Julian Quick
Visiting Researcher at DTU - Technical University of Denmark
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolJulian Quick is a researcher and engineer with 11 years’ experience building scalable optimization, uncertainty quantification, and machine learning systems for wind energy and autonomous fleet control. He has driven significant gains in validation coverage, simulation speed, and mission design efficiency—delivering a 72% rise in scenario coverage, a 95% reduction in mission design time for large fleets, and real-time surrogate forecasting for electricity prices. Julian blends hands-on software engineering (contributions to the python-windrose library enhancing statistical inputs and plotting flexibility) with cross-institutional systems integration, having led V&V pipelines that connect five independent simulation platforms. Based in Berkeley, he pairs a PhD-level research background with practical delivery, shipping RL frameworks and test-covered control systems while keeping sustainability—clean air, food, and water—as his north star. Outside work he cycles, paints, and cooks, reflecting a practical, creative approach to problem solving.
11 years of coding experience
10 years of employment as a software developer
B.S. degree Environmental Resources Engineering, B.S. degree Environmental Resources Engineering at Humboldt State UniversityDoctor of Philosophy - PhD Mechanical Engineering, Doctor of Philosophy - PhD Mechanical Engineering at University of Colorado Boulder
Github Skills (6)
statistics10
python10
statistic10
numpy10
matplotlib10
pandas4
Programming languages (14)
Github contributions (5)
python-windrose/windrose
Dec 2016 - Aug 2018
A Python Matplotlib, Numpy library to manage wind data, draw windrose (also known as a polar rose plot), draw probability density function and fit Weibull distribution
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 8 PRs, 7 comments in 1 year 7 months
Contributions summary:Julian contributed to the `windrose` library, focusing on adding statistical input features and improving the flexibility of the plotting functions. They implemented functionality to incorporate statistical data, allowing users to input data via weibull factors and mean values, enhancing the library's analytical capabilities. Additionally, the user made improvements related to sending options to pyplot's legend and provided PEP8 compliance, ensuring code readability and maintainability. They also added an example file with statistical data.
pythonweibull-distributionwindfitpolar
WISDEM/pyDAKOTA
Jun 2015 - Jun 2018
Contributions:94 commits, 106 pushes, 1 branch in 3 years
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