CV

Education

Post-Doc at Recod.ai, University of Campinas
October 2019 – June 2025
About: Development of state-of-the-art methods for forecasting in temporal series for petroleum data, considering information from different sensors and types of wells, spurious data, and limitations from the platform. Developed approaches ranging from simple recurrent neural networks to transformers. This position was part of a project together with Shell Brazil.
Supervisor: Prof. Anderson de Rezende Rocha, Ph.D.
Ph.D in Computer Science at University of Campinas
August 2014 – May 2019
Thesis title: Learning Graph-based Representations and Matching in Classification Tasks
About: Development of a new method to represent multimedia data using a graph representation combining the different media. Also developed a new method of matching graphs using a bag-of-graphs approach in different classification tasks.
Advisor: Prof. Ricardo da Silva Torres, Ph.D.
Master in Computer Science at University of Campinas
February 2012 – August 2014
Dissertation Title: A unified framework for design, deployment, execution, and recommendation of machine learning experiments
About: Developed a framework for the design, deployment, and execution of machine-learning experiments. It allowed the user to create a plugin for a new method (feature extraction, classification, normalization, etc.) and use it in the framework. It also allowed the user to perform a recommendation on previous experiments created in the framework.
Advisor: Prof. Ricardo da Silva Torres, Ph.D.; Co-advisor: Prof. Anderson de Rezende Rocha, Ph.D.
Bachelor in Computer Science at Federal University of Juiz de Fora
March 2008 – December 2011
Advisor: Prof. Raul Fonseca Neto, Ph.D.

Work experience

AI Specialist (January 2025 – September 2025)
- Psyche Aerospace/Turing
- Developed drone and satellite image classification models, applying advanced image processing techniques to extract key features (e.g., YOLO, Delineate-Anything, GeoPandas).
- Delivered data science insights from Big Data generated by agricultural machines, optimizing operational efficiency and resource allocation.
- Engineered and deployed over 80 Artificial Intelligence Agents (AIAs) for Sales, Service, Finance, and Backoffice sectors, leading a 4-person development team.
Research I (July 2024 – December 2024)
- Recod.ai, University of Campinas
- Led the forecasting research team, directing the proposal, testing, and evaluation of data-driven forecasting methods for the oil and gas sector using Recurrent Neural Networks and Transformer models.
- Authored and co-authored 14 publications during Post-Doc and Researcher roles.
- Proposed a novel forecasting approach by expanding the Temporal Fusion Transformer model to incorporate the probability of well closure over a one-year horizon, based on historical daily closure distributions.
Consultant (February 2023 – July 2023)
- Inmetrics
- Conducted algorithm research to develop classification models for understanding and categorizing online product text data.
- Designed and implemented various Natural Language Processing (NLP) approaches to accurately classify online customer reviews, improving sentiment and topic analysis.

Leadership & Activities

Recod.ai: Coordinated the Forecasting Team at the Shell/Recod.ai project.
Psyche Aerospace/Turing: Managed and led the Agro Image Team and subsequently the Agents Team at the company, overseeing the development and deployment of more than 13 AI-driven agricultural vision models and more than 80 AI Agents. Also communicated directly with 3 clients to elicit and elaborate on their technical necessities, translating requirements into actionable project plans and presenting insights.
Boards: Contributed expertise to 12 Master and Ph.D. examination boards.
Advisory: Co-advisor of one Ph.D. candidate.

Skills & Interests

Technical: Python, C, TensorFlow, PyTorch, n8n, QGIS
Language: Portuguese (native), English (full professional proficiency), French (elementary)
Laboratory: Git, Docker
Interests: image processing, supervised learning, feature engineering, content-based image retrieval, graphs, pattern recognition, regression, time series, deep learning, machine learning, artificial intelligence, forecasting, text classification, agents, prompt engineering

Publications

Few-shot and continuous online learning for forecasting in the energy industry

Gabriel Cirac, Vinicius Eduardo Botechia, Denis José Schiozer, Víctor Martínez, Rafael de Oliveira Werneck, and Anderson Rocha. Few-shot and continuous online learning for forecasting in the energy industry. Energy, 336:138470, 2025.

Rock-type classification: A (critical) machine-learning perspective

Pedro Ribeiro Mendes, Soroor Salavati, Oscar Linares, Maiara Moreira Gonçalves, Marcelo Ferreira Zampieri, Vitor Hugo de Sousa Ferreira, Manuel Castro, Rafael de Oliveira Werneck, Renato Moura, Elayne Morais, Ahmed Esmin, Leopoldo Lusquino, Denis José Schiozer, Alexandre Ferreira, Alessandra Davólio, and Anderson Rocha. Rock-type classification: A (critical) machine-learning perspective. Computers Geosciences, 193:105730, 2024.

