I am Muhammad Tanveer ul Islam, a PhD student in Engineering and Management at the Center for Management Studies, Instituto Superior Técnico (IST), University of Lisbon. I hold a Bachelor's degree in Mechanical Engineering from the University of Engineering and Technology (UET), Lahore, Pakistan (2009), and a Master’s degree in Aerospace Engineering from the Beijing University of Aeronautics and Astronautics, China (2017), where I graduated with honors and a converted Portuguese grade of 19/20. My professional career began in 2009 as an Assistant Technical Manager with the National Engineering and Scientific Commission (NESCOM), Pakistan. Over six years (2009–2015), I was involved in large-scale national energy infrastructure projects, particularly the installation and commissioning of combined-cycle power plants. This role equipped me with hands-on experience in plant operations, performance optimization, and interdisciplinary project management. In 2015, I decided to pursue a Master's degree to deepen my technical knowledge and engage with advanced research methodologies. After completing my degree, I returned to the engineering field in 2017, and joined the Renewable energy Sector. Between 2017 and 2023, I led several multidisciplinary projects focused on energy systems, infrastructure planning, and sustainable development at Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. These projects allowed me to apply my engineering and management knowledge to create innovative, large-scale renewable energy solutions, contributing to the country’s clean energy initiatives. As my career progressed, I recognized the growing urgency of the global energy transition and need to contribute to solving the challenges of climate change. Motivated by deep desire to tackle global energy sustainability issues, I decided to transition from industry to academia, with the goal of pursuing a PhD. I aim to merge my practical engineering background with advanced research to develop meaningful, data-driven solutions for sustainable energy planning, particularly in the context of renewable energy integration and resource optimization. My research at Tecnico focuses on integrating Multi-Criteria Decision Analysis (MCDA) and Machine Learning (ML) for decision support in sustainable energy planning. I am developing an MCDA framework to evaluate and prioritize municipal photovoltaic (PV) projects, incorporating technical, economic, environmental, and social criteria. In parallel, I am building ML models for energy demand forecasting and optimized distribution within Renewable Energy Communities (RECs), using historical and real-time data. This research contributes to the development of hybrid decision-support tools that align with UN Sustainable Development Goal 7 (Affordable and Clean Energy), aiming to provide data-driven insights for policymakers and planners navigating complex energy transitions. As part of my academic contributions, I have presented a peer-reviewed conference paper and a conference abstract at international forums. Additionally, I have a journal article currently under revision, focusing on MCDA framework for municipal energy planning. These works reflect my commitment to advancing knowledge in the fields of sustainable energy systems, decision analysis, and applied machine learning in engineering contexts. By blending engineering practice with management science, I strive to bridge the gap between technical complexity and practical decision-making, offering actionable insights for policymakers, industry leaders, and researchers working toward a more sustainable, resilient, and community-focused energy future.