Senior Computational Geoscientist at Zanskar Geothermal & Minerals (Salt Lake City).
Bayesian uncertainty quantification, probabilistic geothermal modeling, drilling decisions under uncertainty.
Computational geoscientist building Bayesian frameworks for geothermal resource assessment. I architect simulation and ML pipelines that fuse finite-element thermal models with geophysics and well data to prioritize drilling targets and quantify subsurface uncertainty.
PhD from Ghent University (2023) on Bayesian Evidential Learning for experimental design. Previously postdoc at Lawrence Berkeley National Laboratory on watershed-function machine learning.
Geothermal exploration and resource assessment at Zanskar — multi-objective drilling target ranking, history-matching 3D thermal models to well logs, acquisition-function-based prioritization under uncertainty.
- Wang et al. 2025, Water Resources Research — Snowmelt and subsurface heterogeneity in mountain hydrology
- Chen et al. 2026, Geophysical Research Letters — ModEx framework for watershed subsurface investigation
- Zhang et al. 2025, Applied Energy — THM modeling of deep mine geothermal systems
- Thibaut et al. 2022, Water Resources Research — Comparing well and geophysical data for temperature monitoring
- Thibaut et al. 2021, Journal of Hydrology — BEL framework for experimental design — repo: skbel
- Thibaut et al. 2021, Journal of Applied Geophysics — MGS inversion for resistivity/IP data — repo: MGS-public
- skbel — Bayesian Evidential Learning library
- pysgems — Python interface for SGeMS geostatistics
- project_template — Python scientific project template