Xiaofeng Meng

Xiaofeng Meng was a Moore/Sloan Data Science and WRF Innovation in Data Science Postdoctoral Fellow from 2015-2017. He is currently continuing his postdoctoral research at the University of Southern California. 

Biography

Department of Earth & Space Sciences

UW mentors 
John Vidale, Earth & Space Sciences
Jake VanderPlas, eScience Institute

Education history 
Ph.D. Candidate, Geophysics, Georgia Institute of Technology
B.Sc., Physics, Beijing Normal University, 2008.

Research goals 
Xiaofeng Meng’s research interests include the responses of fault systems to stress perturbations, the physics of earthquake nucleation and interaction, improved detection of microearthquakes and seismic hazard assessment. While at Georgia Tech, Meng helped develop a GPU-based matched filter technique that effectively utilizes waveforms of known earthquakes as templates to search for similar signals within continuous data.

For his research with the eScience Institute, he will develop an auto-correlation technique that is capable of detecting repetitive seismic signals (e.g., earthquakes, nonvolcanic and volcanic tremor) through multi-year-long waveforms recorded in Cascadia. The proposed work is not only vital for better understanding the fundamental physics of fault rupture and volcanic eruption, but also useful for seismic and volcanic hazard forecasting and mitigation.