Leonardo Bastos is an Associate Professor and industrial engineer with a PhD who applies machine learning, causal inference, and operations research to improve decision-making in healthcare and oil & gas. He directs the NOIS lab at PUC‑Rio and combines academic leadership with hands‑on consulting at Tecgraf, delivering data-driven products and generative AI solutions for complex operational problems. His research portfolio spans predictive modeling, performance benchmarking, and evaluation of health systems—work supported by grants from institutions like the Bill & Melinda Gates Foundation and Pfizer. He contributes to international collaborations such as ISARIC at the University of Oxford and has led projects detecting epidemic outbreaks and improving intensive care analytics for Brazil’s SUS. Known for translating messy data into actionable knowledge, he brings an uncommon blend of field experience, industrial systems background, and applied AI expertise to bridge research and real-world impact.
8 years of coding experience
6 years of employment as a software developer
Bachelor's Degree, Industrial Engineering, Bachelor's Degree, Industrial Engineering at Universidade do Estado do Pará
Pontifical Catholic University of Rio de Janeiro
PhD Exchange program, PhD Exchange program at University of Amsterdam
Exchange Study Program, Industrial Engineering, Exchange Study Program, Industrial Engineering at University of Windsor
Data for the paper "App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning" This work is in progress and under review.
Contributions:8 pushes, 1 branch in 4 years 1 month
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