Oslo, Oslo, Norway
As the Academic Leader for the Green Transition at OsloMet, my vision is to position our university as a leading national and international catalyst for sustainability transformation. With a decade of technical experience in mechanical engineering, Industrial AI, and prognostic health management, I have built a career on understanding how complex systems function, fail, and endure. Today, I apply that same systems-thinking to our greatest societal challenge: the shift toward a just, low-emission, and circular economy. My mission is to consolidate interdisciplinary research ecosystems, secure high-impact funding, and empower the next generation of researchers to co-create sustainable solutions with industry and society
To strengthen research, education, and collaboration that support a socially just Green Transition globally.
• Teach courses such as Mechanical Design, Piping Design, Sustainable Design and Smart Manufacturing. • Teach Micro Courses on CNC Milling and Digital Twin Technologies Applied in Structural Health Monitoring. • Research Group Leader for Mechanics, Mechatronics and Materials Technology. • Project Manager for GrønnMet - Green Energy Lab. • Supervise BS and MS thesis. Co-Supervise PhD Thesis. • Perform research within prognostics, diagnostics, condition monitoring, risk/reliability engineering, and life estimation modeling.
• Teach Design related courses at Bachelors Level. Supervise BS and MS thesis. Co-Supervise PhD Thesis. • Perform research within prognostics, diagnostics, condition monitoring, risk/reliability engineering, and life estimation modeling.
• Responsible for Teaching Machine Learning. • Responsible for Teaching Probability and Statistics.
• Responsible for teaching course "Production and Manufacturing Engineering" at Bachelor’s level. Key Modules taught during the course (10 study points): Additive Manufacturing (3D Printing) Subtractive Manufacturing (Milling, Drilling, CNC) Welding Technology Metrology Industrial Robotics
• Responsible for evaluating Bachelor and Master Thesis. • Responsible for co-supervising Master and PhD Thesis.
PhD research title is "Probabilistic Fatigue and Fracture Degradation Assessment of Offshore Piping: Remaining Life Estimation, Risk Evaluation and Inspection Analysis". Research propensity is towards the field of: 1. Fatigue and Fracture Degradation Assessment 2. Reliability engineering 3. Asset Integrity 4. Risk-Based Inspection 5. Machine learning 6. Applied Statistics 7. Decision making under uncertainty 8. NDE and PoD 9. Life estimation modelling 10. Probabilistic inspection analysis
Primary responsibilities are: • Identify and initiate new research projects within pipeline and material department. • Actively contribute in various cross industry Joint Industry Projects (JIPs) such as Fatigue Assessment of Girth Weld, Damage Modeling of Composite Pipes. • Perform external condition assessment (free span and fatigue analysis) of the subsea pipelines. • Perform Failure analysis and Root cause analysis of offshore and onshore pipelines. • Apply Machine learning and Deep learning for creating surrogate models and digital twins for Structural Reliability Analysis (SRA) and integrity assessment of pipelines. • Use FEM and Bayesian Networks to build multi scale damage modeling of pipelines. • Estimate probability of failure and life extension possibility of pipelines undergoing fatigue and fracture degradation.
I worked in the Prognostics Center of Excellence (PCoE) at NASA Ames Research. The focus of the research during the stipulated period was: • Performing probabilistic fatigue life assessment of offshore piping using various probabilistic techniques (applied statistics, design of experiments, advanced Monte Carlo etc.) • Developing a surrogate model (using machine learning and deep learning) to predict the stress intensity factor (SIF) for various crack sizes and the loading conditions. • Used Python and ANSYS to develop mathematical models for risk-based inspection (RBI) of offshore piping during the research.