Diego Mandelli
R&D Scientist at Idaho National Laboratory
Idaho Falls, Idaho, United States
Summary
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Senior🎓
Top SchoolDiego Mandelli is an R&D Scientist and nuclear engineer with 14+ years' experience at Idaho National Laboratory, specializing in probabilistic risk, reliability, and system health management through advanced data analytics and optimization. He develops mathematically rigorous, production-ready tools—authoring and managing open-source projects such as RAVEN and contributing to SR2ML and LOGOS—that fuse machine learning on numeric and textual data with model-based system engineering. His work spans dynamic probabilistic risk assessment, multi-unit nuclear system modeling, stochastic scheduling, and distributionally robust optimization, and has been used in both research and industry applications. An instructor on Bayesian inference for the U.S. NRC, he blends deep academic training (PhD, The Ohio State University) with practical code development and a strong interest in causal analytics as the future of reliability engineering.
14 years of coding experience
6 years of employment as a software developer
Laurea, Nuclear Engineering, Laurea, Nuclear Engineering at Politecnico di MilanoMaster of Science (MS), Nuclear Engineering, Master of Science (MS), Nuclear Engineering at The Ohio State University
English, Italian, French
Github Skills (19)
pattern-recognition10
raven10
reduction10
risk-analysis10
data-mining10
uncertainty-quantification10
validation10
hpc-applications10
model-optimization10
probabilistic10
uncertainty10
risk-assessment10
optimization10
stochastic9
discrete-optimization9
Programming languages (3)
Github contributions (5)
idaholab/DACKAR
Sep 2024 - Apr 2025
DACKAR is a software product designed to analyze equipment reliability data and provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind, and to predict their direct consequences.
Contributions:11 reviews, 3 PRs, 16 pushes in 6 months
idaholab/SR2ML
Nov 2019 - Nov 2022
Safety Risk Reliability Model Library
Contributions:68 reviews, 168 commits, 41 PRs in 3 years
reliability-modelsecurityriskrisk-assessmentrisk-analysis
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