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.

Community

Beyond research, I bake, play music, make coffee, and am the co-owner of the world's greatest dog, Pod. I also love giving outreach talks, and am the drummer for the all-astronomer rock band Night Lunch.

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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