Each year, outstanding scientists are selected to serve as Berkowitz Postdoctoral Fellows. They lead collaborative research studies, publish landmark research, and help build bridges across countries and institutions:
Adi Berliner-Senderey, PhD
Adi Berliner-Senderey received her PhD from the Technion, where her research focused on the development and application of computational techniques to provide personalized interventions, with the primary objective of enhancing patient adherence and improving overall outcomes. Berliner's broader research pursuits are dedicated to advancing the diagnosis and treatment of human diseases by leveraging high-dimensional observational data and integrating methodologies from diverse disciplines, including medicine, computational modeling, behavioral science, and statistics. Berliner serves as Director of the AI and Behavioral Insights Unit at the Clalit Research Institute, where she leads pioneering projects at the forefront of medical informatics aiming to optimize patient care and promote adherence to medical guidelines. These projects entail the integration of machine learning algorithms and behavioral science with a particular emphasis on their expeditious implementation in real-world healthcare settings. Berliner leverages her extensive expertise in bioinformatics, clinical informatics, longitudinal observational causal inference methods and behavioral science to advance scientific understanding and implementation of evidence-based practices in healthcare.
Ofer Isakov, MD PhD
Ofer Isakov is a physician-scientist with a specialty in internal medicine. Ofer is a graduate of the MD/PhD program in Tel Aviv University. His doctoral dissertation focused on the development and application of bioinformatics tools to study human and microbial genomics. During his residency, Ofer led a bioinformatics lab at the Tel Aviv Medical Center, which focused on applying machine learning methods to study clinical questions. Ofer serves as the head of bioinformatics at the genomics center in Clalit. His responsibilities include conception, incorporation and validation of the methods, tools and infrastructure required for analysis of genomic data. Ofer’s research interests include rare genetic disease diagnosis, characterization of genotype-phenotype associations and development of novel genomic data science methods.
Ruth Johnson, PhD
Ruth Johnson received her PhD in Computer Science from the University of California, Los Angeles where she was advised by Professor Bogdan Pasaniuc and Professor Sriram Sankararaman. Her research focused on developing computational and statistical methods for understanding the genetic basis of complex diseases. She was actively involved in the UCLA ATLAS Precision Health Biobank, serving as the lead bioinformatics analyst and as the UCLA-lead for numerous multi-site collaborations such as the Covid-19 Host Genomics Initiative. A key component of Ruth’s research involved designing EHR-based risk score algorithms used to identify undiagnosed patients with rare autoimmune diseases, where the methodology has already been implemented at multiple clinical sites. Her current research is centered on EHR-based representation learning and rare genetic diseases. Prior to her PhD, Ruth completed her B.S in Mathematics at UCLA.
Matthew McDermott, PhD
Matthew McDermott received his PhD in Computer Science from MIT, working with Professor Peter Szolovits on clinical and biomedical representation learning; in particular injecting prior domain knowledge and leveraging unsupervised data to build clinically meaningful representations of medical and biological data modalities. His research has investigated topics ranging from semi-supervised biomedical regression problems leveraging partially labeled data; domain-specific pre-training methods for clinical language, intensive care unit numerical timeseries, and protein sequences; and novel frameworks for inducing inductive bias into pre-training algorithms. Prior to his PhD, Matthew studied mathematics at Harvey Mudd College for his undergraduate degree, and worked as a software engineer in data engineering at Google.
Noam Barda, MD PhD
Noam Barda received his MD from Tel-Aviv university. Barda's medical specialty is public health and epidemiology. He received his PhD from Ben-Gurion university, where his doctoral dissertation focused on computational methods to improve cardiovascular disease prediction models. He also has a degree in computer science (BSc). Barda is head of epidemiology and research at Clalit Research Institute, the research institute for the largest health fund in Israel. There and at Harvard Medical School's DBMI, his research focuses on the intersection of epidemiology, machine learning and biostatistics, often with projects that are meant for rapid implementation in clinical settings within the health fund. More specifically, his research interests include issues around medical predictive models and causal inference from observational data.
Noa Dagan, MD, PhD
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. Dagan is 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. 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.