Fossong Guilianno is a research enthusiast with over five years of applied experience in geology, environmental science, and artificial intelligence (AI). I have strong expertise in reservoir characterisation, geological field mapping, and the application of AI to solve geoscience and environmental problems. Fossong is always enthusiastic about leveraging his passion for teaching, learning, innovative research, and community service to advance knowledge in geoscience and contribute meaningfully to societal transformation and development. 

Research Interest

Artificial Intelligence in Geosciences || Reservoir Characterisation and Optimisation || Deep Learning and Transformer Models || Data-Driven Sustainability and Energy Transition Studies || Climate and Environmental Modelling || Carbon Capture, Utilisation, and Storage (CCUS). 

Experience

• Teach undergraduate students GST 107 & 108 (Communication in French I & II).
• Actively involved in building the Environmental Management Curriculum.
• Lead AI-driven research projects on carbon storage and environmental modelling.
• Develop machine-learning applications for emission forecasting and CCS suitability (https://github.com/FOSSONG/CCS-Suitability-Model).
• Facilitate workshops on ethical use of AI in research and scientific writing.

•     Applied advanced analytical tools and machine learning workflows to reservoir and environmental datasets.

  · Conducted petrophysical interpretations and wellbore analyses using Petrel and Techlog.

  ·   Performed GC and AAS laboratory analyses, ensuring QA/QC and environmental compliance.

  ·  Led development of digital tools (company website) to improve visibility and stakeholder engagement.

  ·  Conducted research on AI-based facies classification using TabTransformer models (https://github.com/FOSSONG/TabTransformer-model).

  ·   Investigated cognitive computing approaches for reservoir characterisation.

  ·  Designed workflows integrating well logs, seismic data, and core data for predictive modelling.