🔬 Currently pursuing my PhD at the Vibroacoustic Laboratory (Unicamp), with a research focus on leveraging advanced data science and machine learning techniques to address complex engineering challenges, particularly in the domains of vibration and acoustic.
🌱 Continuously expanding my expertise in Machine Learning, with focus on:
- Building models with Scikit-learn, TensorFlow, and PyTorch;
- Data manipulation and analysis using Pandas, Numpy, and SciPy;
- Feature engineering, hyperparameter tuning (GridSearch/RandomSearch);
- Cross-validation and model evaluation;
- MLOps for deployment and monitoring of models.
- Libraries/Frameworks: Scikit-learn, TensorFlow, Keras, PyTorch
- Data Manipulation: Pandas, Numpy, SciPy
- Model Evaluation: Cross-validation, GridSearch, RandomSearch
- MLOps/Deployment: Docker, MLFlow, FastAPI
- Deep Learning: Neural Networks (CNNs, RNNs)
- NLP: NLTK, SpaCy, Hugging Face Transformers
- Data Pipelines: Understanding ETL (Extract, Transform, Load) processes and automation using tools such as Apache Airflow and Bash scripting.
- Data Warehousing: Basic knowledge of designing and populating Data Warehouses using platforms like Amazon Redshift and Google BigQuery.
- Big Data Technologies: Experience with Apache Hadoop and Spark for handling large-scale datasets and distributed computing.
- Databases: Working with relational databases like MySQL and PostgreSQL, as well as NoSQL databases such as MongoDB and Cassandra.
- Cloud Computing: Basic concepts in cloud platforms like AWS and Google Cloud Platform, with a focus on deploying machine learning models and managing big data.
- Languages: Python, Bash, SQL, Matlab
- Data Science: Scikit-learn, TensorFlow, Keras, PyTorch, Numpy, Pandas, Statsmodels
- Visualization: Matplotlib, Seaborn, Plotly, Dash
- Version Control: Git, GitHub, Docker
- Cloud Platforms: AWS, GCP
🎸 Fun fact: I’m passionate about music and play both acoustic and electric guitar!