Emily Fox is a Professor in the Department of Statistics and Department of Computer Science at Stanford University. She also serves as Chief Technical Advisor at insitro, where she was SVP of AI and Machine Learning from 2024-2025. Prior to Stanford, Emily was the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. From 2018-2021, Emily led the Health AI team at Apple, where she was a Distinguished Engineer.
Emily received an S.B. and Ph.D. from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). She has been awarded a Presidential Early Career Award for Scientists and Engineers (PECASE), Sloan Research Fellowship, ONR Young Investigator award, NSF CAREER award, Leonard J. Savage Thesis Award in Applied Methodology, and MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize.
Her research focuses on advancing machine learning methods for applications in health and biology. Particular interests are in health sensing and wearable technologies, multimodal biological data (microscopy, omics), and neuroimaging data. Methodologically, her work emphasizes sequence modeling, Bayesian approaches, and generative modeling.
Affiliations:
Institute of Computational & Mathematical Engineering (ICME)
Wu Tsai Neurosciences Institute, Wu Tsai Human Performance Alliance
Bio-X
Stanford Data Science
Center for Artificial Intelligence in Medicine & Imaging (AIMI)