Michael Koch
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About
I am a computational scientist with a background in civil and geotechnical engineering and extensive experience in developing numerical and statistical methods for subsurface imaging. My work integrates finite element modelling, PDE-constrained inversion, and Bayesian inference to solve complex problems in seepage flow, electrical resistivity, quasi-static electromagnetics, elastic wave propagation, and gravity modelling. Over the past decade, I have developed novel statistical inversion techniques based on Hamiltonian Monte Carlo and Subtractive Optimally Localized Averages (SOLA) approaches, with an explicit focus on robust interface delineation and efficient uncertainty quantification in subsurface characterisation across geophysical, geotechnical, and agricultural applications.
Academic roles
2024 - present: Research Fellow, RSES, ANU, Australia
2022-2024: Assistant Professor, Graduate School of Agriculture, Kyoto University, Japan
2020-2021: Postdoctoral researcher, Graduate School of Agriculture, Kyoto University, Japan
Education
2016-2020: PhD in Agriculture Sciences, Kyoto University, Japan
2013-2015: M.Tech in Geotechnical Engineering, Indian Institute of Technology Guwahati, India
2009-2013: B.Tech in Civil Engineering, National Institute of Technology Silchar, India
Affiliations
Research interests
My current research at the Research School of Earth Sciences, Australian National University, focuses on developing scalable computational frameworks for gravity inversion using data from airborne quantum gravity sensors. I am exploring new mathematical formulations and dimensionality-reduction strategies for large-scale PDE-constrained inverse problems, aimed at improving the robustness, efficiency, and interpretability of geophysical imaging. More broadly, my work seeks to bridge applied mathematics, high-performance computing, and engineering to advance the next generation of inversion algorithms for Earth and infrastructure systems.