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Dr. Daniel Leite
Professor
Systems Engineering Division
Aeronautics Institute of Technology (ITA), Brazil
E-mail: daniel.leite@gp.ita.br; danfl7@gmail.com
About
I am a Professor in the Systems Engineering Division at the Aeronautics Institute of Technology (ITA), Brazil. Previously, I was a Senior Researcher in the Department of Computer Science at Paderborn University, Germany (2023–2026), and held faculty positions at Universidad Adolfo Ibáñez, Chile, the Federal University of Lavras, and the Federal University of Minas Gerais, Brazil. I received my PhD from the University of Campinas (UNICAMP), Brazil, in 2012 and conducted postdoctoral research at the University of Ljubljana, Slovenia, and the Federal University of Minas Gerais, Brazil.
My research lies at the intersection of machine learning, intelligent systems, and control, with particular emphasis on incremental and continual learning, nonstationary data streams, concept drift, computer vision, explainable AI, and adaptive models. I am particularly interested in learning systems that can adapt their structure and knowledge over time while remaining interpretable under changing environments.
Areas
Incremental and continual machine learning
Nonstationary data streams and concept drift
Computer vision and multimodal learning
Explainable and human-centered AI
Adaptive and interpretable models
Data-driven modeling and control
Selected Distinctions
2026 — Best Theory Paper Award, CONTROLO
2026 — Best Paper Award Shortlist, IEEE EAIS
2023 — Stanford/Elsevier Top 2% Scientists, AI & Image Processing
2017 — NAFIPS Early Career Award
2017 — IEEE CIS Outstanding PhD Dissertation Award
2015 — NAFIPS Best PhD Thesis Award
2014 — Best PhD Thesis in Artificial Intelligence, Brazilian Computer Society
Profiles: Google Scholar · Scopus · Web of Science · ResearchGate · ORCID · Lattes · LinkedIn
Full CV
CV 2026 - Daniel Furtado Leite.pdfSelected Publications
Selected Publications
Incremental and Continual Machine Learning
Incremental and Continual Machine Learning
[36] Leite, D. (2026). (Submitted) Granular Incremental Learning for Uncertain Data Streams: A Unifying Conceptual and Methodological Overview. 58p.
[35] (Submitted) Silva, A., Tavares, E., Moita, G., Leite, D. (2026). I-RFC: Regularized Incremental Fuzzy Classifier with Online Feature Selection and Outlier Sensitivity for Evolving Data Streams. 35p.
[34] Leite, D., Andonovski, G., Škrjanc, I., Gomide, F. (2020). Optimal Rule-Based Granular Systems From Data Streams. IEEE Transactions on Fuzzy Systems, 28(3), 583–596. doi: 10.1109/TFUZZ.2019.2911493
[33] Garcia, C., Leite, D., Škrjanc, I. (2019). Incremental Missing-Data Imputation for Evolving Fuzzy Granular Prediction. IEEE Transactions on Fuzzy Systems, 28(10), 2348–2362. doi: 10.1109/TFUZZ.2019.2935688
[32] Leite, D., Costa, P., Gomide, F. (2013). Evolving Granular Neural Networks from Fuzzy Data Streams. Neural Networks, 38, 1–16. doi: 10.1016/j.neunet.2012.10.006
[31] Leite, D., Costa, P., Gomide, F. (2010). Evolving Granular Neural Network for Semi-Supervised Data Stream Classification. International Joint Conference on Neural Networks (IJCNN), Barcelona, 8p. doi: 10.1109/IJCNN.2010.5596303
Nonstationary Data Streams and Concept Drift
Nonstationary Data Streams and Concept Drift
[30] Leite, D., Gomide, F. (2026). Learning from Nonstationary Data Streams via Incremental Level-Set Modeling (e-LSM). IEEE International Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS'26), Pisa, Italy, 8p.
