Navid Gholamian Kisomi

Areas of Work

Work areas

• Artificial Intelligence and Machine Learning
• Deep Reinforcement Learning
• AI in Healthcare and Medical Optimization
• Operations Research
• Sequential Decision-Making & Multi-Objective Optimization
• Transformer Architectures & Sequential Prediction
• Supply Chain Optimization

Teaching & Lab Instruction

• IBE400 Machine Learning (Lab & Practical Sessions) 
• IBE450 Artificial Intelligence (Lab & Practical Sessions)
• IBE710 Digitalization+AI (Lab & Practical Sessions)

Background

Education

• Ph.D. Candidate in AI in Healthcare Logistics, Molde University College (Nov 2024–present) 
• M.Sc. in Industrial Engineering 
• B.Sc. in Applied Mathematics

Previous positions

• Data Scientist
• Data Analyst 
• Product Manager
• Operations Manager

Research focus

My current Ph.D. research at Molde University College focuses on bridging Deep Reinforcement Learning, Transformer architectures, and Multi-Objective Optimization for sequential decision-making in healthcare logistics. Specifically, I develop novel mathematical and computational frameworks—such as structure-preserving policy optimization, bi-level geometric optimization under Riemannian manifolds, and hybrid evolutionary Bayesian learning networks—to address complex temporal dependencies, intrinsic physiological uncertainties, and multi-drug dosage scheduling in oncology. Before joining Molde University College, I spent over a decade in industry applying advanced AI, machine learning, and operations research techniques to solve complex business problems, predictive analytics, and supply chain optimizations.