Roxana Daneshjou

I am interested in bridging new technologies such as genomics and machine learning with clinical medicine. I am also interested in the use of Twitter for scientific communication and medical education. Clinal focus: Dermatology.

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

James Zou

We develop a wide range of machine learning algorithms and are especially interested in extracting disease insights from population genomics and epigenomics. On the methodology side, we are investigating new approaches to adaptive data analysis, representation learning for bio-medical data, new probabilistic models that encourage diversity, and multi-view learning. Application topics include: whole-genome and exome sequence analysis, risk prediction, synthetic biology, chromatin dynamics and transcription regulation.

Research Areas: 

  • Machine/Statistical Learning
  • AI for Health
  • Computational Biology

Serena Yeung

Our group’s research develops artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. We have a primary focus on computer vision, and developing algorithms to perform automated interpretation and understanding of human-oriented visual data across a range of domains and scales: from human activity and behavior understanding, to human anatomy, and human cell biology. Current projects include computer vision for extracting insights and knowledge from visual data ranging from surgery and behavioral science videos, to cell imaging data.

esearch Areas: 

  • Machine/Statistical Learning
  • Deep Learning
  • Bioinformatics
  • Computer Vision
  • Medical Imaging
  • Computational Biology
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