DATA SCIENCE • MACHINE LEARNING • AERONAUTICS • CYBER
GABRIEL CAVALHERI
Data Science & ML · Physics · Aeronautics · Cyber
Turning raw data, physics and curiosity into working systems — from aircraft reliability and engine prognostics to ML pipelines, with cybersecurity next on the roadmap.
/* a bit about me */
WHO'S BEHIND THE CODE.
I'm a physicist by training (UFSCar), currently pursuing an MBA in Artificial Intelligence & Big Data at USP.
During my undergrad years I developed a passion for turning raw data into insights and working systems — which led me from academic research in applied acoustics into the world of data.
Lately I've been pointing that physics-plus-ML toolkit at aeronautics — fleet reliability, predictive maintenance and engine prognostics with physics-informed neural networks. Cybersecurity is the next frontier on my roadmap.
I like challenges, hacking, data, games, music and control — these things inspire me to build and test new things.
/* projects I've built */
DATA, MODELS AND SYSTEMS THAT SOLVE REAL PROBLEMS.
Each project below is a different attempt to answer a real question — with data, a model or a pipeline.
No projects with that tag yet — but it's coming.
/* stack I use */
THE TOOLS BEHIND THE MODELS.
From languages to ML frameworks, data infrastructure and reliability engineering — what I use day to day to explore, train and ship.
LANGUAGES & CORE
MACHINE LEARNING & AI
DATA & INFRA
AERONAUTICS & RELIABILITY
/* journey */
MY JOURNEY SO FAR.
Cybersecurity
Next on the roadmap: bringing the same data-driven, ML-first approach to security — building on my intrusion-detection work toward threat detection and defensive tooling.
Aeronautics — Reliability & Prognostics
Applying machine learning and physics-informed models to aviation: fleet reliability analytics, preventive-maintenance optimization, engine fault detection and turbofan Remaining Useful Life prediction.
MBA in Artificial Intelligence & Big Data — USP
Deepening machine learning, big data engineering and applied AI — my thesis applies neuroevolution to Physics-Informed Neural Networks.
From Physics to Data
Research in applied acoustics during my undergrad sparked a passion for turning raw signals and data into insight — the bridge from physics into data science.
Physics — UFSCar
Undergraduate degree in Physics, building the foundation in math, modeling and rigorous problem solving that still shapes how I approach every project.
Want the full resume?
/* get in touch */
LET'S WORK TOGETHER?
Have a project, a dataset or a hard problem in mind? Reach out — I'll get back to you as soon as I can.