Dr. Muhammad Kamran Ishfaq is an Assistant Professor in the Department of Electrical Engineering at Government College University Faisalabad, Pakistan, with more than 19 years of academic and research experience in electrical engineering, communication systems, and intelligent energy technologies. He received his PhD in Electrical Engineering from Universiti Teknologi Malaysia (UTM), Malaysia and his MSc and Bachelor’s degrees in Electrical Engineering from the University of Engineering and Technology (UET), Lahore, Pakistan. His research interests include artificial intelligence and machine learning for electrical power systems, short-term load forecasting, wireless communications, 5G/6G technologies, MIMO systems, and antenna design. He has published more than 13 peer-reviewed journal articles and has supervised postgraduate research involving AI-based short-term load forecasting and advanced electrical power-system applications. His interdisciplinary research combines data-driven machine learning methodologies with engineering systems to develop accurate and robust solutions for emerging smart-grid and energy applications.
Dr. Muhammad Kamran Ishfaq is an Assistant Professor in the Department of Electrical Engineering at Government College University Faisalabad, Pakistan, with more than 19 years of academic and research experience in electrical engineering, communication systems, and intelligent energy technologies. He received his PhD in Electrical Engineering from Universiti Teknologi Malaysia (UTM), Malaysia and his MSc and Bachelor’s degrees in Electrical Engineering from the University of Engineering and Technology (UET), Lahore, Pakistan. His research interests include artificial intelligence and machine learning for electrical power systems, short-term load forecasting, wireless communications, 5G/6G technologies, MIMO systems, and antenna design. He has published more than 13 peer-reviewed journal articles and has supervised postgraduate research involving AI-based short-term load forecasting and advanced electrical power-system applications. His interdisciplinary research combines data-driven machine learning methodologies with engineering systems to develop accurate and robust solutions for emerging smart-grid and energy applications.