Jim Xiang is a PhD candidate and researcher in structural engineering with 11 years of experience specializing in building and bridge modeling, seismic design, ground motion analysis, and engineering optimization. Based at UC Irvine and living in Irvine, California, he combines statistical and machine learning techniques—RNNs, Bayesian methods, PSO and CMA-ES—with domain expertise to develop novel approaches for seismic hazard characterization and energy dissipation modeling. His work includes practical validations of Caltrans bridge models, system-identification of building modal properties across 80 California buildings, and a RNN-based method to generate correlated ground motion spectra. Jim often blends theory with applied data-driven workflows, for example using ARIMA to simulate seismic sequences and EM algorithms within neural frameworks, reflecting an uncommon fusion of structural mechanics and probabilistic machine learning.
Contributions:2 pushes, 1 branch in 1 year 3 months
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