Feng Song

OverviewAI-generated summary

Feng Song's research has focused on climate variability and reconstruction, particularly concerning precipitation patterns and lake level fluctuations over extended historical periods. Song has investigated the spatial extent and causal factors of synchronous summer precipitation changes in the modern Mongolian Plateau and the East Asian mid-latitude monsoon region. Additionally, Song's work has included the reconstruction of level fluctuations in Qinghai Lake over the past 600 years. Song has also explored advancements in computational methods, including the development of clustering algorithms based on improved particle swarm optimization and hybrid algorithms combining Levenberg-Marquardt Bayesian Regularization with genetic algorithms, as well as improved BP algorithms for application purposes.

Song has secured federal funding from the National Science Foundation (NSF) for two collaborative research projects. One project, totaling $276,385, focuses on precipitation and streamflow reconstruction and analysis in the Amazon River Basin, with Song serving as a Co-Principal Investigator. The second NSF-funded project, for which Song is the Principal Investigator, received $198,130 and investigates climate variability and change in the Great Plains, spanning from medieval to modern periods. Song's scholarly output includes five publications, with an h-index of 3 and a total of 26 citations.

Metrics

• h-index: 3
• Publications: 5
• Citations: 26