Faculty Directory

Dr. Nan Xu is an Assistant Professor in the ​Fischell Department of Bioengineering at the University of Maryland, College Park, with a secondary affliate appointments in the Department of Electrical & Computer Engineering and the Edward and Jennifer St. John Center for Translational Engineering and Medicine, and graduate program affliations in the Neuroscience & Cognitive Science (NACS), Biophysics, and Applied Mathematics & Statistics, and Scientific Computation (AMSC). She leads the Imaging- and Neuro-computations for Precision Informatics Research (INSPIRE) Lab. Dr. Xu’s research integrates expertise in computational science, statistical learning, applied mathematics, neuroscience, and biomedical applications. Her current work focuses on developing advanced computational models and analyses of functional neuroimaging data to gain new insights into brain function, neurological diseases, and broader informatics applications.

Nan earned a B.S. in Electrical and Computer Engineering and a B.A. in Mathematics 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
• Machine Learning and Data Analytics
• Functional Brain Dynamics

Research Methods:

• Computational Modeling
• Statistical & Machine Learning Techniques
• Scientific Computing
• Dynamical Systems and Time Series Analysis

• Multimodal Functional Neuroimaging