Sebastian Khan
Data Scientist
Cardiff, Wales, United Kingdom
Summary
👤
Senior🎓
Top SchoolSebastian Khan is a data scientist at Starling Bank with a decade of experience bridging gravitational-wave astronomy and production data science. He holds a PhD in Astrophysics and spent several years as a research fellow and associate at Cardiff University and the Max Planck Institute, developing leading waveform models and Bayesian inference tools for LIGO/Virgo analyses. Sebastian is an expert in gravitational-wave data analysis, surrogate waveform modeling using neural networks, and statistical Bayesian methods, and he has contributed core functionality to the widely used pycbc open-source package. He combines deep domain knowledge of physics with practical machine-learning engineering to accelerate expensive scientific models into production-ready surrogates. Comfortable working in large international collaborations, he has a track record of turning ambitious research into usable software and reproducible inference pipelines. Based in Cardiff, he brings both research rigor and pragmatic engineering to complex, data-driven problems.
10 years of coding experience
3 years of employment as a software developer
Mphys, Astrophysics, 1st class Hon., Mphys, Astrophysics, 1st class Hon. at Cardiff University / Prifysgol Caerdydd
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (6)
pyc10
python10
astronomy10
signal-processing10
physics9
documentation8
Programming languages (8)
Github contributions (5)
gwastro/pycbc
Dec 2015 - Mar 2019
Core package to analyze gravitational-wave data, find signals, and study their parameters. This package was used in the first direct detection of gravitational waves (GW150914), and is used in the ongoing analysis of LIGO/Virgo data.
Role in this project:
Backend Developer
Backend DeveloperContributions:15 commits, 27 PRs, 5 pushes in 3 years 4 months
Contributions summary:Sebastian contributed to the core functionalities of the `pycbc` package. Their work included implementing support for new features, such as IMRPhenomD peak frequency, adding mode_array support to `get_td_waveform`, and fixing numerical relativity data entries within waveforms. They also made improvements to documentation, including updates to the inference examples. Finally, the user addressed minor bugs and made general improvements to the codebase.
pythongwastrosignal-processingneutron-starparameters
Cyberface/phenom
Jun 2016 - Nov 2021
Contributions:2 pushes, 2 branches in 5 years 6 months
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