Decoding planetary atmospheres and space mission data using high-performance computing, AI, and reproducible scientific software.
I am a Planetary Scientist and Computational Physicist focused on building
high-performance, reproducible computational workflows for planetary
atmospheres, airglow emissions, space weather, and exoplanet research.
My work bridges theoretical astrophysics, mission-scale data analysis, and
production-quality scientific software, enabling raw telemetry from space
missions to be transformed into validated, publication-ready scientific insight.
- 🔭 Current work: Automated airglow analysis pipelines & AI for exoplanets
- 🌱 Learning: GPU acceleration for atmospheric and radiative-transfer models
- 👯 Collaboration: Open science, mission data analysis, scientific software
- 💬 Ask me about: Python for astronomy, HPC, numerical modeling
Physics of upper-atmospheric emissions using space- and ground-based observations.
Modeling planetary atmospheres, chemistry, and dynamics across the Solar System
and exoplanetary systems.
Algorithm-driven analysis of NASA, ESA, ISRO, and JAXA mission datasets.
High-performance workflows combining numerical methods, visualization, and
machine learning.
Core Strengths
- Scientific software engineering
- Data-intensive pipelines
- AI/ML for physical sciences
- High-performance computing (HPC)
- Research-to-production workflows
Technical Stack
- Python, C/C++, Fortran, Go
- NumPy, SciPy, Astropy, xarray, Geopandas
- PyTorch, TensorFlow, scikit-learn
- MPI, OpenMP, Linux
All repositories are developed with:
- 📄 Journal-grade reproducibility standards
- 📦 Modular, documented architectures
- 🧪 Validation and benchmarking
- 🔁 Automated workflows
- 📚 Citation-ready structure (Zenodo/DOI compatible)
This GitHub profile is intended to serve as a computational methods
supplement to peer-reviewed publications and funded research proposals.
Languages
Python • Fortran • C/C++ • Julia • Bash
Scientific Computing
Astropy • NumPy • SciPy • xarray • Pandas
Machine Learning
PyTorch • TensorFlow • Scikit-learn
HPC & DevOps
MPI • OpenMP • SLURM • Docker • Git • Linux
Visualization
Matplotlib • Seaborn • Plotly • ParaView
- 🌐 Website: https://masoomjethwa.github.io
- 📄 Google Scholar: https://scholar.google.com/citations?hl=en&tzom=-330&user=KMDIbVwAAAAJ
- 🧠 ORCID: https://orcid.org/0000-0003-0338-9464
- 💼 LinkedIn: https://linkedin.com/in/masoomjethwa
- Architecting reproducible code for atmospheric science
- Transforming raw mission telemetry into scientific insight
- AI in education: a planetary science perspective
Interested in collaborating on space science, open science, or funded research?
