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<!DOCTYPE html>
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<title>Marina Dunn</title>
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<br>
<br>
<b style="font-size: 18px">Galaxy Morphology Classification Using Bayesian Neural Networks for
LSST (2021-2023)</b>
<br>
<p style="font-size: 15px"><b>
<a href="https://deepskieslab.com" target="_blank"
rel="noopener">Deep Skies Lab</a>; University of California, Riverside
<br>
<i style="font-size: 15px">Advisor:
<a href="https://faculty.ucr.edu/~mobasher/"
target="_blank" rel="noopener">Bahram Mobasher</a>;
Research Mentors:
<a href="https://aleksandraciprijanovic.wordpress.com"
target="_blank" rel="noopener">Aleksandra Ćiprijanović</a>, <a
href="http://briandnord.com/bio">Dr. Brian Nord</a></i>
</b></p>
<p style="font-size: 14px">
My graduate research delves into leveraging deep neural networks to
address the formidable challenges arising from the massive data
volumes anticipated from upcoming surveys such as the Vera C. Rubin
Observatory's
<a href="https://www.lsst.org" target="_blank" rel="noopener">Legacy Survey of Space and Time (LSST)</a>. Through the exploration of
Convolutional Neural Networks and
Bayesian Neural Networks, alongside transfer learning techniques, I
have been focused on classifying galaxy morphologies in various
simulated LSST image data releases. By replicating real-world
observational conditions, including varying levels of observational
noise, this project aims to illuminate the impact of noise on
classification accuracy. This investigation underscores the critical
importance of observational realism in simulated astronomical
imaging data when developing machine learning models. Developing
robust models capable of seamlessly transitioning between simulated
and real data will be crucial for the next generation of of
large-scale surveys.
</p>
<center>
<a href="https://github.com/marinadunn/thesis" target="_blank" rel="noopener">
<h3>Project GitHub</h3>
</a>
<iframe src="https://drive.google.com/file/d/10ozwANa6IDaKIbLr9tIPp5KQFaW5sqCc/preview" width="600" height="380" allowfullscreen="true"
title="Marina's 2023 UCR Grad Slam Presentation"
allow="accelerometer; fullscreen; gyroscope; picture-in-picture; speaker-selection; web-share;" referrerpolicy="no-referrer">
</iframe>
</center>
<br>
<br>
<b style="font-size: 18px">Detection and Segmentation of Ice Blocks in Europa's Chaos Terrain
Using Mask R-CNN (2023)</b>
<br>
<p style="font-size: 15px"><b>
Planetary Systems Laboratory, NASA Goddard Space Flight
Center/CRESST <br />
</b></p>
<p style="font-size: 14px">
I led research to develop a deep learning approach for detecting and
segmentin individual ice blocks within the intricate "chaos terrain"
on the surface of Jupiter's moon, Europa, employing algorithms like
Mask R-CNN. This research endeavor seeks to deepen our comprehension
of the geophysical properties and processes on Europa influencing
the formation of chaos terrain, while also offering crucial insights
to guide future mission planning within the solar system.
</p>
<center>
<a href="https://github.com/marinadunn/europa-chaos-ML" target="_blank" rel="noopener">
<h3>Project GitHub</h3>
</a>
<a href="https://ml4physicalsciences.github.io/2023/files/NeurIPS_ML4PS_2023_156.pdf"
target="_blank" rel="noopener">
<h3>2023 ML4PS NeurIPS Paper</h3>
</a>
<a href="https://youtu.be/faJoBonMXdw?feature=shared" target="_blank" rel="noopener">
<h3>2023 OPAG Conference Presentation Video</h3>
</a>
<img src="assets/images/research/NeurIPS_ML4PS23_Europa_Poster.png" width="460" height="auto"
alt="Project poster for the Machine Learning for Physical Sciences Workshop at the 37th (NeurIPS), presented on December 15, 2023." />
</center>
<br>
<br>
<b style="font-size: 18px">The Machine Learning (ML) Showroom (Fall 2022)</b>
<br>
<p style="font-size: 15px"><b>
Information, Data, & Analytics Services (IDAS), NASA Langley Research
Center
</b></p>
<p style="font-size: 14px">
I spearheaded a project aimed at bolstering NASA's agency-wide
digital transformation and addressing the escalating demand for data
science services. Through the development of multiple components,
including user-friendly, cloud-based coding notebooks showcasing
simplified machine learning models utilizing tools like
Scikit-learn, PyTorch, and TensorFlow, as well as an internal
Microsoft SharePoint site, I empowered NASA teams to explore,
experiment, and assess the utility of machine learning for their
research endeavors. This initiative streamlined access to resources
by providing a centralized, pre-configured, and pre-authenticated
platform, thereby minimizing complexity and fostering a more rapid
and widespread adoption of machine learning technologies across the
agency.
