Building intelligent systems that defend, adapt, and respond to cyber threats autonomously. Specialized in AI-driven security tools and graph-based threat modeling.
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Passionate about building the future of cybersecurity through machine learning and automation.
Building intelligent systems that defend, adapt, and respond to cyber threats autonomously. I believe the future of cybersecurity lies in machine learning–driven defense, where systems not only detect attacks but learn and evolve with every threat.
Developing AI-assisted security tools — from incident response automation to graph-based intrusion detection using neural networks.
Exploring how Graph Neural Networks (GNNs) can map relationships between attack vectors, user behavior, and network events — enabling systems to anticipate and isolate threats before they spread.
A selection of my cybersecurity and machine learning projects
Advanced semantic search implementation using machine learning techniques for intelligent content retrieval.
Blockchain implementation with security features and distributed consensus mechanisms.
Privacy-preserving machine learning framework for distributed training across multiple nodes.
Deep learning project focusing on Long Short-Term Memory networks for sequence prediction tasks.
Intelligent Tic-Tac-Toe game with AI opponent using reinforcement learning techniques.
Comprehensive security tool for bug bounty, penetration testing, and red teaming operations.
Proficient in various programming languages, frameworks, and security tools
Interested in AI-driven security systems, automation in SOC operations, or graph-based threat modeling? I'd love to chat!