Dr. Nan Xu is an Assistant Professor in the ​Fischell Department of Bioengineering at the University of Maryland, College Park. She is also affiliated with the Department of Electrical & Computer Engineering and the Neuroscience & Cognitive Science Program. Nan directs Imaging- and Neuro-computations for Precision Informatics Research (INSPIRE) Lab. With a strong foundation in computational science, her research spans statistical and machine learning, applied mathematics, neuroscience, and various biomedical applications. She primarily focuses on developing advanced computational models and analyses of functional neuroimaging data to uncover novel insights into brain functions, diseases, informatics, and beyond. 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 visiting 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.
Personal Website
INSPIRE Lab

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 resides at the intersection of data science and neuroscience. We are committed to developing advanced models and innovative data science methodologies to elucidate brain function, neurological disorders, and other biological processes. By leveraging multimodal functional neuroimaging data—including fMRI-BOLD, LFP, optical imaging, and MEG—from animal models, healthy individuals, and patients, we decode complex brain activities and diseases. This integrative approach aims to provide groundbreaking insights that advance both fundamental understanding and translational applications in brain science, informatics, and beyond.
Research Methods
Computational Modeling
Statistical Learning
Scientific Computing
Dynamical systems analysis
Neuroimaging
Research Interests
Computational Neuroscience
Machine Learning
Functional Brain Dynamics
Nan
Email
nanxu [at] umd.edu