I'm a Computer Science undergraduate interested in Machine Learning, optimization, and data-driven systems.

I'm currently building a strong foundation in the mathematics behind ML while developing projects that let me experiment with those ideas in practice.

My approach is simple: understand the fundamentals, implement them, experiment, and then use established tools when they make sense.

• 🤖 Machine Learning & model development
• 📐 Linear Algebra, Calculus & Statistics
• 📉 Optimization & Gradient-Based Methods
• 🎲 Probability, Monte Carlo Methods & Uncertainty
• 📊 Data Analysis & Visualization
• 🔬 Experimentation and reproducible research

Interactive exploration of gradient-based optimization and loss landscapes.

Implemented and visualized optimization algorithms to understand how different update rules behave across challenging functions, including the effects of learning rates, curvature, saddle points, and ill-conditioned surfaces.

Stack: Python • NumPy • Streamlit • Plotly • Pytest

Exploring decision-making under uncertainty through stochastic simulation.

A simulation project focused on understanding distributions of possible outcomes rather than relying solely on deterministic averages. Includes uncertainty analysis, sensitivity experiments, and tail-risk measurements.

Stack: Python • NumPy • Statistics • Monte Carlo

Implementing fundamental linear algebra operations without relying on numerical libraries.

Built core operations including matrix multiplication, determinants, Gaussian elimination, matrix inversion, and rank computation to better understand the mathematical foundations used throughout machine learning.

Stack: Python • Algorithms from Scratch • Linear Algebra

A data-driven simulation project exploring decision-making under uncertainty.

Uses real-world racing data as an application domain for studying simulation, optimization, and probabilistic decision-making.

Stack: Python • Monte Carlo • Dynamic Programming • FastAPI

Languages

Python C++ SQL

Machine Learning & Data

NumPy pandas scikit-learn Matplotlib Plotly

Development

Git GitHub Jupyter Pytest Streamlit

Areas of Interest

Machine Learning Optimization Statistics Numerical Methods Data Science Simulation

Mathematics
├── Linear Algebra
├── Calculus
├── Probability & Statistics
└── Optimization

Machine Learning
├── Regression
├── Gradient-Based Optimization
├── Model Evaluation
├── Feature Engineering
└── Classical ML

Research
├── Reading ML papers
├── Experimental design
├── Reproducible experiments
└── Mathematical foundations

I'm particularly interested in the intersection of:

Mathematics → Algorithms → Machine Learning → Real-World Data

I enjoy taking an idea, understanding the mathematics behind it, implementing it, and experimenting with how it behaves.