</p>
<br>
<br>
<b style="font-size: 18px">Earth Science Datasets in the Commercial Cloud (August 2021 - May
2022)</b>
<br>
<p style="font-size: 15px"><b>
<a href="https://www.earthdata.nasa.gov/eis" target="_blank" rel="noopener">Earth Information Systems (EIS)</a>, NASA Goddard Space Flight Center
</b></p>
<p style="font-size: 14px">
I conducted research aimed at optimizing the migration and storage
of NASA Earth Science data and models on commercial cloud platforms,
enhancing scientific analysis efficiency. Specifically, I focused on
integrating various wildfire-related datasets into the EIS Fire
Portal to support comprehensive understanding and analysis of fire
activity, aiding organizations dedicated to fire and air quality
forecasting. Our intern team successfully showcased the advantages
of the cloud-optimized Zarr file format and the impact of specific
chunking strategies on processing speed and memory usage during
common data access and analysis operations with large
multi-dimensional datasets.
<br>
<center>
<a href="https://essopenarchive.org/doi/full/10.1002/essoar.10511054.1"
target="_blank" rel="noopener">
<h3>Journal of Earth and Space Sciences Preprint</h3>
</a>
</center>
In addition, for the <a
href="https://www.jpl.nasa.gov/images/pia25144-shift-campaign-research-plane-flight-area-map"
target="_blank" rel="noopener">NASA Surface Biology and Geology
High-Frequency Time Series (SHIFT) Campaign</a>,
I developed a comprehensive data pipeline to convert raw
hyperspectral imaging data of vegetation into the cloud-optimized
Zarr format, facilitating efficient extraction, transformation,
visualization, and analysis within a Science Managed Cloud
Environment (SMCE). Additionally, I implemented storage of this data
in a SpatioTemporal Asset Catalog (STAC) compliant format,
streamlining future indexing, discovery, and analysis processes. I
crafted interactive notebooks showcasing seamless data access from
an AWS S3 bucket, along with demonstrations of common data
operations accompanied by visualizations.
</p>
<center>
<a href="https://github.com/marinadunn/SHIFT-STAC-backend"
target="_blank" rel="noopener">
<h3>SHIFT Backend GitHub</h3>
</a>
<a href="https://github.com/marinadunn/SHIFT-STAC-demo"
target="_blank" rel="noopener">
<h3>SHIFT Public Demo GitHub</h3>
</a>
<img src="assets/images/research/NASA_Spring_2022_Poster.jpg"
alt="NASA Spring 2022 SHIFT Campaign Poster" width="550px" height="auto">
</center>
<br>
<br>
<b style="font-size: 18px">Computing Scholar, Data Science Summer Institute (Summer 2022)</b>
<p style="font-size: 15px"><b>
<a href="https://data-science.llnl.gov/dssi/class/2022" target="_blank" rel="noopener">Lawrence Livermore National Laboratory</a>
</b></p>
<p style="font-size: 14px">
I researched and developed a visualization tool aimed at enhancing
ML model optimization, particularly tailored for the scalable
Gaussian Process hyper-parameter estimation method 'MuyGPs' for
predictive modeling of orbital debris. This initiative aims to
accurately bridge gaps in missing observations of orbital debris,
thereby enriching catalogs of known satellites. By providing
researchers with enhanced visibility into model structures and
influential parameters impacting performance, this tool promises to
pave the way for broader applications in future astronomical
surveys, including research areas such as galaxy blend
classification and weak lensing shear, as exemplified by its
potential integration into the Rubin Observatory Legacy Survey of
Space and Time (LSST).
</p>
<center>
<a href="https://www.youtube.com/embed/Ru11sTYCk98?si=UVTd0XYETL8iP7L7"
target="_blank" rel="noopener">
<h3>2022 LLNL DSI Summer Slam Talk Video</h3>
</a>
</center>
<p style="font-size: 14px">
In addition, as part of the 2022 DSSI Challenge Problem, I worked on
a team to develop a machine learning fusion model that utilizes
molecular descriptors and 3D atomic representations to assess drug
compounds targeting SARS-CoV-2. The project aimed to predict the
binding of proteins and ligands, thereby contributing to the urgent
global endeavor of swiftly screening potential drug candidates for
their efficacy in treating or preventing SARS-CoV-2 infections.
