Background in Earth, Atmospheric, Planetary, & Data Sciences.#

Professional Experience#

Atmospheric Data Scientist

October 2023-May 2025.

Model Evaluation and Outreach with the Community Multiscale Air Quality Model (CMAQ) Model Development Team

U.S. Environmental Protection Agency (Office of Research and Development, Center for Environmental Measurement and Modelling) & Oak Ridge Associated Universities (National Student Services Contract)

Primary Duties#

  • Perform model evaluation for the EPA’s Community Multiscale Air Quality (CMAQ) scientific development team by investigating the impact of gridded inputs on CMAQ Ozone outputs with historical data. Analyze and package results visually and in writing to project leadership to deliver to State and Local organizations attempting to reach ozone attainment.

    • Developed workflows providing a spatiotemporal analysis of the relationship between trends in modeled (NCAR’s Weather Research & Forecasting Model; WRF) meteorology and modeled (EPA’s Air Quality Time Series; EQUATES) ozone bias.

    • Perform big data wrangling, preprocessing of twenty model years of data in a High-Performance Computing environment; leveraged EPA’s Atmospheric Model Evaluation Tool (AMET) for WRF data acquisition, QA/QC of scientifically relevant data.

    • Trained and optimized a machine learning model suite to perform feature permutation for quantifying the relative importance of meteorological and non-meteorological potential drivers of CMAQ MDA8 Ozone Bias.

    • Design investigation within the scope of physical regions in model domain, as well as the administrative regions that govern air quality and emissions. Deploy workflow for metropolitan case study usage in projects supporting partner organizations and model user groups.

  • Speaker at CMAS Conference 2024: ‘Evaluating The Relationship of Modeled Meteorology and Ozone Bias using Random Forest Regression’

    • See This Page for abstract and presentation slideshow on evaluation results of the base model.

Additional Duties#

  • Develop public-facing notebook tutorials for analyzing cloud-based CMAQ/EQUATES data hosted on AWS Open Data Registry by reading and compressing the S3 bucket data (>1 TB) into local and SageMaker Jupyter servers.

  • Contributor to CMAQ’s Github through documentation upkeep and Github Actions workflows.

  • Lead developer of multi-language (Python, HTML, Markdown Flavor) workflow for building and deploying CMAQ’s Github Pages hosted documentation website.

  • Active member of internal Center for Environmental Measurement and Modeling’s Data Science development community; led demonstrations and discussion of Pythonic data science and machine learning techniques and software configuration.

  • Developed an internal tool for directly querying and reading CMAQ’s SQL held data into a Python environment as interactive datasets.

  • Consult generally on adjacent projects exploiting geospatial data with Python/SQL software and machine-learning techniques.

Part-time Experiences#

  • Substitute Teacher, Scoot Education

    • Austin, TX / Nov. 2022 - April 2023

  • Python Tutor, Self-Employed

    • Metamora, IL / April 2023 - October 2023

  • Undergradate Research Assistant, Purdue University Department of Earth, Atmospheric, and Planetary Sciences

    • West Lafayette, IN / May-Dec. 2019, May 2020-2021

  • Undergraduate Teaching Assistant, Purdue University Department of Earth, Atmospheric, and Planetary Sciences

    • West Lafayette, IN / Aug.-Dec. 2020 / EAPS 102: Earth Science for Elementary Teachers

Education#

October 2021-November 2022

MSc Environmental Data Science & Machine Learning

Imperial College London / London, England

Notable Awards: Merit Graduate

Course Details: MSc EDSML

Extracurricular Activities: Imperial College Union, Player/Coach for American Football Club

August 2017-May 2021

BS Planetary Sciences

Purdue University / West Lafayette, IN

Notable Awards: Presidential Scholarship, Paul & Linda Krishna Scholarship in Earth, Atmospheric, and Planetary Sciences, 6x Dean’s List Placement, 6x Semester’s Honors

Course Details: BS Planetary Sciences

Extracurricular Activities: College of Science Ambassador, Gimlet Leadership Honorary (Secretary), Planetary Science Society of Purdue, Purdue Astronomy Club, Wesley Foundation Service

Skills in Data and Geoscience#

Programming Languages#

  • Python:

    • Expert Numerical processing and applications, data visualization libraries

      • Numpy, Pandas, Scipy, Matplotlib (Pyplot), Plotly, Seaborn

    • Fluent Sci-Kit Learn; basic-to-intermediate machine learning applications

    • Advanced Deep-learning libraries

      • Pytorch, Keras (Tensorflow), OpenCV

    • Advanced Sphinx, Sphinx-Design; for automated HTML deployment of repository code and text (markdown, restructured-text) documentation

    • Advanced PyTest, for automated and customized code testing

    • Experienced Specialized libraries for the environmental data manipulation

      • Geopandas, Xarray, NetCDF4, Segyio, GDAL, Shapely, Rasterio

  • Familiar Trained academically, practiced in research/employment occasionally

    • R (RStudio), C++

  • Experienced/Practicing

    • SQL, primarily through Python’s interfacing library mySQL

Programming Environments#

  • Github (Experienced):

    • git, Github CLI

    • Github Actions for basic continuous integration practices and automated documentation creation

    • Github Pages for hosting documentation websites directly from repository

    • Contributor to the Community Multiscale Air Quality (CMAQ) model repository, familiar with integration and repository maintaining best-practices.

  • Jupyter Lab preferred for Python and general software development, familiar with VisualStudio

  • Proficient with command-line programming and shell scripting, and ensuring sustainability across operating systems

    • MacOS, Windows, Linux; Bash, C-Shell

  • Comfortable in virtual and High-Performance Computing environments

    • Proficient scripting within or writing scripts to interact with cloud-based computing

Remote Sensing/Environmental Data#

  • General Software/Geographic Information Systems (GIS) Experience

    • ERMapper, Petrel, ArcGIS, IDL, ENVI, MASTER, CRISM, Google Earth Engine

  • Comfortable in exploiting satellite imagery and various environmental sensor datasets (RADAR, LIDAR, Spectral) for identification of soil, water, greenery, etc. image data.

  • Advanced Model Output Experience

    • EPA’s Community Multiscale Air Quality (CMAQ) Model

    • NWP:

      • NCAR’s Weather Research & Forecasting (WRF) Model for air quality and meteorology evaluation, respectively.

  • Advanced Dataset Knowledge

    • NetCDF-4, h5NetCDF, Zarray (.nc, .zarr), Segy Seismic Volumes (.sgy)

Machine Learning#

  • Familiar and comfortable with using a variety of machine learning and deep-learning algorithms to develop applications for data within and beyond environmental sciences. Experience with regression/classfication and supervised/unsupervised architectures.

    • Linear, Logistic, Forest, CNNs, Convolutional Autoencoders, GANs, LSTMs, FFNs, Custom Architectures

  • Experienced with developing clustering and computer vision models for seismic, satellite, and general imagery data. (See Capstone Project)