My Projects

Security-first LLM fine-tuned for Agentic DevOps that translates natural language into safe, valid Bash commands while achieving a 100% adversarial attack blocking rate. Implements a "Defense in Depth" architecture with sanitized QLoRA fine-tuning and three-layer runtime guardrail system.

Layered security framework for LLMs and agents that combines deterministic rules, semantic classifiers, and policy enforcement to detect prompt injection, malicious intent, and unsafe reasoning before outputs reach users or systems.

Security-focused RAG application with built-in guardrails for safe document and codebase analysis with AI.

Enterprise-Grade Security: Advanced ensemble ML system with 9 trained models, grey-zone decision gating, and SHAP explainability. Features Streamlit web interface, CLI tools, and production-ready deployment achieving 95%+ recall for SOC environments.

An intelligent security system with face detection and motion tracking, built with Python and OpenCV.

AI-powered malware detection using LightGBM and static PE file analysis for enterprise endpoint security. Achieves 98%+ F1-score with millisecond prediction times.

Comprehensive malware detection using PE file metadata with advanced feature engineering, visualization, and model validation. Multi-method feature selection with PCA, t-SNE, and ensemble methods.

Integration Capstone: Combines supervised and unsupervised ML approaches with recall-first optimization for SOC environments. Synthesizes insights from LightGBM and PE metadata projects into a unified detection framework achieving 99.95% recall.

Proactive Malware Defense: Signature-free malware detection using unsupervised learning for Aegis AI Security's Sentinel Endpoint Agent. Achieves 93% AUC with Isolation Forest for real-time pre-execution threat analysis.

Malware Triage: Advanced ensemble-based PE file analysis combining Isolation Forest, K-Means, and One-Class SVM. Features interpretable anomaly scoring and confidence-based triage for SOC environments with rich visualization suite.

Production-Grade Multi-Agent Security Evaluation Framework: Advanced evolution of Trust Bench featuring SecureEval guardrails, intelligent agent routing, consensus building, and operational excellence. Features specialized security, quality, and documentation agents with cross-agent collaboration, weighted scoring, confidence metrics, and comprehensive export capabilities. Includes structured logging, health probes, CI/CD pipeline, and 79% test coverage.

Secure multi-agent SOC triage with A2A guardrails and structured observability, designed as a v1 competition finalist with v2 upgrades planned for expanded SOC automation.

A LangGraph-based multi-agent workflow that inspects software repositories for security leaks, code quality gaps, and documentation health. Features a collaborative system of specialized agents working together to provide comprehensive repository analysis.

Lightweight TypeScript agent with web automation capabilities. Connects to the MCP Core Server to perform tasks and process information.

Automates Windows tasks, analyzes system logs using AI, and helps manage your digital workspace efficiently.

A Neural Network for emotion detection in text using PyTorch and the HuggingFace Transformers library.

Advanced regression analysis using the XGBoost algorithm, including feature importance and model evaluation.

Predicting customer churn using a neural network model built with TensorFlow/Keras, helping banks identify at-risk customers.

Machine learning model using XGBoost to predict visa approval outcomes based on applicant data and historical patterns.

Decision tree model for predicting loan approval decisions, including feature importance analysis and model interpretation.

Comprehensive analysis of FoodHub's food delivery data, including customer behavior, order patterns, and restaurant performance metrics.

Retrieval-Augmented Generation (RAG) system for medical information retrieval and question answering using NLP and vector databases.

End-to-end machine learning solution for retail sales prediction. Features include data preprocessing, feature engineering, and model deployment with FastAPI.

End-to-end Azure fundamentals — ARM-based VM deployment with tagging, governance policies, and Azure Monitor alerts. Demonstrates cloud architecture, cost optimization, and observability with Infrastructure-as-Code (IaC).

A playful security "gym" for AI engineers to build muscle memory around sanitization, API management, and guardrail patterns using a Mastery Loop training engine.

A simple yet powerful tool for identifying potential short squeeze opportunities in the stock market.