Artificial Intelligence Engineer • Mathematics Enthusiast
I approach Computer Science through the lens of mathematical rigor. My focus is on understanding the theoretical foundations of algorithms to build robust, efficient, and scalable AI solutions. I value precision in code and clarity in logic.
- Machine Learning: Model architecture, algorithm optimization, and predictive analysis.
- Mathematics: Applying Linear Algebra and Probability Statistics to solve data problems.
- Software Engineering: Writing clean, maintainable, and well-documented code (Python, C++, Java).
| Category | Technologies |
|---|---|
| Languages | Python, C/C++, Java, R, Matlab |
| AI / ML | PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy |
| Infrastructure | Docker, Git, Linux |
Open to discussions on Deep Learning theory and Open Source collaboration.

