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    Maya Petersen

    Associate Professor of Biostatistics at UC Berkeley

    Maya Petersen is a distinguished Professor of Biostatistics, Epidemiology, and Computational Precision Health at the prestigious University of California, Berkeley. With a robust academic lineage and a wealth of experience, she has made notable contributions to the field of health sciences, focusing on the intricate interplay of machine learning and statistical methodologies. In her current role, Professor Petersen also serves as the Berkeley Director of the UC Berkeley-UCSF joint program in Computational Precision Health and is the co-Director of the UC Berkeley Center for Targeted Machine Learning and Causal Inference. Her leadership in these programs positions her at the forefront of groundbreaking research aimed at enhancing health outcomes through precision health strategies.

    Maya's methodological research primarily explores the intersection of machine learning, statistical inference, and causal inference. She applies her expertise to analyze complex observational and experimental data, driving innovations in individualized treatment strategies and adaptive study designs. Her ability to navigate the nuances of data analytics and predictive modeling has allowed her to develop insights that are critical in understanding health systems and improving public health interventions.

    On the applied research front, Professor Petersen's work is impactful and far-reaching, focusing on critical global health challenges including pandemics, HIV, and COVID-19. Her contributions during the COVID-19 pandemic, in particular, have been integral to informing public health policies and practices aimed at mitigating the impact of the virus. Through her research, she has engaged in multidisciplinary collaborations that underscore the importance of data-driven decision-making in global health issues.

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    Maya Petersen
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    Location

    San Francisco, California, United States