A multi-modal approach for mixed-frequency time series forecasting

Leopoldo Lusquino Filho, Rafael de Oliveira Werneck, Manuel Castro, Pedro Ribeiro Mendes Júnior, Augusto Lustosa, Marcelo Zampieri, Oscar Linares, Renato Moura, Elayne Morais, Murilo Amaral, Soroor Salavati, Ashish Loomba, Ahmed Esmin, Maiara Gonçalves, Denis José Schiozer, Alexandre Ferreira, Alessandra Davólio, and Anderson Rocha. A multi-modal approach for mixed-frequency time series forecasting. Neural Computing and Applications, 36(34):21581–21605, 2024.

Watch the reservoir! Improving short-term production forecast through transformers

R. Werneck, L. A. Lusquino Filho, A. Lustosa, A. Loomba, M. M. Gonçalves, A. Esmin, S. Salavati, E. Morais, P. Ribeiro Mendes Junior, M. Zampieri, M. Amaral, O. C. Linares, M. Castro, R. Moura, D. J. Schiozer, A. Mello Ferreira, A. Davolio, and A. Rocha. Watch the reservoir! Improving short-term production forecast through transformers. volume SPE Europe Energy Conference and Exhibition of SPE Europec featured at EAGE Conference and Exhibition, page D031S016R002, 06 2024.

Enhancing Short-Term Production Forecast in Oil Fields: Integrating Data-Driven and Model-Based Approaches to Reduce Uncertainty

M. M. Gonçalves, R. Werneck, M. Castro, M. Amaral, P. Ribeiro Mendes, L. A. Lusquino Filho, A. Esmin, R. Moura, E. Morais, O. C. Linares, A. Lustosa, S. Salavati, D. J. Schiozer, A. Mello Ferreira, A. Rocha, and A. Davolio. Enhancing short-term production forecast in oil fields: Integrating data-driven and model-based approaches to reduce uncertainty. volume SPE Europe Energy Conference and Exhibition of SPE Europec featured at EAGE Conference and Exhibition, page D031S019R002, 06 2024.

Time series causal relationships discovery through feature importance and ensemble models

Manuel Castro, Pedro Ribeiro Mendes Júnior, Aurea Soriano-Vargas, Rafael de Oliveira Werneck, Maiara Moreira Gonçalves, Leopoldo Lusquino Filho, Renato Moura, Marcelo Zampieri, Oscar Linares, Vitor Ferreira, Alexandre Ferreira, Alessandra Davólio, Denis Schiozer, and Anderson Rocha. Time series causal relationships discovery through feature importance and ensemble models. Scientific Reports, 13(1):11402, 2023.

A New Hybrid Data-Driven and Model-Based Methodology for Improved Short-Term Production Forecasting

Vitor Hugo de Sousa Ferreira, Manuel Castro, Renato Moura, Rafael de Oliveira Werneck, Marcelo Ferreira Zampieri, Maiara Moreira Gonçalves, Oscar Linares, Soroor Salavati, Leopoldo Andre Dutra Lusquino Filho, Pedro Ribeiro Mendes Júnior, Alexandre Mello Ferreira, Alessandra Davolio, Denis José Schiozer, and Anderson Rocha. A new hybrid data-driven and model-based methodology for improved short-term production forecasting. volume Offshore Technology Conference of OTC Offshore Technology Conference, page D041S049R007, 05 2023.

Oil production and pressure multimodal forecasting integrating high-frequency production data

Leopoldo André Dutra Lusquino Filho, Rafael de Oliveira Werneck, Pedro Ribeiro Mendes Júnior, Manuel Castro, Eduardo dos Santos Pereira, Renato Moura, Vítor Hugo de Sousa Ferreira, Alexandre Mello Ferreira, Alessandra Davolio Gomes, and Anderson de Rezende Rocha. Oil production and pressure multimodal forecasting integrating high-frequency production data. In IBP, editor, Rio Oil & Gas 2022: Sessões Técnicas Digitais e Presenciais | Technical Sessions (Video Presentations + Technical Papers in PDF), number 308, Rio de Janeiro | Brasil, September 2022.

Detecção de anomalias em dados de produção usando técnicas de aprendizado de máquina

Aurea Rossy Soriano Vargas, Rafael de Oliveira Werneck, Maiara Moreira Gonçalves, Eduardo dos Santos Pereira, Leopoldo André Dutra Lusquino Filho, Soroor Salavati, M. Manzur Hossain, Alexandre Mello Ferreira, Alessandra Davolio Gomes, Denis José Schiozer, and Anderson de Rezende Rocha. Detecção de anomalias em dados de produção usando técnicas de aprendizado de máquina. In IBP, editor, Rio Oil & Gas 2022: Sessões Técnicas Digitais e Presenciais | Technical Sessions (Video Presentations + Technical Papers in PDF), number 298, Rio de Janeiro | Brasil, September 2022.

Data-driven deep-learning forecasting for oil production and pressure

Rafael de Oliveira Werneck, Raphael Prates, Renato Moura, Maiara Moreira Gonçalves, Manuel Castro, Aurea Soriano-Vargas, Pedro Ribeiro Mendes Júnior, M. Manzur Hossain, Marcelo Ferreira Zampieri, Alexandre Ferreira, Alessandra Davólio, Denis Schiozer, and Anderson Rocha. Data-driven deep-learning forecasting for oil production and pressure. Journal of Petroleum Science and Engineering, 210:109937, 2022.

