Guoqiang Yu

Guoqiang Yu is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a joint appointment at the Virginia Tech Research Center - Arlington. His research focuses on integrating machine learning, signal processing, and statistical methods to develop computational tools for analyzing multiplatform biomedical data. Key areas include neuroinformatics, bioinformatics, and systems biology, with applications in understanding human diseases through genomic, proteomic, and imaging data integration.

• Education:
• Ph.D. in Electrical Engineering, Virginia Tech (2011)
• Postdoctoral Fellowship at Stanford University (2012)
• M.S. Tsinghua University (2004)
• B.S. Shandong University (2001)

Research Interests: Machine learning methodologies for biomedical data analysis, pattern recognition in complex datasets, optimization algorithms for high-dimensional data, stochastic signal processing, and their applications in neurodegenerative diseases (e.g., ALS, Alzheimer's), glial cell biology, and precision medicine. His work emphasizes developing open-source tools like ABDS, CAM3.0, and SynQuant for data normalization, deconvolution, and quantitative imaging analysis.

Awards & Service:

• NSF Career Award (2018)
• Dean's Award for Excellence in Research (2022)
• Member of NIH BRAIN Initiative Consortium (2021–present)
• Associate Editor for BMC Bioinformatics (2017–present)

Labs & Teams: Leads the Yu Lab at Virginia Tech, collaborating with multidisciplinary teams in neuroscience, bioengineering, and computational biology. Active in NIH-funded consortia focused on brain data science and large-scale neuroimaging initiatives.