Aramco - Houston Research Center
Weichang Li is with Aramco Americas’ Houston Research Center where he joined in January 2015. He currently leads the Machine Learning group.
Weichang obtained his PhD degree in Electrical and Oceanographic Engineering in 2006, and MS (dual) in EECS and OE in 2002, all from MIT. From 2006-2008 he was with Woods Hole Oceanographic Institution (WHOI) working on broadband acoustic communications under an ONR postdoctoral fellowship. From 2008-2015, he was with ExxonMobil Corporate Strategic Research Lab where he led the machine learning group from 2011-2014.
His current research focus is on machine learning, statistical signal processing algorithm research and applications in geophysics, geosciences, and petroleum engineering problems.
As we witness a surge of interest in exploring the potential of machine learning in scientific and engineering applications where there are often well established first principle models, curious questions regarding the interplay between machine learning and physics models become important both at conceptual level and for practical purposes, such as what are the connections and differences, the interactions of machine learning and physics in a collated framework, and the comparative advantage and limitations of each method. In this talk I will present several electromagnetic (EM) inversion cases where conventional inversion, machine learning based regression, and machine learning with physics constraints are applied and compared. In the case of machine learning with physics constraints, I will show how the prior and posterior density can be effectively shaped by alternating between minimizing the data and parameter space losses, respectively, to improve inversion resolution and accuracy.