Simon Topp
Machine Learning Engineer at Upstream Tech
Carrboro, North Carolina, United States
Summary
👤
Senior🎓
Top SchoolSimon Topp is a machine learning engineer and PhD scientist specializing in remote sensing and hydrology, with eight years of experience turning research-grade models into operational tools for water resources forecasting. He has led small interdisciplinary teams—most recently as a USGS Mendenhall Fellow—developing explainable deep learning, reproducible Python/R workflows with containerization, and high-throughput model deployments. At Upstream Tech he focuses on productionizing process-guided deep learning to inform sustainable water management, bridging field ecology, GIS, and scalable ML systems. His background in wildlife biology and extensive fieldwork gives him practical intuition for environmental data collection and the messy realities behind model inputs. A proponent of healthy, inclusive workplaces, he combines technical rigor with mentorship and reproducibility-first engineering.
8 years of coding experience
3 years of employment as a software developer
BA, Environmental and Urban Studies, BA, Environmental and Urban Studies at Bard CollegeMaster of Science (MS), Environmental Policy, Master of Science (MS), Environmental Policy at Bard Center For Environmental PolicyDoctor of Philosophy - PhD, Remote Sensing and Hydrology, Doctor of Philosophy - PhD, Remote Sensing and Hydrology at University of North Carolina at Chapel Hill
Spanish
Github Skills (62)
environmental9
water-quality8
unc8
water8
exploratory7
google-earth-engine7
earth-engine7
satellite6
react6
catalog6
r-package6
temperature5
modeling5
surface5
pipeline5
Programming languages (5)
Github contributions (5)
SimonTopp/riv_dl_drb_generalizability
Jun 2021 - Mar 2023
This is an adaptation of the GraphWaveNet model for the Delaware River Basin.
Contributions:2 PRs, 75 pushes, 5 branches in 1 year 9 months
delawareriverbasinadaptation
SimonTopp/river-dl
May 2021 - Dec 2022
Deep learning model for predicting environmental variables on river systems
Contributions:86 pushes, 24 branches in 1 year 7 months
riverenvironmentaldeep-learningmachine-learningvariables
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