A foundational researcher with Computer Science BSc - University of London with a focus on AI, Machine Learning, Data Science and Deep Learning. Passionate about research, reproducibility and high‑quality open‑source implementations.
- Foundation models, NN, NLP, Evaluation & robustness
- Efficient training, data pipelines, and MLOps
- Databases for ML, data management, and reproducibility
- Contribution in Open Source projects
- Improve programs computational efficiency and software modularity
- Create beneficial programs and tools
- I Use Arch BTW
- Zettlekasten - Knowledge Hub
- Physics-Informed Diffusion Model for Generating Synthetic Extreme Rare Weather Events Data — a physics-informed diffusion model based on the Context-UNet architecture to generate synthetic, multi-spectral satellite imagery of extreme weather events with scalability.
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SERWED: A Physics Informed Diffusion Model with scalability for High Picture Dimensions.
[Tested using NASA GEOS Satellite Data for Rare Cyclone Events + Research Paper]
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Rustingo: A Rust ML Library that uses CPU and GPU resources [On Going]
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MarcoPolo: Most efficient way to get Books or Web Scrape to Download [PDF, Img, Videos] from any site on the internet.
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Vesper: CLI messaging program made in Rust, C and Python "Safe until Quantum Computers take over".
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BSc Computer Science University of London |
"Whatever is Easy is Hard, Whatever is Hard is Easy"