</p>
<center>
<a href="https://github.com/marinadunn/covid19-drug-screening-ML"
target="_blank" rel="noopener">
<h3>COVID-19 ML Project GitHub</h3>
</a>
</center>
<br>
<br>
<a href="http://uaastroclub.org/projects/radio-astronomy-project/"
target="_blank" rel="noopener"><b style="font-size: 18px">UA Astronomy
Club Radio Project (2015-2018)</b></a>
<p style="font-size: 15px"><b>
University of Arizona Astronomy Club
</b></p>
<br>
<p style="font-size: 14px">
As part of a research project facilitated by the UA Astronomy Club,
our team conducted observations using the Arizona Radio Observatory
12-Meter Telescope on Kitt Peak over multiple semesters. Our aim was
to observe 101 prestellar cores--dense, dark, starless gas
clouds--identified in the Bolocam Galactic Plane Survey that may be
good potential candidates for future star formation, with the goal
of enhancing our understanding of how frequently and rapidly stars
are being created in specific environments. We determined 6
prestellar cores actively gathering enough material for stellar
production. Employing a radiative transfer model, we calculated
material collection rates ranging from approximately 500 to 2000
M⊙/Myr, suggesting a substantial increase in core masses likely to
double within a free-fall time frame, implying imminent star
production. This research was subsequently published in the
Astrophysical Journal in 2018.
</p>
<center>
<a href="https://iopscience.iop.org/article/10.3847/1538-4357/aabfea"
target="_blank" rel="noopener">
<h3>2018 Astrophysical Journal Paper</h3>
</a>
<img src="assets/images/research/ARO_12M.jpg" width="400" height="auto"
alt="University of Arizona Steward Observatory undergraduate students involved in the radio astronomy project stand in front of the ARO Telescope." />
<img src="assets/images/research/ARO_group.jpg" width="400" height="auto"
alt="University of Arizona Steward Observatory undergraduate students involved in the radio astronomy project stand in front of the ARO building." />
</center>
<br>
<br>
<b style="font-size: 18px">Steward Observatory Radio Astronomy Laboratory (2016-2017)</b>
<br>
<p style="font-size: 15px"><b>
University of Arizona, Steward Observatory
</b></p>
<p style="font-size: 14px">
I collaborated on a large team of scientists and engineers from
various institutions on a weekly basis, wrote significant proposal
components, procured instrument estimates, and managed large budgets
for the
<a href="https://science.nasa.gov/mission/gusto/" target="_blank"
rel="noopener">NASA GUSTO mission</a> and the
<a href="https://www.lpl.arizona.edu/SIIOS/#:~:text=What%20is%20The%20SIIOS%20Project,of%20different%20planets%20and%20moons" target="_blank" rel="noopener">Seismometer
to Investigate Ice and Ocean Structure (SIIOS)</a>.
Additionally, I played a pivotal role in organizing and
facilitating a successful site visit for the NASA GUSTO mission,
securing $40 million in funding and advancing it to its next phase.
I also authored comprehensive proposals, and designed, constructed,
and tested antenna prototypes for inflatable balloon observatories
like the Terahertz Space Telescope.
</p>
<center>
<img src="assets/images/research/AAS_2017_Poster.jpg"
alt="2017 American Astronomical Society Poster" width="550px" height="auto">
</center>
<p style="font-size: 14px">
I also conducted research focused on the interstellar medium's
lifecycle, particularly investigating the impact of nearby star
formation turbulence on giant molecular cloud (GMC) evolution.
Utilizing data from the SuperCam instrument on the Submillimeter
Telescope on Mt. Graham, I analyzed observations of a specific
carbon monoxide molecular transition within the GMC R Coronae
Australis (R CrA). By calculating the gas temperature and column
density of the cloud, I was able to create integrated intensity maps
using Python and determine the energy balance within the cloud.
Through these analyses, I confirmed previous hypotheses indicating a
young star's outflows within R CrA, driving star formation on both
sides of the cloud.
</p>
<br>
<br>
<b style="font-size: 18px">UA Exoplanet Project (2014-2016)</b>
<br>
<p style="font-size: 15px"><b>
University of Arizona
</b></p>
<p style="font-size: 14px">
I conducted regular observations of the transiting exoplanet XO-2b
using the 61” Kuiper telescope on Mt. Bigelow. Analyzing the
corresponding light curves of the host star's dimming during the
exoplanet's transit, I investigated the variations in effective
radius across different wavelengths to ascertain the atmospheric
composition. Our findings highlighted the efficacy of specific data
reduction methodologies, notably the utilization of brighter nearby
reference stars, in elucidating atmospheric characteristics. This
research culminated in a presentation at the 2016 American
Astronomical Society Meeting.
</p>
<center>
<img src="assets/images/research/AAS_2016_Poster.jpg"
alt="2016 American Astronomical Society Meeting Exoplanet Poster" width="550px" height="auto">
</center>
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