David Dempsey is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Canterbury's Faculty of Engineering, where he has been affiliated since December 2020. He leads the Subsurface Engineering Group, addressing critical challenges for a low-emissions future. His research integrates geomechanics, machine learning, and fluid dynamics across four key areas: Carbon Dioxide Removal : Designing systems to combust forestry waste and sequester CO₂ in geothermal reservoirs. Underground Hydrogen Storage : Modeling hydrogen injection/recovery in depleted gas fields. Volcanic Forecasting : Developing real-time ML systems for eruption prediction using seismic data. Induced Seismicity : Quantifying earthquake risks from energy projects. His recent publications (2021-2025) show a strong focus on machine learning applications in geohazards and energy storage, with themes including seismic forecasting, CO₂-hydrogen geostorage, and wildfire prediction. Numerical modeling and data-driven approaches dominate his methodology. He actively supervises 13+ graduate students on projects such as hydrogen geostorage, volcanic forecasting, and flood prediction. His group collaborates with industry on geothermal and seismic risk projects, leveraging real-time data from networks like GeoNet.
Christiane Jablonowski is a Professor in the Department of Atmospheric, Oceanic and Space Sciences (AOSS) at the University of Michigan's College of Engineering. She serves on the NCAR Community Earth System Model (CESM) Scientific Steering Committee, the AMS Committee on Artificial Intelligence Applications to Environmental Science, and represents U-M at the University Corporation for Atmospheric Research (UCAR). Her educational background includes: Ph.D. in Atmospheric & Space Sciences and Scientific Computing, University of Michigan M.S. in Meteorology, University of Bonn, Germany B.S. in Physics, Aachen University of Technology, Germany Professor Jablonowski specializes in atmospheric dynamics, focusing on baroclinic waves, tropical cyclones, and stratospheric phenomena. She pioneers idealized test cases for dynamical cores of General Circulation Models (GCMs) and leads the Dynamical Core Model Intercomparison Project (DCMIP). Her research integrates machine learning with high-resolution modeling for weather prediction and climate simulation, with significant work on Great Lakes coupling and volcanic eruption impacts. Her recent publications reveal strong trends toward machine learning applications in physical parameterizations and ultra-high-resolution modeling, with recurring themes in stratospheric dynamics, tropical cyclone simulation, and dynamical core evaluation across 15 recent articles spanning volcanic aerosol impacts, QBO modeling, and Great Lakes ice forecasting. Her scientific accolades include the Presidential Early Career Award for Scientists and Engineers (PECASE) and Department of Energy Early Career Award, alongside the 2023 UCAR Outstanding Accomplishment Award. Additional honors recognize her methodological innovations and graduate fellowship achievements. UCAR Outstanding Accomplishment Award in Publication (2023) AGU EOS publication highlights (2022) Presidential Early Career Award for Scientists and Engineers (PECASE) (2010) Department of Energy Early Career Award (2010) AOSS Faculty Award (2010) Distinguished Achievement Award, U-M College of Engineering NCAR Advanced Study Program Fellowship She directs major federally funded initiatives including the NOAA Unified Forecast System Short-Range-Weather team and CESM Atmospheric Model Working Group, mentoring numerous graduate students through DOE and NASA grants while leading the Dynamical Core Model Intercomparison Project. As founder of the Dynamical Core Model Intercomparison Project (DCMIP) and co-chair of NOAA's Unified Forecast System team, she coordinates international collaborations developing next-generation atmospheric modeling frameworks at the Climate and Space Research Building.
