Xu, Nan
Assistant Professor
Fischell Department of Bioengineering
Electrical and Computer Engineering
Electrical and Computer Engineering
Dr. Nan Xu is an incoming Assistant Professor (starting December 2024) in the Fischell Department of Bioengineering with affiliations in the Department of Electrical & Computer Engineering and the Neuroscience & Cognitive Science Program at the University of Maryland, College Park. Nan directs Imaging- and Neuro-computations for Precision Informatics Research (INSPIRE) Lab. 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 earned a B.S. in Electrical and Computer Engineering and a B.A. in Mathematics, with a minor in Music, from the University of Rochester in 2011. She completed her M.Sc. (2015) and Ph.D. (2017) in Electrical and Computer Engineering at Cornell University, with minors in both Applied Mathematics and Cognitive Neuroscience. Her interdisciplinary postdoctoral experience includes a fellowship in Chemical and Biomolecular Engineering at Georgia Tech (2017-2018), a visiting scientist position 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 the NIH BRAIN Initiative K99/R00 award.
Our research resides at the intersection of data science and neuroscience. We develop 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 Interests
- Computational Neuroimaging & Neuroscience
- Artificial Intelligence and Data Analytics
- Functional Brain Dynamics
Research Methods:
- Computational Modeling
- Deep/Statistical/Machine Learning
- Scientific Computing
- Dynamical Systems Analysis
- Neuroimaging
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