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Armin Schwartzman
Professor

Armin Schwartzman’s research encompasses theoretical and practical aspects of statistical signal and image analysis in a variety of scientific applications. These include spatio-temporal and high-dimensional data analysis, geometric statistics and smooth Gaussian random fields, with applications in biomedicine, the environment, neuroscience, genetics and cosmology.

Armin Schwartzman received his bachelor’s and master’s degrees in electrical engineering from the Technion – Israel Institute of Technology and the California Institute of Technology; and his PhD in Statistics from Stanford University. He was an R&D engineer at Rockwell Semiconductor and Biosense Webster, and has held faculty positions in Biostatistics at Harvard University and Statistics at North Carolina State University.

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Benjamin Smarr
Assistant Professor

Benjamin Smarr is an assistant professor at the Halicioğlu Data Science Institute and the Department of Bioengineering at the University of California, San Diego. As an NIH fellow at UC Berkeley he developed techniques for extracting health and performance predictors from repeated, longitudinal physiological measurements. Historically his work has focused on neuroendocrine control and women’s health, including demonstrations of pregnancy detection and outcome prediction, neural control of ovulation, and the importance of circadian rhythms in healthy in utero development. Pursuing these and other projects he has won many awards from NSF, NIH, and private organizations, and has founded relationships with patient communities such as Quantified Self. With the COVID-19 pandemic, he became the technical lead on TemPredict, a global collaboration combining physiological data, symptom reports, and diagnostic testing, seeking to build data models capable of early-onset detection, severity prediction, and recovery monitoring.