Breno De Araujo
Applied Scientist at Amazon
Cambridge, Massachusetts, Canada
Summary
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Senior🎓
Top SchoolBreno De Araujo is an applied scientist with nine years of experience at the intersection of optimization, machine learning, and industrial applications, currently working at Amazon after a postdoctoral stint at MIT’s Center for Transportation & Logistics. He holds a PhD from TUM in Business Analytics and Intelligent Systems and was trained in the DFG AdONE program, bringing deep expertise in networked optimization and data-driven decision making. His career spans R&D roles in oil & gas and consulting engagements where he delivered anomaly detection, forecasting, and supply-chain analytics for enterprise clients. Breno combines academic rigor with production impact—moving models from theory to deployed systems—and has experience collaborating across management consulting and engineering teams. Notably, his background blends electronics and computer engineering foundations with advanced ML/optimization research, enabling a practical yet principled approach to hard industry problems. Based in Cambridge, MA and originally from Brazil/Canada, he bridges international research and industrial innovation.
9 years of coding experience
8 years of employment as a software developer
High School, High School at Colégio Santo InácioDoctor of Philosophy - PhD, Business Analytics and Intelligent Systems, Doctor of Philosophy - PhD, Business Analytics and Intelligent Systems at Technical University of MunichBachelor of Engineering (B.Eng.), Magna Cum Laude, Electronics and Computer Engineering, Bachelor of Engineering (B.Eng.), Magna Cum Laude, Electronics and Computer Engineering at Universidade Federal do Rio de JaneiroVisiting PhD Student, Visiting PhD Student at Massachusetts Institute of TechnologyPontifical Catholic University of Rio de Janeiro
German, English, Portuguese
Github Skills (3)
stochastic6
julia5
stochastic-processes4
Programming languages (2)
Github contributions (5)
BSAraujo/StochasticBlockModel.jl
Nov 2019 - Mar 2020
A Julia package for Stochastic Block Models
Contributions:36 commits, 8 PRs, 30 pushes in 4 months
julia-packagestochasticstochastic-processesjulia
vidalt/Optimal-SBM
Jan 2021 - Jan 2021
Contributions:3 commits, 2 pushes in 1 day
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