the development and implementation of digital healthcare solutions to promote preventive, proactive and personalized medicine. She leads the entire lifecycle of AI-driven interventions, from conception, through machine-learning modeling, to implementation in medical practice.
Dr. Dagan's research focuses on practical implementations of machine-learning algorithms using clinical data, with a specific interest in the prevention of cardiovascular events and osteoporotic fractures. Dr. Dagan is also active in research of ethical aspects of machine-learning models such as fairness.
Noam Barda
I am a public health physician, epidemiologist, biostatistician and computer scientist in unequal parts. On the medical side - I received my MD from Tel-Aviv University and did my residency in public health (epidemiology tract) in Clalit Health Services. On the non- medical side – I received my BSc in computer science from the Open University and my PhD from Ben-Gurion University, where my doctoral dissertation, co-advised by public health and computer science, focused on computational methods to improve models to predict cardiovascular disease. My post-doctorate was at the department of biomedical informatics at Harvard.
Currently I am a lecturer at the department of Software and Information Systems Engineering at BGU. My research lies on the intersection of epidemiology, machine learning and biostatistics – specifically around causal inference from electronic health record-based observational data and predictive modeling in healthcare.
