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@higgins4286
higgins4286
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Lauren Higgins higgins4286

Machine Learning | Deep Learning | Data Visualization | Large Data Sets | Python | Javascript | LaTex | Physics | Astronomy | Empathy | Teamwork
  • University of Missouri - Kansas City
  • Kansas City, Missouri

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higgins4286/README.md

Welcome to my github! I am so glad you found me! 😁

👀 What you'll find here

Contained in my github are all of my coding projects and the raw .tex files for technical reports and publications that I have authored. I code primarilyin Python, but I have experience with Java, MatLab, SQL, R, and HTML.

👩🏼 About me

Pronouns: She/Her/Hers
Email:lhiggins86@gmail.com

  • Talented data scientist with a background in machine learning, data analytics, data visualization, and physics.
  • High Python Skills: ScikitLearn, Pytorch, TensorFlow, Keras, Cuda, Numpy, SciPy, MatplotLib, Pandas.
  • Track record of expeditious data analysis, writing meaningful reports, and skillfully presenting findings.
  • Able to work collaboratively as a part of a team to reach research goals and develop new processes.
  • Reliable, Adaptable, Creative, Attention to Detail, Organized, Empathic and Compassionate Professional.

    • 🔗 My Links

    Linkedin LinkedIn
    A Bridge to The Stars

    🥅 My Goals

    I am looking for a full-time position working on data analytics and/or machine learning.

    🎯 My Accomplishments

    🏅 Awards and Scholarships

    Click to see
    • Universities Space Research Association Undergraduate Award, Honorable Mention
    • Undergraduate Research Fellow Distinction - University of Missouri - Kansas City
    • NASA Missouri Space Grant Scholarship
    • Summa Cum Laude - University of Missouri - Kansas City

    📄 Posters and Publications

    Click to see
    • “21 cm Cosmology and the Near Infrared Background,” Lauren Higgins, Anna Schauer, Volker Bromm, Jan. 6, 2020, 235th Meeting of the American Astronomical Society, Honolulu, HI, abstract #108.14 poster.https://ui.adsabs.harvard.edu/abs/2020AAS...23510814H/abstract
    • “21 cm Cosmology and the Near Infrared Background,” Lauren Higgins, Anna Schauer, Volker Bromm, ASPCS, 2020 Compendium of Undergraduate Research in Astronomy and Space Physics.http://www.aspbooks.org/a/volumes/article_details/?paper_id=39446
    • “Spectral Energy Distribution Analysis of WISE-Selected Obscured AGNs in Major Mergers from the SDSS,” Madalyn E. Weston, Daniel H. McIntosh, Kameswara Mantha, Lauren Higgins, Nicholas Putnam, Jan. 8, 2019, 233rd Meeting of the American Astronomical Society, Seattle, WA, abstract #243.36 poster.https://ui.adsabs.harvard.edu/abs/2019AAS...23324336W/abstract
    • “Spectral Energy Distribution Analysis of WISE-Selected Obscured AGNs in Major Mergers from the SDSS,” Madalyn E. Weston, Daniel H. McIntosh, Lauren Higgins, Nicholas Putnam, Jul. 30 – Aug. 3, 2018, Are AGNs special?, Durham, UK poster #2/3N.
    • “A Bridge to the Stars Scholarship and Mentoring Program (FY2016)” Jennings, Derrick, Lauren Higgins, Rubyet Evans, Yana Yen. Missouri Space Grant Consortium 2017 Spring Meeting.
    • “How to Build an Effective High School-to-College STEM Bridge” Jennings, Derrick, Jaime Arnold, Rubyet Evan, Lauren Higgins, Osiris Hines. Missouri Space Grant Consortium 2018 Spring Meeting.
    • “Spectral Energy Distributions of WISE-Selected Obscured AGNs in the Local Universe” Weston, Madalyn E., Daniel H. McIntosh, Lauren Higgins, Nicholas Putnam, 2019 in prep.
    • “MergerPhot: A Multi-Wavelength Homomorphic Aperture Photometry Code for Merging Galaxies” Weston, Madalyn E., Daniel H. McIntosh, Kameswara Mantha, Lauren Higgins, 2019 in prep.

    🎙 Presentations

    Click to see
    • "Tree Rings in Dark Energy Survey Camera" Lauren Higgins. Aug. 14th, SLACK Intern Deliverables Presentation.
    • “Spectral Energy Distributions of WISE-Selected Obscured AGNs in the Local Universe” Lauren Higgins, Madalyn E. Weston, Daniel H. McIntosh, Nicholas Putnam. Apr. 27th, Missouri Space Grant Consortium 2019 Spring Meeting.
    • “Spectral Energy Distributions of WISE-Selected Obscured AGNs in the Local Universe” Lauren Higgins, Madalyn E. Weston, Daniel H. McIntosh, Nicholas Putnam. Apr. 13th, Mid- American Regional Astrophysics Conference 2019.
    • “Increasing Popularity and Success of A Bridge to the Stars, a High School to College Pipeline Program (FY2017)” Jaime Arnold, Rubyet Evan, Lauren Higgins, Osiris Hines. Apr 20th. Missouri Space Grant Consortium 2018 Spring Meeting.
    • “Bring into Focus STEM Identities for Urban High School Students: A Bridge to the Stars” Derrick Jennings, Rubyet Even, Lauren Higgins, Yana Yen. April 21st. Missouri Space Grant Consortium 2017 Spring Meeting.

    Popular repositoriesLoading

    1. Galaxy-Evolution-Data-and-Image-AnalysisGalaxy-Evolution-Data-and-Image-AnalysisPublic

      These are the projects I completed in my Astronomy research python course. I created detailed plots and images using raw data from the Hubble Space Telescope.

      Python 2 1

    2. NN-SiliconNN-SiliconPublic

      This project was to create a neural network that will predict the coefficients for the potential function of Silicon crystals.

      TeX

    3. First-Population-of-StarsFirst-Population-of-StarsPublic

      This repository contains the code I created to calculate the intensity of Near Infrared and X-ray light that should be observed from the first population of stars.

      TeX

    4. Machine-LearningMachine-LearningPublic

      This repository contains the four machine learning projects I created for my machine learning class. I used the Python package SciKit to write each algorithm.

      Python

    5. Deep-LearningDeep-LearningPublic

      This repository contains the deep learning projects I completed in my Deep Learning course at the University of Missouri-Kansas City.

      MATLAB

    6. Evolution-of-CANDELS-Massive-Spheroid-and-Disk-GalaxiesEvolution-of-CANDELS-Massive-Spheroid-and-Disk-GalaxiesPublic

      I was in charge of extracting relevant galaxy evolution data from 5 databases into one organized database where I the data was separated into our key target parameters.

      TeX

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