With 30 years' experiences in applied geophysics and mathematics, both in academia and industry, my main purpose of working here as an adjunct associate professor is to explore all opportunities of utilizing academic resources to solve the practical problems met in industries. Currently, my major research interests include exploration geomechanics and geophysics, unconventional reservoirs (shale gas and shale oil), microseismic studies, induced seismicities / earthquake engineering, and Big Data analytics (machine learning with Hadoop, Spark, and the graph database Neo4j). In addition, I am also working on global seismology, with focuses on seismic source characterization and optimization in model building on the orders of Big Data.

Throughout my career so far, all those best engineers I have worked with say that the key to their successes is to keep things simple, which I totally agree. However, keeping things simple doesn't mean the data processing and analysis is simple. It means grasping the major factors to keep the problem handleable and avoiding the pedantic "perfectness". With the complexities and the huge amount of data we are facing today, even figuring out the major factors to play with may require pretty sophisticated analysis based on a good understanding of modern statistical theories and through a Big Data system. So, let's keep the things simple, and provide practical solutions to the problems met in industries and academic researches.