Recent research:
Across a diverse set of applications, the researcher advances AI-enhanced decision support for environmental health and renewable energy, integrates intelligent control with sustainable farming, and contributes to power systems education and safety. The work combines hybrid machine learning (Random Forest, CatBoost, Gradient Boosting, and 1D CNN) with real-time chatbot interfaces to predict solar irradiance and issue personalized health-environment alerts, aiming to strengthen sustainable energy integration and user accessibility. In agriculture, the research evaluates organic hydroponic systems with intelligent monitoring to optimize romaine lettuce growth under variable recirculation, linking process variables to physiological plant responses and demonstrating potential for water- and energy-efficient food production under climate change. In power engineering, the studies address electrical breakdown in oil-filled medium-voltage switches through mathematical simulation and experimental validation, and examine grounding mesh configurations for touch and step voltages per IEEE-80 standards to enhance safety. Additional work includes AI-based object detection on production lines using YOLOv8 on embedded hardware, and educational tooling like portable PLC-based didactic systems to improve practical engineering training. Collectively, the profile highlights a methodological emphasis on data-driven modeling, sensor-informed control, and safety-critical evaluation within energy, environment, and education domains, with demonstrable impact for sustainability, reliability, and knowledge transfer.