Daniela Huppenkothen
Universitair Docent at University of Amsterdam
Utrecht, Utrecht, Netherlands
Summary
👤
Senior🎓
Top SchoolDaniela Huppenkothen is a Universitair Docent and data-driven astronomer with 11 years of experience building statistical and machine learning methods to extract physical insight from complex astronomical time series. Her work spans academia and research institutes—including positions at SRON, the DIRAC Institute, and NYU—where she developed Gaussian process, hierarchical Bayesian, and other advanced techniques for X-ray and gamma-ray variability. She is a contributing developer to the widely used stingray library for spectral timing, improving core functionality like power spectrum significance estimation and robust rebinning. Beyond methods and software, she leads efforts to mitigate observational systematics and to design interventions that foster welcoming online communities during participant-driven workshops and hackathons. This blend of rigorous methodology, open-source impact, and community-building underpins her pragmatic approach to turning large, noisy datasets into physical discovery.
11 years of coding experience
10 years of employment as a software developer
Bachelor of Science (BS), Geosciences and Astrophysics, Bachelor of Science (BS), Geosciences and Astrophysics at Jacobs University BremenMaster of Science, Astronomy and Astrophysics, Master of Science, Astronomy and Astrophysics at University of Amsterdam
German, English, French, Dutch
Github Skills (9)
fourier-transform10
time-series10
f10
python10
frequency-analysis10
numpy10
data-analysis10
astropy9
testing8
Programming languages (10)
Github contributions (5)
StingraySoftware/stingray
Sep 2015 - Feb 2022
Anything can happen in the next half hour (including spectral timing made easy)!
Role in this project:
Data Scientist
Data ScientistContributions:6 releases, 166 reviews, 343 commits in 6 years 5 months
Contributions summary:Daniela primarily contributed to the development and improvement of the `stingray` library for astronomical time series analysis. Their work focused on enhancing the functionality of the `Powerspectrum` class, including the implementation of methods for calculating and evaluating classical significances in power spectra. They also added tests for the rebinning function, `rebin_data_log`. These commits demonstrate an understanding of the underlying algorithms and practical application in data analysis.
x-rayblackholedata-analysistime-series-analysispython
dhuppenkothen/dhuppenkothen.github.io
Oct 2015 - Feb 2025
Contributions:1 PR, 27 pushes, 1 branch in 9 years 5 months
shiny
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