I am passionate about machine learning and I’m always doing projects and researching. The possibility of having an idea that can help someone is what motivates me to work and learn about data science every day.
In my master’s degree I study ML-based unsupervised anomaly detection in time-series applied to optical fibers.
I’m also a Trainee at a startup where I develop computer vision solutions with YOLO, perform Front-end with Angular, Back-end tasks with C# and data analysis with SQL and Power BI.
Prevention and Loss Trainee to improve the management of the company’s motorcycle fleet
Jan 2023 - Mar 2023, Campinas, Brazil
AI research using bayesian neural networks
Internship to help and monitore the sales team to achieve their goals
Mar 2017 - Jun 2019, Campinas, Brazil
Extracurricular robotics project for student competitions
Aug 2017 - Jun 2019
Mar 2017 - Jun 2019
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2022-2023
Master's in Computer ScienceCGPA: 4 out of 4 |
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2017-2021
B.Sc. in Electrical EngineeringCGPA: 7.5 out of 10Extracurricular Activities
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2019-2020
Student Exchange ProgramTaken Courses
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Liceu de Artes e Ofícios de São Paulo2012-2014
Electronics technicianCGPA: 7.5 out of 10 |
Includes Generative AI Fundamentals, Big Data and Machine Learning Fundamentals and much more.
Start the UX Design Process and Build Dynamic User Interfaces (UI) for Websites.
Online course with a workload of 72 hours to develop dashboards with Power BI.
Online course with a workload of 26.5 hours.
Online course with a workload of 44 hours.
Online course with a workload of 22 hours.
Online course with a workload of 8 hours.
Online course with a workload of 8 hours.
Music social network.
A Football Simulator App developed with Flutter.
“Solidário” app a cashback system to help the community with donnations.
Prediction of a football league final season classification based on previous performances.
A Machine Learning containing a resume of all the most used techniques in the field.
Image detection with YOLO framework.
We propose an unsupervised ML approach to localize optical amplifier anomalies using input-output power time series. Tested on real optical network telemetry data, our method successfully identified a faulty amplifier.
We propose an SGD-based QoT estimation technique that operates on a network-wide scale by transferring gradients among neighboring wavelengths. Simulation results indicate effective and low-complexity QoT estimation using only transponder SNR telemetry.