Diego Mandelli

About Diego Mandelli

I am a nuclear engineer with strong mathematical, statistical, and system modeling…

Experience

1. R & d Scientist

   Idaho National Laboratory

   Aug 2011 — Present

   I am a research scientist who is called to develop and demonstrate computational solutions that are mathematically/statistically robust. Leader of the “Cost and Risk Categorization Applications” work package. Developed an enterprise risk-analysis framework which integrates economic and reliability in a single analysis platforms: · Data analytics methods for equipment reliability data that integrate numeric and textual data · Development of economic risk models for nuclear power plants · Innovative margin based methods reliability models for system reliability/health analysis · Development of reliability models and methods released in the open source library SR2ML (https://github.com/idaholab/SR2ML) · Discrete optimization methods for job scheduling and project prioritization in several forms: deterministic, stochastic and distributionally robust · Development of optimization models released in the open source library LOGOS (https://github.com/idaholab/LOGOS) Leader of the “Methods Development” work package. Performed classical and dynamic Probabilistic Risk Assessment (PRA) of nuclear systems: · Data mining methods for analysis of large amounts of accident scenarios · Adaptive sampling methods based on machine-learning algorithms · Several dynamic PRA analyses performed including multi-unit modeling (first of its kind) Project manager and developer of the RAVEN open source code (github.com/idaholab/raven). · Data mining methods for analysis of large amounts of accident scenarios · Risk importance measures for dynamic PRA · Model-based optimization methods through genetic algorithms · Multi-objective optimization methods based on Pareto frontier and evolutionary methods Instructor at the U.S. Nuclear Regulatory Commission (NRC) for the “Bayesian Inference in Risk Assessment” and “Bayesian Inference in Risk Assessment - Advanced Topics” courses. Topics covered: probability theory, computational Bayesian inference, uncertainty propagation on reliability models

Skills

• Data Mining
• Espresso
• Data Visualization
• High Performance Computing
• Risk Analysis
• Fortran
• Probabilistic Models
• Risk Assessment
• Mathematical Modeling
• Adaptive Sampling
• Mcnp
• Safety Engineering
• Stochastic Modeling
• Matlab
• C++
• Nuclear Engineering
• System Simulation
• Modeling
• Latex
• Pattern Recognition
• Python
• Systems Engineering
• Nuclear
• Numerical Analysis
• Safety Analysis
• Physics