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 

Research groups

Sensitivity of Permafrost in the Arctic (SPARC) group, lead by Prof. Dr. Julia Boike, Fall 2020 - Spring 2025

Telegrafenberg ML club, 2020 - 2022

Machine learning research lab, lead by Prof. Claire Monteleoni, Fall 2018 - Spring 2020

Peer review:

• ICLR 2019 - 2022

• NeurIPS 2021

• Climate Informatics 2019

• ICML 2020

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 (CryoGrid.jl), as well as Bayesian inference for numerical simulators more generally (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

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 Assistant, CSCI 3104, Spring 2020

Graduate Student Staff, CSCI 5622, Fall 2019