Currently working in Information Security. Passionate about cybersecurity, machine learning, automation, and building tools that solve real-world problems.

Information Security Junior

• 🔒 Application security and vulnerability management
• 🛡️ Collaboration with development teams on secure software practices
• 🔍 Security assessment and risk analysis
• 📊 Working with security frameworks and industry standards

• 🔐 Application Security - Secure software development and vulnerability assessment
• 🛠️ DevSecOps - Integrating security into development workflows
• 📋 Security Frameworks - Industry standards and best practices

Production-ready machine learning system for predicting ATP tennis match outcomes.

Performance: 80.71-84.28% accuracy | 0.918-0.937 AUC-ROC (temporal holdout validation)

Key Features:

• XGBoost classifier with Bayesian hyperparameter optimization
• Surface-specific models (Hard, Clay, Grass courts)
• Dynamic ELO rating system (global + surface-specific)
• 40+ engineered features (win rates, form, H2H, serve stats)
• SHAP model interpretability
• GPU acceleration (CUDA)
• Comprehensive CLI with 10+ commands

Tech Stack: Python, XGBoost, Scikit-learn, Pandas, NumPy, SHAP, CUDA

Production-ready Bash utilities for automation, privacy, and productivity.

Tools:

• 🛡️ Metadata Cleaner: Privacy tool removing EXIF, sensitive data, execution history from files (images, documents, notebooks). Features backup system, audit verification, aggressive sanitization mode.
• 🚀 Publish Repo: Automated Git repository initialization and GitHub publishing with dry-run mode.
• 🎮 Life Game: Gamified habit tracker turning daily tasks into an XP-based progression game with badges and achievements.

Tech Stack: Bash, exiftool, mat2, Git, GitHub CLI

Study materials, notes, and projects from certifications and online courses.

Completed:

• CCNA 200-301 (2025): Networking fundamentals, OSPF, VLANs, IPv4/IPv6, ACLs, NAT, QoS, network security, automation
• FreeCodeCamp - Data Analysis with Python (2024): Pandas, visualization, statistical analysis
• FreeCodeCamp - Scientific Computing with Python (2024): OOP, algorithms, testing

Tech Stack: Python, Networking protocols, Cisco IOS

End-to-end data science projects demonstrating the complete ML workflow (2023-2024).

Projects:

• Machine Learning: Wine classification with Random Forest (99% accuracy)
• Data Visualization: Global food production environmental impact analysis
• SQL Analysis: U.S. travel safety analysis with PostgreSQL
• Python Automation: Intelligent file organization system
• Final Project: Student performance prediction with statistical modeling

Skills: Data wrangling, EDA, feature engineering, model evaluation, interactive visualization

Tech Stack: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, Plotly, PostgreSQL

Computational physics simulations and scientific computing projects from university coursework (2020-2022).

Statistical Physics (2022)

• Monte Carlo simulations (Metropolis & Wolff algorithms)
• 2D Ising model: phase transitions and critical phenomena
• Random walks: diffusion and self-avoiding walks
• SIR epidemic model on complex networks

Experimental Data Processing Lab (2021)

• Numerical methods: integration, differential equations, root finding
• Object-oriented design with C++
• ROOT framework for scientific visualization

Computer Science with C++ (2020)

• Fundamentals: algorithms, data structures, pointers
• Structured programming and modular design

Tech Stack: C++, Python, ROOT, NumPy, Matplotlib, Jupyter

• Information Security: Network security fundamentals, security best practices, continuous learning in cybersecurity
• Networking: TCP/IP, routing protocols (OSPF), VLANs, network configuration and troubleshooting (CCNA level)
• Automation & Scripting: Bash scripting, Python automation, file processing, workflow automation, privacy tools
• Machine Learning: Classification, regression, ensemble methods, hyperparameter tuning, time series validation, model interpretability
• Data Science: Feature engineering, EDA, statistical analysis, data visualization, predictive modeling
• Scientific Computing: Monte Carlo simulations, numerical methods, computational physics, ROOT framework

Securing systems • Building tools • Sharing knowledge through code