Using Business Processes in System Requirements Definition. - Elaine Alves de Carvalho; Tatiana Escovedo; Rubens N. Melo (SEW, 2009)

Combining Forecasts: A Genetic Programming Approach. - Adriano S. Koshiyama; Tatiana Escovedo; Douglas Mota Dias; Marley Maria Bernardes Rebuzzi Vellasco; Marco Aurélio Cavalcanti Pacheco (Int. J. Nat. Comput. Res., 2012)

GPF-CLASS: A Genetic Fuzzy model for classification. - Adriano S. Koshiyama; Tatiana Escovedo; Douglas Mota Dias; Marley M. B. R. Vellasco; Ricardo Tanscheit (IEEE Congress on Evolutionary Computation, 2013)

NEVE: A Neuro-Evolutionary Ensemble for Adaptive Learning. - Tatiana Escovedo; André Vargas Abs da Cruz; Marley Maria Bernardes Rebuzzi Vellasco; Adriano Soares Koshiyama (AIAI, 2013)

Using ensembles for adaptive learning: A comparative approach. - Tatiana Escovedo; André Vargas Abs da Cruz; Marley Maria Bernardes Rebuzzi Vellasco; Adriano S. Koshiyama (IJCNN, 2013)

Learning under Concept Drift using a Neuro-Evolutionary Ensemble. - Tatiana Escovedo; André Vargas Abs da Cruz; Marley Maria Bernardes Rebuzzi Vellasco; Adriano S. Koshiyama (Int. J. Comput. Intell. Appl., 2013)

GPFIS-Control: A fuzzy Genetic model for Control tasks. - Adriano S. Koshiyama; Tatiana Escovedo; Marley Maria Bernardes Rebuzzi Vellasco; Ricardo Tanscheit (FUZZ-IEEE, 2014)

NEVE++: A neuro-evolutionary unlimited ensemble for adaptive learning. - Tatiana Escovedo; André Vargas Abs da Cruz; Adriano S. Koshiyama; Rubens Nascimento Melo; Marley Maria Bernardes Rebuzzi Vellasco (IJCNN, 2014)

A2D2: A pre-event abrupt drift detection. - Tatiana Escovedo; Adriano S. Koshiyama; Marley Maria Bernardes Rebuzzi Vellasco; Rubens Nascimento Melo; André Vargas Abs da Cruz (IJCNN, 2015)

DetectA: abrupt concept drift detection in non-stationary environments. - Tatiana Escovedo; Adriano S. Koshiyama; André Vargas Abs da Cruz; Marley M. B. R. Vellasco (Appl. Soft Comput., 2018)

Neuroevolutionary learning in nonstationary environments. - Tatiana Escovedo; Adriano S. Koshiyama; André Vargas Abs da Cruz; Marley M. B. R. Vellasco (Appl. Intell., 2020)

Requirements Engineering for Machine Learning: A Systematic Mapping Study. - Hugo Villamizar; Tatiana Escovedo; Marcos Kalinowski (SEAA, 2021)

Clusters of Brazilian municipalities and the relationship with their fiscal management. - Marcio Afonso; Tatiana Escovedo (SBSI, 2021)

Machine Learning Applied to the INSS Benefit Request. - Ney Barchilon; Tatiana Escovedo (SBSI, 2021)

Construction of Mortality Tables using LSTM Neural Networks. - José Douglas Nascimento; Tatiana Escovedo (SBSI, 2021)

Materials: Requirements Engineering for Machine Learning: A Systematic Mapping Study. - Hugo Villamizar; Tatiana Escovedo; Marcos Kalinowski (Zenodo, 2021)

Machine Learning Aplicado à Predição da Obrigação do Investimento em P, D&I. - Flávia Bravo; Luciana Sousa; Tatiana Escovedo; Hélio Lopes 001; Marcos Kalinowski (SBBD, 2022)

Mineração de Processos Aplicada à Auditoria Interna na Marinha do Brasil. - Marcos Alexandre Castro; Nelson Souza Jr; Tatiana Escovedo; Hélio Lopes 001; Marcos Kalinowski (SBBD, 2022)

Padronização da Descrição de Produtos Comerciais utilizando NER. - Laércio Lucchesi; Tatiana Escovedo; Marcos Kalinowski (SBBD, 2022)

Predicting IMDb Rating of TV Series with Deep Learning: The Case of Arrow. - Anna Luiza Gomes; Getúlio Vianna; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2022)

