Brian Groenke
brian.groenke@pik-potdsam.de
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About me
About me
I am a postdoctoral researcher at the Potsdam Institute for Climate Impact Research in Potsdam, Germany. My primary research interests are in applications of probabilistic modeling, uncertainty quantification, and scientific machine learning to geophysical modeling of Earth systems.
In my PhD, I worked on probabilistic inverse modeling of subsurface heat transfer in terrestrial permafrost. Prior to that, I worked on the application generative deep learning to statistical downscaling of climate and weather variables from coarse scale model outputs.
My industry background consists primarily of software engineering, both front-end and back-end development, with a wide range of frameworks and languages.
When I'm not coding or thinking about data, I also enjoy traveling, playing guitar, drinking good beer, and speaking barely passable German.
Education
Education
Ph.D. Computer Science, Technical University of Berlin, Berlin, 2020 - 2025 (Date of defense: 22nd of May, 2025)
M.Sc. Computer Science, University of Colorado, Boulder, 2018 - 2020
B.Sc. Computer Science and Engineering, The Ohio State University, 2014 - 2018
Community involvement
Community involvement
Research groups
FutureLab "Artificial Intelligence in the Anthropocene" lead by Prof. Dr. Niklas Boers, Summer 2025 - present
Compound Event Impacts group lead by Prof. Dr. Jakob Zscheischler, Summer - Fall 2024 (temporary research stay)
Sensitivity of Permafrost in the Arctic (SPARC) group, lead by Prof. Dr. Julia Boike, Fall 2020 - Spring 2025
Machine learning research lab, lead by Prof. Claire Monteleoni, Fall 2018 - Spring 2020
Peer review:
ICLR 2019 - 2022
NeurIPS 2021
Past projects
Past projects
Quantifying and constraining uncertainty in modeling permafrost under a warming climate, Fall 2020 - Spring 2025
PhD thesis research project supervised by Prof. Dr. Julia Boike and Prof. Dr. Guillermo Gallego.
Studied the problem of uncertainty quantification and inverse modeling of physical processes affecting permafrost.
Published two papers on solving inverse problems related to permafrost modeling:
"Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer". 2023. The Cryosphere.
"Robust reconstruction of historical climate change from permafrost boreholes". 2024. JGR: Earth Surface.
Developed modern computational frameworks for process-based modeling of heat and water flow in permafrost soils (CryoGrid.jl), as well as Bayesian inference for numerical simulators (SimulationBasedInference.jl).
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows, Summer 2019 - Fall 2020
M.Sc. thesis research project supervised by Prof. Claire Monteleoni (Accepted on May 4th, 2020)
Developed novel, unsupervised and generative method for statistical downscaling of climate data
Representation learning for coupled climate models, Spring 2019
Investigated the application of various dimensionality reduction algorithms and unsupervised learning methods to data from CMIP5
Survey of machine learning methods for climate informatics datasets, Fall 2018
Developed and used techniques for evaluating a variety of dimensionality reduction algorithms on climate related RAMPs [1] as part of collaborative course project
AutoMoL: Automated Monadic Logic, 2017 - 2018
Undergraduate student research project under Dr. Neil Tennant, OSU Dept of Philosophy
Implemented automated theorem prover and visualization software for the study of proofs in Core Logic (Tennant 2017)
Experimented with applying reinforcement learning to automated proof search
Industry Experience
Industry Experience
Data Science Intern at Jupiter Intelligence Inc, Boulder, CO, Summer 2019
Researched and evaluated the application of deep neural networks for image super resolution to statistical downscaling of low resolution climate model output
Software Development Engineer I at Lake Shore Cryotronics, Westerville, OH, Aug 2017 - May 2019
Developed user facing software for Lake Shore’s new M41/M71, M91 FastHall™ instruments as part of core product development team
Researched applications of machine learning in sensor manufacturing
Software Engineer Co-op at Lutron Electronics, Coopersburg, PA, Summer 2017
Implemented new embedded device firmware update system for Lutron VIVE devices
Created a cross-platform mobile app frontend alongside embedded server backend
Worked directly on-site with customers on upgrading device firmware
Software Engineer Intern at Lake Shore Cryotronics, Westerville, OH, Jan 2016 - Apr 2017
Designed and implemented software/firmware update framework on Android and embedded systems
Designed and developed user interfaces for Lake Shore’s Model 155 Voltage/Current Source
Software Validation Engineer Intern at Intel Corporation, Chicago, IL, Summer 2015
Implemented system for running and maintaining unit tests in Java codebase for Intel’s Express Service Gateway
Implemented framework for gathering and publishing code coverage data from unit/integration testing
Teaching
Teaching
Teaching Assistant, CSCI 3104, Spring 2020
Graduate Student Staff, CSCI 5622, Fall 2019
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