Posts classified under: Faculty

Chiara Sabatti

Chiara grew up in Brescia, Italy and obtained a master’s degree in “Economics and Social Sciences” (DES) from the Bocconi University in Milan in 1993. She came to Stanford in 1994 to pursue a PhD in Statistics, and worked with Jun Liu on multiscale MCMC methods. Between 1998 and 2000, she was a post-doctoral scholar, working with Neil Risch in Stanford’s Department of Genetics, and she was dazzled by the power of statistical methods in the booming field of genetics. In 2000, she joined the faculty at UCLA in the newly established departments of Human Genetics and Statistics. She returned to Stanford in 2009, with appointments in Health Research and Policy and in Statistics.

Chiara was one of the founding members of the new Department of Biomedical Data Science, where she now serves as Associate Chair of Education and Training. Since 2010, Chiara has served as Faculty Director of the longstanding  Workshop in Biostatistics series, which provides a key educational opportunity for students and faculty alike. She is involved in the Stanford Data Science Initiative, and her work is partly supported by an NSF grant which encourages collaboration across many Data Science Hubs across the United States. She also serves as the Associate Director of the Undergraduate Major in Mathematical and Computational Science program, also known as Stanford’s Data Science Major. For the last two years, she has served as a faculty mentor in the summer Data Science for Social Good fellowship program. She is happiest when working through a hard problem with students and she never turns down the opportunity for a philosophical chat

Mirabela Rusu

Research focuses on developing analytic methods for biomedical data integration, with a particular interest in the spatial integration of radiology and pathology images. Such integrative methods may be applied to create comprehensive multi-scale representations of biomedical processes and pathological conditions, thus enabling their in-depth characterization. The radiology-pathology fusion allows the creation of rich spatial labels that can be used as input for advanced machine learning to predict the presence and aggressiveness of cancers.

Matthew Lungren

Deep Learning in medical imaging (diagnosis, prediction) and clinical imaging outcomes prediction, clinical decision support, imaging utilization and appropriateness, cohort feature engineering with structured and unstructured EMR data for modeling applications

Jonathan H. Chen

In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community’s latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously learning health system. My group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine that will deliver better care than what either can do alone.