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Hi, I'm Nick

I'm a NASA Hubble research fellow based at MIT at the Kavli Institute for Astrophysics. I build end-to-end machine learning systems for large-scale, real-world data (e.g. noisy, messy, unlabeled) at the intersection of image, time-series, and radio waveform datasets. I am particularly interested in leveraging advances in deep learning for building robust and interpretable forward models (see my current work on the BayesLIM project).

See below for some of my open-sourced software contributions, and see here for some highlights of my research.

Nicholas Kern's Projects

21cmsense icon 21cmsense

Code for calculating the sensitivity of 21cm experiments to the EoR power spectrum

astro_9 icon astro_9

Summer Course on Python Programming in Astronomy

astropy icon astropy

Repository for the Astropy core package

c4 icon c4

C4 directory on /n/Christoq1/nkern

capo icon capo

Calibration and Analysis of PAPER Observations

caustic icon caustic

Contains Directories, Files, Scripts etc. for Caustic Technique and Galaxy Cluster Analysis

cograd icon cograd

Conjugate Gradient Algorithms in Python

emcee icon emcee

The Python ensemble sampling toolkit for affine-invariant MCMC

osdcstacking icon osdcstacking

This is a replica of the "OSDCStacking" repository found on our OSDC servers

plot_ellipse icon plot_ellipse

An easy-to-use function for plotting ellipses with matplotlib

pycape icon pycape

Python toolbox for Cosmic Dawn Parameter Estimation

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

serpens_south icon serpens_south

Repository for my Serpens_South directory containing scripts and data files for the Serpens South project

simpleqe icon simpleqe

A simple framework for power spectrum quadratic estimators

stacking icon stacking

Repository for the Stacking Method and the Caustic Technique

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