CV

Jared Frazier

📧 jaredfrazierapplications [at] gmail [dot] com
💻 github.com/jfdev001

Career Interests

I am an early career specialist in HPC with performance optimization, automation, and DevOps competencies honed during my time as a Scientific Programmer at the Leibniz Institute of Atmospheric Physics. I am eager to contribute these plus diverse skills in scientific modeling and simulation to a software engineering role.

Education

Master of Science (M.Sc.): Computational Science
University of Amsterdam (UvA) / Vrije Universiteit (VU)
2024

Bachelor of Science (B.S.): Computer Science
Middle Tennessee State University (MTSU)
2022

Experience

Scientific Programmer – Leibniz Institute of Atmospheric Physics (IAP)
October 2024 – Present

Improving speed and efficiency of climate data processing pipelines.
Developing in-house tools for visualizations used in scientific publications.
Facilitating code collaboration and sustainability by instructing scientists and establishing use of standard code development tools such as GitLab.
Providing debugging, documentation, and support for use of the ICON weather, climate, and environmental prediction model at the IAP.
Collaborating with German Weather Service (DWD) and German Climate Computing Center (DKRZ) on refactoring upper atmosphere physics of the ICON model.
Mentoring high school interns in climate data analysis and visualization.

M.Sc. Thesis: Discretization of Mechanical Metamaterials on Large-Scale Parallel Computers
Nov 2023 – Aug 2024

Co-developed distributed meshing algorithms for the simulation of metamaterials via finite element methods and domain decomposition methods.
See: GalerkinToolkit.jl

Intern for Imaging Biomarkers and Computer-Aided Diagnosis – National Institutes of Health
Jun 2022 – Aug 2022

Conducted full-time machine learning research funded by NIH.
Developed state-of-the-art computer vision models for 3D universal lesion detection in CT scans using the MMDetection framework.
Wrote and submitted conference paper describing improvement over state-of-the-art.

B.S. Thesis: Machine Learning in Atmospheric Science
Aug 2021 – Mar 2022

Implemented linear regression, random forest, LSTM/GRU RNNs, and CNNs for multi-step ambient temperature prediction on Mars using Curiosity Rover weather data.

NSF REU: Machine Learning in Drug Design – University of Michigan
Jun 2021 – Aug 2021

Implemented variational autoencoders for mapping discrete molecular representations to continuous representations.

Nano/Forensic Chemistry Research Assistant – MTSU
Jan 2019 – Aug 2020

Designed experiments and collected data using direct analysis in real time (DART) ambient ionization with mass spectrometry.
Authored two manuscripts and coauthored several others (see publications).

Free and Open-Source Software (FOSS) Contributions

Major Contributions

FTorch: A library for directly calling PyTorch ML models from Fortran

Added support for automatically generating a pkg config file for the library (relevant for including FTorch as a dependency in legacy build systems) (PR: #464).
Added support for building as static library (relevant for use with operational numerical weather prediction models like the German Weather Service’s ICON fork) (PR: #448).
Added CI for Intel compilers (PRs: #438, #449).
Improved installation and testing stability (PRs: #431, #434, #437, #450).
All merged PRs and issues

Minor Contributions (Documentation, Bug Fixes, etc.)

Ferrite.jl (Merged PRs, Issues).
PETSc (Merged PRs).
fortran-lang/fftpack (Merged PRs).
Goddard-Fortran-Ecosystem/pFUnit (Issues).
SpeedyWeather.jl (Issues).

Skills

Coding:
- Working Knowledge: Python, C, Fortran, Bash, Julia
- Basic Knowledge: C++, R, Mathematica, MATLAB
Technologies:
- HPC: MPI, OpenMP, OpenACC*, CUDA*, Slurm
- Version Control: Git
- Code Collaboration: GitHub, GitLab
- Build: CMake, Make, GNU Autotools
- Compilers: gcc, gfortran, ifx/ifort
- Debuggers and Analysis: gdb, pdb, valgrind
- Libraries: TensorFlow, PyTorch, NumPy, Pandas, xarray, NetCDF, Matplotlib, Eigen3
- OS: Linux (e.g., Ubuntu, Red Hat), Windows
- Containerization: Docker
- Miscellaneous: CI/CD (e.g., GitHub Actions), Prompting LLMs (e.g., ChatGPT), nvim, tmux
Languages: English (native), German (B2**), Dutch (B1**), Italian (A2**)

*Basic Knowledge, **Estimated CEFR Proficiency

Publications

Frazier, J., Mathai, T.S., Liu, J., Paul, A., & Summers, R.M. (2023). SPIE Medical Imaging. doi: 10.1117/12.2655250.
Mahjour, B., Bench, J., Zhang, R., Frazier, J., & Cernak, T. (2023). RSC: Digital Discovery. doi: 10.1039/D3DD00008G.
Frazier, J., Cavey, K., Coil, S., Hamo, H., Zhang, M., & Van Patten, P. G. (2021). Langmuir, 37 (50), 14703-14712.
Tilluck, R., Mohan N., Hetherington, C., Leslie, C., Sourav, S., Frazier, J., et al. (2021). J. Phys. Chem. Lett., 12, 9677-9683.
Liang, J., Sun, J., Chen, P., Frazier, J., Benefield, V., & Zhang, M. (2021). Food Research International, 140, 109877.
Frazier, J., Benefield, V., & Zhang, M. (2020). Forensic Chemistry, 18, 100233.
Liang, J., Frazier, J., Benefield, V., Chong, N. S., & Zhang, M. (2019). Analytical Chemistry, 92(2), 1925–1933.

Conference Presentations

Frazier, J. (2021). Blue Mars Initiative: Developing Linear Regression and Artificial Neural Network Models to Forecast Mesoscale Martian Weather Conditions. National Council on Undergraduate Research (Virtual).
Frazier, J. (2020). Practical Investigation of Direct Analysis in Real Time Mass Spectrometry for Fast Screening of Explosives. Posters at the Capitol, Nashville, TN.
Frazier, J. (2019). Fast Screening of Explosives by Direct Analysis in Real Time Mass Spectrometry. 67th ASMS Conference, Atlanta, GA.

Key Graduate-Level Coursework

Uncertainty Quantification – 8.5/10
Bioinformatics I (Dynamical Systems Modeling) – 8.5/10
Data Mining Techniques – 9/10
Programming Multi-Core & Many-Core Systems – 7.5/10
Parallel Programming Practical – 8.0/10
Numerical Algorithms – 8.5/10
Programming Large-Scale Parallel Systems – 8.5/10

Academic Honors and Awards

Amsterdam Merit Scholarship (2022 – 2024): The most prestigious merit scholarship at the University of Amsterdam, awarded to only 1–2 science master’s students outside EU/EEA each year.
Goldwater Scholarship (2020 – 2022): One of the most prestigious, nationally competitive U.S. scholarships supporting future leaders in natural sciences, engineering, and mathematics.
DAAD RISE Scholarship (2020, Canceled due to COVID-19): Research internship with Forschungszentrum Jülich / Helmholtz Institute for Renewable Energy (IEK-11).

Media Coverage

MTSU Alumni Spotlight (July 2025) An Atmosphere of Change – Feature on Involvement as Scientific Programmer at Leibniz Institute of Atmospheric Physics.
MTSU True Blue Mars Magazine Feature (July 2021) – Feature on Machine Learning Research for Martian Colonization.
MTSU Out of the Blue Interview – Blue Mars Initiative (July 2021) – Interview with VP for Marketing and Communications.
Goldwater Scholar Coverage by MTSU News & Rutherford Source (Apr 2020) – Local news coverage of Goldwater Scholarship Achievements.