Amir Gharavi

Dr. Amir Gharavi serves as a Lecturer (Teaching) in Energy and Data Analytics at University College London's Bartlett School of Environment, Energy, and Resources (BSEER), where he also directs the Energy System Data Analytics (ESDA) program. His academic journey spans petroleum engineering and data science, reflecting his interdisciplinary expertise.

Education:

• PhD in Artificial Intelligence applications for oil reservoirs (University of Portsmouth)
• MSc in Petroleum Engineering
• BSc in Petroleum Engineering
• BSc in Genetics

Dr. Gharavi's research bridges traditional energy systems with cutting-edge data analytics, focusing on four interconnected domains: Artificial Intelligence (deep learning for predictive modeling and evolutionary computation), Energy Transition (low-carbon strategies and socio-economic impacts), Renewable Energy (grid integration and lifecycle analysis), and Petroleum Reservoirs (AI-enhanced characterization and enhanced recovery techniques). His work demonstrates how machine learning transforms both conventional and renewable energy sectors.

His publication portfolio reveals a clear trajectory from petroleum reservoir analytics toward broader energy system applications, with recent 2024 works expanding into collision avoidance systems - indicating strategic diversification into AI safety applications while maintaining core energy focus. This evolution showcases his ability to transfer domain expertise across technical contexts.

As an educator, Dr. Gharavi leads the Energy Data Analytics and Advanced Machine Learning modules, leveraging industry experience from Halliburton and Baker Hughes to create practice-oriented curricula. His multidisciplinary background enables unique pedagogical approaches that connect petroleum engineering fundamentals with contemporary data science techniques.

Dr. Gharavi maintains active industry connections through his previous roles as Data Scientist at major energy firms, though current lab affiliations aren't specified in available materials. His research direction suggests growing emphasis on AI applications for energy transition challenges, particularly in system optimization and sustainability metrics.