Biography

Hi, Dave here, and welcome to my site! I’m a graduate physics student and researcher at the University of Central Florida. As a member of the Eikenberry Research Group, I focus on using multi-messenger observations to precisely determine cosmological parameters. I also work with the Exoplanet Measurement Group, where I simulate data for the James Webb Space Telescope and act as system administrator for the group’s computing cluster.

Interests

  • Cosmology
  • High Energy Astrophysics
  • Gravitational Wave Physics
  • Computational Science
  • HPC in Physics

Education

  • PhD in Physics (ongoing), 2021

    University of Central Florida

  • BSc in Physics, Astronomy Concentration, 2021

    University of Central Florida

  • BSc in Math, 2021

    University of Central Florida

Skills

Python

System Administration

Electronics

C

Laboratory Trained

Linux

Publications

Quickly discover relevant content by filtering publications.

An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b

We present the open-source Bayesian Atmospheric Radiative Transfer (BART) retrieval package, which produces estimates and uncertainties for an atmosphere’s thermal profile and chemical abundances from observations. Several BART components are also stand-alone packages, including the parallel Multi-Core Markov chain Monte Carlo (MC3), which implements several Bayesian samplers; a line-by-line radiative-transfer model, transit; a code that calculates Thermochemical Equilibrium Abundances, TEA; and a test suite for verifying radiative-transfer and retrieval codes, BARTTest. The codes are in Python and C. BART and TEA are under a Reproducible Research (RR) license, which requires reviewed-paper authors to publish a compendium of all inputs, codes, and outputs supporting the paper’s scientific claims. BART and TEA produce the compendium’s content. Otherwise, these codes are under permissive open-source terms, as are MC3 and BARTTest, for any purpose. This paper presents an overview of the code, BARTTest, and an application to eclipse data for exoplanet HD 189733 b. Appendices address RR methodology for accelerating science, a reporting checklist for retrieval papers, the spectral resolution required for synthetic tests, and a derivation of the effective sample size required to estimate any Bayesian posterior distribution to a given precision, which determines how many iterations to run. Paper II, by Cubillos et al., presents the underlying radiative-transfer scheme and an application to transit data for exoplanet HAT-P-11b. Paper III, by Blecic et al., discusses the initialization and post-processing routines, with an application to eclipse data for exoplanet WASP-43b. We invite the community to use and improve BART and its components at http://GitHub.com/ExOSPORTS/BART/.

Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843–0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90–180x faster per chain than BART on that CPU.

Experience

 
 
 
 
 

Researcher

University of Central Florida

Aug 2021 – Present Orlando, FL

Research with the Eikenberry Research Group. Topics include:

  • Precision Cosmology
  • Gravitational Wave Physics
  • Multi-messenger Astrophysics
  • Optical Instrument Design
  • Data Science
  • Linux
  • Python
 
 
 
 
 

Graduate Student

University of Central Florida

Aug 2021 – Present Orlando, FL
Physics graduate student.
 
 
 
 
 

Researcher

University of Central Florida

May 2020 – Jun 2021 Orlando, FL

Sports Data Analysis. Effects of alternative soccer point systems on league rankings and Pareto distribution models of soccer team manager rankings. Relevant experience gained:

  • Web-scraping
  • Data Science
 
 
 
 
 

Network Administrator and Treasurer

Society of Physics Students UCF

Nov 2019 – Aug 2021 Orlando, FL
Managed Website, IT needs, and finances for the Society of Physics students.
 
 
 
 
 

Researcher

University of Central Florida

Jul 2019 – Dec 2019 Orlando, FL

Created a web based curriculum for introductory physics. Topics included:

  • Education Research
  • Data Science
  • System Administration
  • Linux
  • Content Creation for Course
  • Obojobo, a web based platfrom for designing, sharing and distributing instructional materials
 
 
 
 
 

Researcher

University of Central Florida

Jun 2019 – Sep 2019 Orlando, FL
Studied pedagogy techniques for teaching introductory physics.
 
 
 
 
 

Researcher

University of Central Florida

Apr 2019 – Present Orlando, FL

Research with the Exoplanets Measurement Group. Topics include:

  • Exoplanet Atmospheres
  • Data Science
  • System Administration
  • Linux
  • Python
  • Simulating data for JWST
 
 
 
 
 

Researcher

University of Central Florida

Oct 2018 – Jun 2020 Orlando, FL
Studied mechanical properties of 3D printed materials for use in medical prosthetics.
 
 
 
 
 

Learning Assistant

University of Central Florida

Jan 2018 – May 2019 Orlando, FL

Assisted Professors in Physics 2 classrooms in a role that was identical to a traditional teaching assistant.

I learned/gained:

  • Pedagogy Techniques
  • Experience with Learning Management Systems (Canvas)
  • Teaching Experience
 
 
 
 
 

Undergraduate Student

University of Central Florida

Aug 2016 – May 2021 Orlando, FL
Physics and Math double major with a focus in Astronomy.

Accomplishments

UCF Founders' Day Award – Undergraduate Student

The award celebrates outstanding achievements of the faculty members, staff and students. The dean of each college selects one undergraduate student a year to receive the Founders’ Award for exceptional work within their area of study.

Distinguished Undergraduate Researcher Award

The Distinguished Undergraduate Researcher Award recognizes outstanding academic research by undergraduates at the University of Central Florida. Recipient for the month of April 2021.

Projects

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Classifying LIGO Data with Neural Networks and Scaleograms

Classify LIGO Data Using a Convolutional Neural Net and Scaleograms

ExoSim

An open-source simulator of exoplanet transits