About
Siqi Wu is a quantitative researcher affiliated with the Department of Statistics at the University of California, Berkeley. He earned a B.S. from the University of Hong Kong in 2010 and completed his Ph.D. in Statistics at Berkeley in 2016 under the guidance of Professor Bin Yu.
- Education: B.S. (University of Hong Kong, 2010), Ph.D. (UC Berkeley, 2016)
- Current Role: Quantitative Researcher at Citadel Securities
His research focuses on machine learning and computational biology, particularly dictionary learning, spatial gene expression analysis, and nonnegative matrix factorization. His work bridges theoretical statistics with practical applications in high-dimensional data and biological systems. Key trends include methods for local identifiability in optimization and stability-driven approaches to gene network construction.
Key Contributions: Development of DataLab for data management, theoretical analysis of l1-minimization dictionary learning, and spatial gene expression modeling for Drosophila development.
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