Department of Mechanical Engineering

PhD, Peking University, China.

My primary research interest lies at the intersection of machine learning and solar energy forecasting. I am dedicated to advancing the accuracy and reliability of solar irradiance predictions through the development and integration of sophisticated machine learning models. This includes exploring sequential deep learning architectures to capture temporal dynamics in solar irradiance data, which enhances prediction accuracy for solar energy applications. I utilize ensemble learning techniques and hybrid models that combine various machine learning methods to improve predictive performance and robustness. Additionally, I am involved in testing and validating predictive models across diverse geographical conditions to ensure scalability and global applicability. I am also investigating how machine learning can optimize the integration and management of renewable energy sources into the power grid, focusing on efficiency and sustainability.

Recipient of the Chinese Government Scholarship for PhD studies and the HEC Student Travel Award. Active member of the Pakistan Engineering Council and a regular presenter at international conferences on renewable energy and machine learning. Serves as a peer reviewer for reputable journals and leads multiple international collaborative projects focused on solar irradiance predictions. Has completed advanced training in new energy technologies and is committed to educational outreach, promoting the integration of renewable energy solutions. Proficient in English and Urdu, with basic knowledge of Chinese and Arabic, facilitating extensive international collaborations. Future goals include developing AI-driven tools for real-time energy management and enhancing the scalability of renewable energy technologies globally

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