About

Ph.D. researcher in Computer Science with extensive experience in machine learning, forecasting, and graph-based methods. Led research projects in academic and applied settings, published multiple papers and patents, and contributed to advancements in time series analysis and AI. Skilled in Python and deep learning frameworks, with a strong record of collaboration, mentoring, and delivering innovative solutions.

5 Most Recent Publications

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

Published in Energy, 2025

Forecasting in the energy sector is critical for planning and efficiency, but existing methods require extensive historical data and struggle with changing conditions. This work presents a few-shot forecasting method for energy time series prediction under nonstationary conditions and data scarcity. The solution “plugs into” any existing regressor, combining ideas to create a flexible data-efficient tool.

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

Published in Computers & Geosciences, 2024

We investigate machine-learning techniques for rock-type classification. A throughout literature review (considering the machine-learning technique, number of classes, rock types, and image types) presents a diversity of datasets employed and a wide range of classification results as well as multiple problem formulations. Throughout the discussion of the literature, we highlight some common machine-learning pitfalls and criticize the decisions taken by some authors on the problem formulation.

Recommended citation: 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.
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A multi-modal approach for mixed-frequency time series forecasting

Published in Neural Computing and Applications, 2024

This study proposes a novel multimodal approach for mixed-frequency time series forecasting in the oil industry, enabling the use of high-frequency (HF) data in their original frequency. We specifically address the challenge of integrating HF data streams, such as pressure and temperature measurements, with daily time series without introducing noise.

Recommended citation: 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.
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Watch the reservoir! Improving short-term production forecast through transformers

Published in SPE Europe Energy Conference and Exhibition, 2024

Data-driven methodologies have been used in reservoir management and production forecasting, particularly demonstrating remarkable efficacy in short-term oil production forecasts. However, there is space to improve its prediction, especially in tackling the complexities of challenging reservoirs, such as the heterogeneous carbonate reservoirs from Brazilian Pre-salt fields. In this paper, we propose a new strategy to improve short-term forecasting for oil production through attention mechanisms that boost state-of-the-art methods.

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

Published in SPE Europe Energy Conference and Exhibition, 2024

Reservoir simulation models are usually applied to optimize oil field production across its life cycle but face challenges in short-term forecasting. Data-driven techniques (DD) show promise for short-term predictions but lack reliability over extended periods. This study introduces a Hybrid Methodology (HyM) combining the optimal features of model-based (MB) with DD approaches to select the best simulation models to make short-term decisions, effectively reducing uncertainty in short-term production forecasts for a real field.

Recommended citation: 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.
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