Dr. Nan Xu is an incoming Assistant Professor (Jan 2025) at the Fischell Department of Bioengineering. With a strong foundation as a computational scientist, her research spans statistical learning, applied mathematics, neuroscience, and various biomedical applications. Her primary efforts are centered on developing advanced computational models and analyses with functional neuroimaging data to uncover novel insights into brain functions and diseases. Nan completed her undergraduate studies at the University of Rochester, earning a B.S. in Electrical and Computer Engineering and a B.A. in Mathematics, with a minor in Music, in 2011. She then pursued graduate studies at Cornell University, where she received her M.Sc. (2015) and Ph.D. (2017) in Electrical and Computer Engineering, minoring in Applied Mathematics and Cognitive Neuroscience. Throughout her postdoctoral journey, Nan gained extensive interdisciplinary research experience, including a fellowship in Chemical and Biomolecular Engineering at Georgia Tech (2017-2018), a research scientist role at the McGovern Brain Institute at MIT (2022), and a postdoctoral fellowship in Biomedical Engineering at Georgia Tech and Emory University (2019-2024). Her research is currently supported by a BRAIN Initiative Diversity K99/R00 award.

Degrees

  • BS
    Electrical and Computer Engineering, University of Rochester
  • BA
    Mathematics, University of Rochester
  • MS
    Electrical and Computer Engineering, Cornell University
  • PhD
    Electrical and Computer Engineering, Cornell University

Our research lies in the intersection of data science and neuroscience. We are dedicated to understanding brain function and cognitive mechanisms by analyzing and modeling spatiotemporal dynamics in the brain. Employing multimodal functional neuroimaging data, including fMRI-BOLD, LFP, optical imaging, MEG and more, from a variety of sources such as animals, healthy humans, and patients, we aim to decode the complex processes that underlie brain functions and diseases. This approach enables us to provide novel insights into both basic and translational neuroscience.

Research Methods
Computational Modeling
Statistical Learning
Scientific Computing
Dynamical systems analysis
Neuroimaging
Research Interests
Computational Neuroscience
Machine Learning
Imaging
Functional Brain Dynamics
Photo of Dr. Xu
Email
im.nan.xu [at] gmail.com