About me #
Researcher at SENAI Institute of Inovation /
Data Scientist
Master's Student at PPGEEL (UFSC)
Hey, I’m Rodrigo! An AI Researcher at the SENAI Institute of Inovation in Embedded Systems and an Electronic Engineer with a keen interest in the application of machine learning models in the industry. Currently pursuing a Master’s degree in Applied Machine Learning at UFSC, I am dedicated to enhancing industrial efficiency through data-driven solutions.
Over the past four years, I have honed my skills in Python programming and machine learning techniques. My expertise lies in developing innovative algorithms and implementing machine learning models for real-world applications. I thrive on the challenge of solving complex problems and leveraging cutting-edge technologies to drive advancements in the industry.
Experience #
March/2024 - Present #
Machine Learning Researcher
- Development of machine learning models in projects for different sector of the industry.
November/2021 - February/2024 #
Machine Learning Researcher
- Applied machine learning algorithms for predictive maintenance using real vibration data;
- Worked with state-of-the-art convolutional network models and a public bearing fault dataset;
- Conducted exploratory data analysis and data cleaning;
- Performed extraction, transformation, and loading (ETL) pipelines;
- Implemented machine learning models and optimized hyperparameters;
- Tracked experiments using MLOps tools.
February/2021 - October/2021 #
Machine Learning Engineer
- Developed and deployed a failure forecasting and classification model for HVAC-R monitoring systems;
- Trained machine learning models for demand and price forecasting for the automotive sector;
- Built data pipelines and machine learning model pipelines using Airflow;
- Monitoring of deployed models' performance;
- Data wrangling and exploration.
February/2020 - February/2021 #
Machine Learning Engineer Intern
- Developed and evaluated several ML models for stress corrosion cracking failures in the gas industry;
- Developed and deployed an anomaly detection model for HVAC-R monitoring systems;
- Performed data wrangling and exploration and helped with the model data ingestion by creating ETL pipelines.
Papers #
Diagnóstico de Falhas em Rolamentos usando Redes Convolucionais: Otimização da Representação de Sinais e uma Nova Metodologia de Avaliação
Rodrigo Kobashikawa Rosa, Vicente Knobel Borges, Danilo de Souza Braga, Danilo Silva
XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais-SBrT 2023
[link]Fault detection for rotating machinery based on vibration data using machine learning
Lucas de Toledo Barreto, Rodrigo Kobashikawa Rosa, Danilo Silva, Danilo Braga
[link]Conversão Texto-Fala para o Português Brasileiro Utilizando Tacotron 2 com Vocoder Griffin-Lim
Rodrigo Kobashikawa Rosa, Danilo Silva
XXXIX Simpósio Brasileiro de Telecomunicações e Processamento de Sinais-SBrT 2021
[link]
Contact #
You can reach me at rodrigokrosa@gmail.com