Name: Dr. Aaron C. Bell (he/him)
Type: User
Company: @InsightEdgeJP
Bio: Data Scientist at Insight Edge, an in-house DX engineering center of Sumitomo Corporation. Alum of the 2018 NASA FDL Astrobio team, and U-Tokyo Astronomy.
Twitter: moichiaaron
Location: Toshima, Tokyo, Japan
Blog: https://www.linkedin.com/in/aaroncnbell/
Dr. Aaron C. Bell (he/him)'s Projects
Here I am developing a simple recipe for creating large mosaics from the individual tiles of the preliminary AKARI/IRC all-sky survey.
Astronomical Plotting Library in Python
Machine learning and Statistics toolkit for physicists and biologists.
On to a chi-squared-less fitting of dust properties
An approximate translation of circular aperture photomotery code, for HEALPix maps, written in IDL by Clive Dickinson
A LaTeX resume template, tailored for the recent graduate who aspires to be a Data Scientist/Engineer.
Open-ended excercise using public datasets
Github backup of my master's thesis
ExoGaia code
Throwing the COBE-DIRBE near to far infrared all-sky maps at `scikit-learn`'s PCA, ICA, and NMF codes. (To isolate Zodiacal light!)
An example respository, showing one possible way to transform the coordinate system of a HEALPix map (and apply the necessary rotation)
This code is for producing cut-outs from HEALPix maps with error propagation from their accompanying HEALPix noise maps. for
Python healpix maps tools
This is a temporary repository. The purpose is to test PSF smoothing methods of the AKARI Far Infrared Surveyor all-sky surveys.
Applying Kepler Mapper, etc. to all-sky maps
A complete daily plan for studying to become a machine learning engineer.
Enumerating biosynthetic pathways in metabolic networks
Takes X and Y data, with corresponding absolute noise vectors, and creates a distribution of noise-weighted Spearman or Person correlation test results.
Repository to publicly display Jupyter notebook slides
Bootstrapping the PyAtmos ATMOS simulation results form FDL 2018 Astrobio team 1's work
DustEM wrapper for Python
A simple NN implementation in python. Inspired by Coursera Machine Learning course's exercises
Sample notebooks that are published by IBM for IBM Data Science Experience.