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Olivia A. Greene, PhD 

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Astrophysicist
CFO & Dir of SciComm for The Neuroverse Initiative
Pipeline Developer
Program + Workshop Developer
STEAM and ND Advocate

Dr. Olivia A. Greene is an astrophysicist and advocate for neurodiversity in STEM. She received her PhD in Astrophysics from Vanderbilt University, where she specialized in galaxy formation and evolution. Her dissertation created a complete, visually classified catalog of post-starburst E+A galaxies from the SDSS-IV MaNGA Survey through an innovative citizen science approach. Olivia is also the Chief Financial Officer and Director of Science Communications for The Neuroverse Initiative, a 501(c)(3) organization working at the intersection of space science and neurodiversity. She holds a Master's degree from Fisk University through the Fisk-Vanderbilt Bridge Program and a Bachelor's of Science degree in Physics from The Harriett L. Wilkes Honors College at Florida Atlantic University.

Publications

First and contributing author papers. 
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Seeing What Is, What Was, What Could Be, What Must Not:
Refining, Cataloging, and Investigating A Complete, Spatially Resolved Spectrophotometric Sample of Nearby Post-Starburst E+A Galaxies in SDSS-IV MaNGA.

Greene, 2026

Neurodivergent in Astronomy: The Early-Career Researcher Edition

Dungee, Greene & Schonhut-Stasik, 2025

Comparing Star Formation Histories and Evolutionary Pathways of Post-starburst and E+A Galaxies in TNG50 and SDSS-IV MaNGA


Ludwig et al., 2025

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Refining the E+A Galaxy: A Spatially Resolved Spectrophotometric Sample of Nearby Post-starburst Systems in SDSS-IV MaNGA (MPL-5)

Greene et al., 2021

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SDSS-IV MaNGA: A Dwarf E+A Galaxy Quenched by AGN Feedback

Marinelli et al., 2020

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SDSS-IV MaNGA: Evidence of the importance of AGN feedback in low-mass galaxies

Penny et al., 2018

Posters

Talks

Master's Defense Presentation, 2020

All Videos

All Videos

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Refining the E+A Galaxy: A Spatially Resolved Spectral Analysis & Synthesis of Nearby Post-Starburst Systems in SDSS-IV MaNGA (MPL-5)

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Behind the Curtain: Revealing the effect of sub-grid models on supermassive black hole merger rates for LISA

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