Intro
Education: Ph.D.: University of Washington (2021), B.A.: Wesleyan University (2015). MBA: Western Governors University (2025)
Interests: Statistical inference, big data, deep learning.
Publications: ADS Author Query
About Me: I am an Astrophysicist-turned-Data Scientist at Phillips, Fractor, & Company.
Previously, I received my Ph.D. in August, 2021 from the University of Washington before completing a
Postdoctoral Fellowship at the Carnegie Observatories.
I am broadly interested in machine learning and data science. The one unifying theme across
both my enterprise and academic work is a desire to use both well-established and cutting-edge m
ethods in novel contexts: from stars and supernovae to dollars and cents.
On this site, you can learn
more about my professional interests, or see what I do
outside of work. If you'd like to reach
out with a question or opportunity, please see my contact info.
Data Science & Software
Many people are surprised to learn that modern astrophysics is
data science. Current observatories are churning out 10s of terabytes
of data each night, a problem that requires modern solutions.
As an astronomer, I developed a strong interest in Bayesian inference techniques
to extract physically meaningful results. I developed models for studying niche
populations of stellar behemoths using Markov chain Monte Carlo
simulations, time-domain analysis techniques for separating the signal from the noise
with Gaussian processes, and trained multiple machine learning models to classify stars
and learn their properties.
Since leaving the field, I have worked as a data scientist and statistician at Phillips,
Fractor, and Company, a legal services company that provides econometric analyses and
expert witness testimony. In addition to my casework at PFC, I developed the analysis backend of
Lantana Reports, a platform to aid in the valuation of the
economic losses suffered by survivors of natural disasters using real-time data and human expertise.
Finally, I am an open-source software developer. Some of my projects include
a package for efficient
wavelet transforms on irregularly-sampled time-series data,
data analysis workflows for spectroscopic observations, and a tool for
hierarchical Bayesian inference on stellar populations.
Contact
If you have any questions about my work, feel free to reach out.
I am also available to speak at your institution, and am always happy
to give public outreach talks.
Email:
trevorzaylenn@gmail.com
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i = 0;
while (!deck.isInOrder()) {
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';
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19.99 |
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