Tiangang obtained his PhD at the University of Auckland in 2010. Before joining Monash in 2016, he was a Postdoc associate at Massachusetts Institute of Technology from 2012 to 2015 and also spent a year at the ExxonMobil Upstream Research Company (2015-2016) as a Senior Research Engineer.
With backgrounds in applied mathematics and engineering, Tiangang’s research interest lies in the algorithmic interface between computational mathematics and data science, with a specific focus on integrating data-driven learning and mathematical models for issuing credible model-based predictions and decisions. He develops scalable statistical inference tools for computational inverse problems and uncertainty quantification. He also develops multilevel methods and model reduction methods that enable the deployment of statistical learning algorithms in large-scale real-world applications such as subsurface flows and geophysics.