Brian Groenke
brian.groenke@awi.de
About me
I am a Doctoral Researcher at the Alfred Wegener Institute Helmholtz Center for Polar and Marine Research in Potsdam, Germany. My research interests are centered around the application of machine learning in scientific data analysis. Currently, I am working on the integration of statistical estimators into the CryoGrid permafrost model through computational statistics and simulation-based inference. Before that, I worked on the application generative learning methods to generate fine scale realizations 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 travelling, playing guitar, drinking good beer, and speaking barely passable German.
Education
Ph.D. Computer Science, Technical University of Berlin, Berlin, 2020 - present (expected 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
Research groups
Sensitivity of Permafrost in the Arctic (SPARC) group, lead by Prof. Dr. Julia Boike, 2020 - present
Telegrafenberg ML club, 2020 - 2022
Machine learning research lab, lead by Prof. Claire Monteleoni, 2018 - 2020
Peer review:
ICLR 2019 - 2022
NeurIPS 2021
Teaching
Teaching Assistant, CSCI 3104, Spring 2020
Graduate Student Staff, CSCI 5622, Fall 2019
Past projects
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows, Fall 2019-Spring 2020
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
NuGRUV: Music generation using deep learning, Fall 2017
Modified existing architecture to improve quality and novelty of generated music tracks
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