Nicholas Kern - NASA Hubble Fellow at Massachusetts Institute of Technology

Nicholas Kern

NASA Hubble Fellow at Massachusetts Institute of Technology

Ann Arbor, Michigan, United States

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Nicholas Kern is a NASA Hubble Fellow and data scientist with 11 years of experience applying machine learning, signal processing, and Bayesian inference to messy, high-dimensional astrophysical data. He builds production-ready, well-tested ML systems—often in PyTorch—that accelerate inference on noisy images, waveforms, and time series, and has led teams to deploy pipelines that improved state-of-the-art results by an order of magnitude. His work spans nonlinear optimization, MCMC, and uncertainty-aware differentiable forward models for radio cosmology, and he’s contributed performance and statistical methods to major open-source projects like emcee and astropy. Comfortable bridging research and engineering, he mentors PhD students, manages reproducible data pipelines, and routinely translates complex results for technical and non-technical audiences. A practical innovator, he has turned theoretical emulators into tools that cut inference runtimes by thousands-fold on real-world problems.

11 years of coding experience

5 years of employment as a software developer

Doctor of Philosophy - PhD, Astrophysics, Doctor of Philosophy - PhD, Astrophysics at University of California, Berkeley

Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at University of Michigan

Stackoverflow

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Github Skills (8)

statistics10

astropy10

mcmc10

python10

astrophysics10

astronomy10

numpy10

testing9

Programming languages (3)

CJupyter NotebookPython

Github contributions (5)

astropy/astropy

Feb 2017 - Apr 2017

Astronomy and astrophysics core library

Role in this project:

Data Scientist

Contributions:26 commits, 2 PRs, 12 comments in 2 months

Contributions summary:Nicholas primarily contributed to the `astropy/astropy` repository by implementing and refining the `biweight_midcovariance` method within the `astropy.stats` module. Their work included adding the function, optimizing its performance using NumPy, fixing doctests, addressing PEP8 issues, adding tests, and refining documentation examples. The user's contributions focused on enhancing the library's statistical analysis capabilities.

astrologypythonscienceastrophysicsastrodynamics

dfm/emcee

Dec 2016 - Dec 2016

The Python ensemble sampling toolkit for affine-invariant MCMC

Role in this project:

Data Scientist

Contributions:7 commits, 1 PR, 2 comments in 8 days

Contributions summary:Nicholas made several modifications to the `emcee/ensemble.py` file, suggesting a focus on the core functionality of the ensemble sampler. These changes appear to revolve around optimizing how the lnprobfn is called, possibly to enable vectorized calculations. They introduced a `vectorize` flag and adjusted the conditional logic to accommodate vectorized lnprob calls. These contributions directly impact the performance and efficiency of the MCMC sampling process.

mcmc-samplerpythonprobabilistic-data-analysisensembleinvariant

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