Computer Science M.S. with a Mathematics background.
I build research-driven, performance-aware systems where correctness,
scalability, and real constraints matter.
My work sits at the intersection of machine learning, high-performance computing, and systems engineering, with applied projects in finance, security, and modern software architecture.
-
Research systems that test model reliability, limits, and failure modes
(e.g., noise robustness in hybrid quantum neural networks) -
Performance-critical code where architecture matters
(CUDA GPU acceleration, MPI distributed memory, SLURM-based execution) -
Applied ML systems that go beyond notebooks
(time-series modeling, inference optimization, deployment-ready pipelines) -
Secure and scalable software systems
(backend/frontend architecture, blockchain-based identity primitives)
Across all projects, the emphasis is the same: measure first, reason from data, design for scale.
The pinned repositories below represent complete, production-minded projects:
- Research on hybrid quantum neural networks and noise robustness
- CUDA and MPI projects demonstrating GPU acceleration and distributed scaling
- End-to-end machine learning systems with performance constraints
- Quantitative finance models grounded in mathematical reasoning
- Security-focused full-stack systems, including blockchain identity verification
Each repository is documented to explain what was built, why it matters, and what tradeoffs were encountered.
Languages:
Python · C++ · CUDA · Java · R · SQL · JavaScript / TypeScript
Parallel & Systems:
CUDA · MPI · OpenMP · SLURM · Linux
ML & Data:
PyTorch · ONNX · scikit-learn · NumPy · Pandas
Web & Infrastructure:
Node.js · Next.js · Docker · GraphQL
- LinkedIn: https://www.linkedin.com/in/jesusrgil/
- Email: jesusgil1098@gmail.com