Machine Learning Aplicado ao Resultado de Pedido de Concessão de Benefícios do INSS - Análise Ampliada. - Ney Barchilon; Tatiana Escovedo; Marcos Kalinowski (Braz. J. Inf. Syst., 2022)

Construção de Tábuas de Mortalidade com o uso de Redes Neurais LSTM Bidirectional para Predição das Probabilidades de Morte. - José Douglas Nascimento; Tatiana Escovedo; Marcos Kalinowski (Braz. J. Inf. Syst., 2022)

Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey. - Antonio Pedro Santos Alves; Marcos Kalinowski; Görkem Giray; Daniel Méndez 001; Niklas Lavesson; Kelly Azevedo; Hugo Villamizar; Tatiana Escovedo; Hélio Lopes 001; Stefan Biffl; Jürgen Musil; Michael Felderer; et al. (PROFES (1), 2023)

Training the Professionals that Industry Needs: The Digital Software Engineering Education Program at PUC-Rio. - Marcos Kalinowski; Tatiana Escovedo; Fernanda Pina; Adriana Vidal; Ariane Pereira Da Silva; Ricardo Ponsirenas; Daiana Garibaldi Da Rocha (SBES, 2023)

Optimizing Wireline Formation Testing in Oil Wells: A Data Science approach. - Marcelo Abbehusen; Rodolfo Rodrigues; Lucas Oliveira; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2023)

Machine Learning Applied to the Classification of Technical Inspection Recommendations Regarding the Trend to Increase Criticality. - André Amaral; André L. R. de Araújo; Bruno Tomazela; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2023)

Inference of Properties of a Natural Gas Processing Plant Through the Application of Machine Learning to Time Series. - Luciana Silva; Marcelo Cardoso Marques; Marcos Vinicius de Carvalho Gomes; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2023)

Machine Learning Applied to Open Government Data for the Detection of Improprieties in the Application of Public Resources. - Ramon Vaqueiro; Ana Vargas; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2023)

ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems. - Eduardo Zimelewicz; Marcos Kalinowski; Daniel Méndez 001; Görkem Giray; Antonio Pedro Santos Alves; Niklas Lavesson; Kelly Azevedo; Hugo Villamizar; Tatiana Escovedo; Hélio Lopes 001; Stefan Biffl; Juergen Musil; et al. (Zenodo, 2023)

Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding. - Raphael Cabral; Marcos Kalinowski; Maria Teresa Baldassarre; Hugo Villamizar; Tatiana Escovedo; Hélio Lopes 001 (CAIN, 2024)

Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil. - Antonio Pedro Santos Alves; Marcos Kalinowski; Daniel Méndez 001; Hugo Villamizar; Kelly Azevedo; Tatiana Escovedo; Hélio Lopes 001 (SBES, 2024)

Investigating Predicting Voluntary Resignation Program Participation with Machine Learning. - Ezequiel Mule Jorge; Lúcio Tales Barbieri; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2024)

Machine Learning Applied in the Construction of a Disability Retirement Entry Table of the General Social Security Regime (RGPS) of Brazil. - Gustavo Morais; Tatiana Escovedo; Marcos Kalinowski (SBSI, 2024)

ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems. - Eduardo Zimelewicz; Marcos Kalinowski; Daniel Méndez 001; Görkem Giray; Antonio Pedro Santos Alves; Niklas Lavesson; Kelly Azevedo; Hugo Villamizar; Tatiana Escovedo; Hélio Lopes 001; Stefan Biffl; Juergen Musil; et al. (SWQD, 2024)

Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil. - Antonio Pedro Santos Alves; Marcos Kalinowski; Daniel Méndez 001; Hugo Villamizar; Kelly Azevedo; Tatiana Escovedo; Hélio Lopes 001 (Zenodo, 2024)

Investigating Issues That Lead to Code Technical Debt in Machine Learning Systems. - Rodrigo Ximenes; Antonio Pedro Santos Alves; Tatiana Escovedo; Rodrigo O. Spínola; Marcos Kalinowski (CAIN, 2025)

Naming the Pain in machine learning-enabled systems engineering. - Marcos Kalinowski; Daniel Méndez 001; Görkem Giray; Antonio Pedro Santos Alves; Kelly Azevedo; Tatiana Escovedo; Hugo Villamizar; Hélio Lopes 001; Maria Teresa Baldassarre; Stefan Wagner 001; Stefan Biffl; Jürgen Musil; et al. (Inf. Softw. Technol., 2025)