A visual analytics approach to anomaly detection in hydrocarbon reservoir time series data

Aurea Soriano-Vargas, Rafael Werneck, Renato Moura, Pedro Mendes Júnior, Raphael Prates, Manuel Castro, Maiara Gonçalves, Manzur Hossain, Marcelo Zampieri, Alexandre Ferreira, Alessandra Davólio, Bernd Hamann, Denis José Schiozer, and Anderson Rocha. A visual analytics approach to anomaly detection in hydrocarbon reservoir time series data. Journal of Petroleum Science and Engineering, 206:108988, 2021.

Learning cost function for graph classification with open-set methods

Rafael Werneck, Romain Raveaux, Salvatore Tabbone, and Ricardo da Silva Torres. Learning cost function for graph classification with open-set methods. Pattern Recognition Letters, 128:8 – 15, 2019.

Graph-Based Early-Fusion for Flood Detection

R. Werneck, Í. C. Dourado, S. G. Fadel, S. Tabbone, and R. da S. Torres. Graph-Based Early-Fusion for Flood Detection. In IEEE International Conference on Image Processing, pages 1048–1052, 2018.

Exploiting ConvNet Diversity for Flooding Identification

Keiller Nogueira, Samuel G. Fadel, Ícaro C. Dourado, Rafael de Oliveira Werneck, Javier A. V. Muñoz, Otávio A. B. Penatti, Rodrigo Tripodi Calumby, Lin Tzy Li, Jefersson A. dos Santos, and Ricardo da Silva Torres. Exploiting convnet diversity for flooding identification. IEEE Geosci. Remote Sensing Lett., 15(9):1446–1450, 2018.

Learning Cost Functions for Graph Matching

Rafael Werneck, R. Raveaux, S. Tabbone, and R. da S. Torres. Learning Cost Functions for Graph Matching. In Xiao Bai, Edwin R. Hancock, Tin Kam Ho, Richard C. Wilson, Battista Biggio, and Antonio Robles-Kelly, editors, Structural, Syntactic, and Statistical Pattern Recognition, pages 345–354, Cham, 2018. Springer International Publishing.

Graph-based bag-of-words for classification

F. B. Silva, R. de O. Werneck, S. Goldenstein, S. Tabbone, and R. da S. Torres. Graph-based bag-of-words for classification. Pattern Recognition, 74(Supplement C):266 – 285, February 2018.

Kuaa: A unified framework for design, deployment, execution, and recommendation of machine learning experiments

Rafael Werneck, Waldir Rodrigues de Almeida, Bernardo Vecchia Stein, Daniel Vatanabe Pazinato, Pedro Ribeiro Mendes Júnior, Otávio Augusto Bizetto Penatti, Anderson Rocha, and Ricardo da Silva Torres. Kuaa: A unified framework for design, deployment, execution, and recommendation of machine learning experiments. Future Generation Computer Systems, 2018.

Data-Driven Flood Detection using Neural Networks

K. Nogueira, S. G. Fadel, Í. C. Dourado, R. de O. Werneck, J. A.V. Muñoz, O. A.B. Penatti, R. T. Calumby, L. T. Li, J. A. dos Santos, and R. da S. Torres. Data-Driven Flood Detection using Neural Networks. In Working Notes Proc. MediaEval Workshop, 2017.

Nearest neighbors distance ratio open-set classifier

P. R. Mendes Júnior, R. M. de Souza, R. de O. Werneck, B. V. Stein, D. V. Pazinato, W. R. de Almeida, O. A. B. Penatti, R. da S. Torres, and A. Rocha. Nearest neighbors distance ratio open-set classifier. Machine Learning, 106(3):359–386, 3 2017.

Where is my puppy? Retrieving lost dogs by facial features

Thierry Pinheiro Moreira, Mauricio Lisboa Perez, Rafael de Oliveira Werneck, and Eduardo Valle. Where is my puppy? retrieving lost dogs by facial features. Multimedia Tools and Applications, 76(14):15325–15340, 2017.

Pixel-Level Tissue Classification for Ultrasound Images

D. V. Pazinato, B. V. Stein, W. R. de Almeida, R. de O. Werneck, P. R. M. JÃžnior, O. A. B. Penatti, R. d. S. Torres, F. H. Menezes, and A. Rocha. Pixel-level tissue classification for ultrasound images. IEEE Journal of Biomedical and Health Informatics, 20(1):256–267, 1 2016.

Mid-level image representations for real-time heart view plane classification of echocardiograms

Otávio A.B. Penatti, Rafael de O. Werneck, Waldir R. de Almeida, Bernardo V. Stein, Daniel V. Pazinato, Pedro R. Mendes Júnior, Ricardo da S. Torres, and Anderson Rocha. Mid-level image representations for real-time heart view plane classification of echocardiograms. Computers in Biology and Medicine, 66:66 – 81, 2015.

Rafael de Oliveira Werneck

CV