Fabricio Torquato
Technical Data Scientist Manager
São Paulo, Brazil
Summary
👤
Senior🎓
Top SchoolFabricio Torquato is a Technical Data Scientist Manager with nine years of experience building scalable AI and software systems, currently leading data science and generative AI initiatives at CESAR. He combines hands-on expertise from embedded systems to web and mobile platforms with production-grade data engineering using Spark/PySpark and robust software architectures. As a published researcher and university professor, he teaches Computer Vision, AI and Big Data while coauthoring work on Brain-Computer Interfaces, space simulations and gamified learning—demonstrating a rare bridge between research and applied product development. His track record includes architecting a monitoring system handling over 300k daily requests and building person-tracking platforms for controlled environments. Known for technical leadership, mentoring and a focus on testable, scalable solutions, he also brings competitive programming pedigree and frequent contributions to academic and industry knowledge. Based in São Paulo, he aims to convert research insights into real business impact through innovative, production-ready AI solutions.
9 years of coding experience
8 years of employment as a software developer
Pós-graduação Lato Sensu - Especialização, Inteligência Artificial Aplicada (Machine Learning), Pós-graduação Lato Sensu - Especialização, Inteligência Artificial Aplicada (Machine Learning) at Facens | Faculdade de Engenharia de SorocabaPós-graduação Stricto Sensu - Mestrado Profissional, Computer Software Engineering, Pós-graduação Stricto Sensu - Mestrado Profissional, Computer Software Engineering at CESAR School
Github Skills (8)
recurrent8
robot7
robotics6
ros6
operating-system4
signal4
architectures3
neural-network2
Programming languages (2)
Github contributions (5)
fabriciotorquato/Gamificacao-do-Ensino-de-Linguagem-C
Sep 2017 - Jan 2022
Contributions:6 pushes, 3 branches in 4 years 4 months
fabriciotorquato/pyxavier
Oct 2019 - Jan 2022
This work evaluates different recurrent neural network architectures to control a virtual object on Robot Operating System (ROS) using electroencephalogram for signal acquisition. For the interface controls, voluntary hand motor actions were used, each hand for a different direction.
Contributions:11 commits, 4 PRs, 10 pushes in 2 years 3 months
signalrobot-operatingroboticsrobot-operating-systemrecurrent-neural-network
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