Mohamed Zinelabidine Doghmane is a renowned researcher in petroleum engineering who currently serves as a part-time graduate research assistant at Texas Tech University (TTU). His interdisciplinary educational background in petroleum engineering, geophysics, and applied automation gives him a solid foundation upon which he plans to spearhead carbon-free and carbon-negative technologies in geologic hydrogen production and CO₂ sequestration. He holds a Ph.D. in Applied Automation and Signal Processing and is working toward a second Ph.D. in Petroleum Engineering at TTU to revolutionize petroleum engineering processes for natural hydrogen exploration. His research integrates cutting-edge machine learning architectures with geomechanical reservoir engineering to enhance prediction models in tight reservoirs. Prior to TTU, he was employed as a geophysicist at Sonatrach, the largest oil and gas company in Algeria, where he managed logging and seismic activities for over ten years. He also held an appointment as an assistant professor at the University of Boumerdes, where he instructed in optimization, advanced signal processing, and applications of machine learning in petroleum engineering. He has made many research contributions, including several patents in drill bit wear classification and seismic data processing, and published multiple peer-reviewed articles in artificial intelligence-based reservoir evaluation. His more recent work focuses on hydrogen-bearing reservoirs.