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

Application Security Associate

• 🔒 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

Machine learning system that predicts ATP match outcomes, evaluated on matches it never saw.

Performance: 66.3% accuracy, 0.603 log loss and 0.731 AUC on 19,086 held-out matches from May 2019 to May 2026, ahead of Elo-only (64.3%) and ranking-only (63.9%) baselines. The model used for predictions is then retrained on all 190,620 matches since 1970.

Key Features:

• XGBoost with a strict chronological train, validation and test split
• Overall and surface Elo ratings with experience-based K-factor
• 80 leakage-free features: form, serve and return stats, workload, head-to-head, ranking
• Tests proving that features never depend on the match result and match between training and prediction
• Model explanations with TreeSHAP
• Pickle-free model storage verified with SHA-256 manifests; hash-checked data download
• Reproducible from a clean clone: atp download and atp train recreate every reported number; optional CUDA training
• CLI with 11 commands, 170 tests (97% coverage), CI with CodeQL, Bandit and pip-audit

Tech Stack: Python, XGBoost, Scikit-learn, Pandas, NumPy, GitHub Actions

Bash tools for privacy, publishing and productivity, built with a secure development life cycle.

Tools:

• 🛡️ Metadata Cleaner: removes EXIF, GPS, document and notebook metadata, redacts secrets and personal data in text files, and audits the result.
• 🚀 Publish Repo: initialises a repository, scans pending files for secrets, commits after confirmation and pushes without touching existing branches.
• 🎮 Life Game: gamified habit tracker with levels, badges, a Pomodoro timer and Markdown study plan import.

Security: STRIDE threat model, no eval or sourcing of data, helper programs that never write next to the user's files, 87 bats tests including injection and symbolic-link cases, CI with ShellCheck on every change.

Tech Stack: Bash, ExifTool, qpdf, mat2, Git, GitHub CLI, ShellCheck, bats

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

Completed:

• ISC2 Certified in Cybersecurity (CC) (2026): security principles, incident response, access control, network security, security operations
• 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 (98% test 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

• Application Security: secure development life cycle, STRIDE threat modelling, secure code review, supply-chain hardening (pinned actions, hash-locked dependencies, CodeQL)
• 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, leakage-free feature engineering, time-based 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