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Freelancer Overview

I am a PhD candidate in Mechanical Engineering specializing in the development of hybrid physics–machine learning digital twins, predictive maintenance, and explainable AI for industrial systems. My experience includes designing and validating co-simulation frameworks that integrate high-fidelity simulation models (Simulink/Simscape), real-time IoT data acquisition, and advanced machine learning techniques such as ensemble learning, deep autoencoders, and LSTM networks. I have worked extensively with data-driven fault diagnosis, condition monitoring, and remaining useful life prediction, ensuring high data quality and robust model performance in industrial and manufacturing domains. My work emphasizes the importance of accurate data labeling and annotation for training reliable AI models, and I am adept at integrating sensor data, performing feature engineering, and applying explainable AI methods to enhance interpretability and trustworthiness in decision-support systems. I am passionate about contributing to projects that require meticulous data handling, annotation, and AI model training, particularly within smart manufacturing and Industry 5.0 contexts.

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