[29] Leite, D., Škrjanc, I., Gomide, F. (2020). An Overview on Evolving Systems and Learning from Stream Data. Evolving Systems, 11, 181–198. doi: 10.1007/s12530-020-09334-5
[28] Garcia, C., Esmin, A., Leite, D., Škrjanc, I. (2019). Evolvable Fuzzy Systems from Data Streams with Missing Values: With Application to Temporal Pattern Recognition and Cryptocurrency Prediction. Pattern Recognition Letters, 128, 278–282. doi: 10.1016/j.patrec.2019.09.012
[27] Leite, D., Costa, P., Gomide, F. (2012). Interval Approach for Evolving Granular Systems Modeling. In: Learning in Non-Stationary Environments, Springer, 271–300. doi: 10.1007/978-1-4419-8020-5_11
[26] Leite, D., Gomide, F., Ballini, R., Costa, P. (2011). Fuzzy Granular Evolving Modeling for Time Series Prediction. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Taipei, 2794–2801. doi: 10.1109/FUZZY.2011.6007452
[25] Leite, D., Costa Jr., P., Gomide, F. (2010). Granular Approach for Evolving System Modeling. In: Computational Intelligence for Knowledge-Based Systems Design, Lecture Notes in Computer Science, 6178, Springer. doi: 10.1007/978-3-642-14049-5_35
Computer Vision and Multimodal Learning
Computer Vision and Multimodal Learning
[24] Leite, D. (2026). Task-Free Continual Learning with Expansion-based Granular CNN: Gradual Partitioning of Manifold in Image Stream Classification. Neurocomputing, 671, 132665, 21p. doi: 10.1016/j.neucom.2026.132665
[23] Leite, D., Sharma, A., Demir, C., Ngomo, A.-C. (2024). Interpretability Index Based on Balanced Volumes for Transparent Models and Agnostic Explainers. IEEE World Congress on Computational Intelligence (WCCI), Yokohama, Japan, 10p. doi: 10.1109/FUZZ-IEEE60900.2024.10611937
[22] Leite, D., Škrjanc, I., Blažič, S., Zdešar, A., Gomide, F. (2023). Interval Incremental Learning of Interval Data Streams and Application to Vehicle Tracking. Information Sciences, 630, 1–22. doi: 10.1016/j.ins.2023.02.027
Explainable and Human-Centered AI
Explainable and Human-Centered AI
[21] Sharma, A., Leite, D., Demir, C., Ngomo, A.-C. (2024). Trading-Off Interpretability and Accuracy in Medical Applications: A Study toward Optimal Explainability of Hoeffding Trees. IEEE World Congress on Computational Intelligence (WCCI), Yokohama, Japan, 10p. doi: 10.1109/FUZZ-IEEE60900.2024.10611982
[20] Decker, L., Leite, D., Bonacorsi, D. (2022). Explainable Log Parsing and Online Interval Granular Classification from Streams of Words. IEEE World Congress on Computational Intelligence, Padua, Italy, 8p. doi: 10.1109/FUZZ-IEEE55066.2022.9882710
[19] Leite, D., Frigeri Jr., V., Medeiros, R. (2022). Incremental Fuzzy Machine Learning for Online Classification of Emotions in Games from EEG Data Streams. In: Handbook of Computer Learning and Intelligence, World Scientific, 29p. World Scientific
[18] Leite, D., Frigeri Jr., V., Medeiros, R. (2021). Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games. IEEE Latin American Conference on Computational Intelligence (LA-CCI), Temuco, Chile, 6p. doi: 10.1109/LA-CCI48322.2021.9769842
[17] Leite, D., Costa, P., Gomide, F. (2009). Interval-Based Evolving Modeling. IEEE Workshop on Evolving and Self-Developing Intelligent Systems (ESDIS), Nashville, 8p. doi: 10.1109/ESDIS.2009.4938992
Adaptive and Interpretable Models
Adaptive and Interpretable Models
[16] Leite, D., Gomide, F. (2026). Adversarial Perturbations on Level Set Fuzzy Regression Models. North American Fuzzy Information Processing Society Annual Conference (NAFIPS'26), El Paso, USA, 10p.
[15] Škrjanc, I., Iglesias, J., Sanchis, A., Leite, D., Lughofer, E., Gomide, F. (2019). Evolving Fuzzy and Neuro-Fuzzy Approaches in Clustering, Regression, Identification, and Classification: A Survey. Information Sciences, 490, 344–368. doi: 10.1016/j.ins.2019.03.060
[14] Leite, D., Škrjanc, I. (2019). Ensemble of Evolving Optimal Granular Experts, OWA Aggregation, and Time Series Prediction. Information Sciences, 504, 95–112. doi: 10.1016/j.ins.2019.07.053
[13] Leite, D., Gomide, F., Škrjanc, I. (2019). Multiobjective Optimization of Fuzzy Autonomous Evolving Fuzzy Granular Models. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, USA, 1–7. doi: 10.1109/FUZZ-IEEE.2019.8858964
[12] Soares, E., Costa, P., Costa, B., Leite, D. (2018). Ensemble of Evolving Data Clouds and Fuzzy Models for Weather Time Series Prediction. Applied Soft Computing, 64, 445–453. doi: 10.1016/j.asoc.2017.12.032
[11] Leite, D., Costa, P., Gomide, F. (2009). Evolving Granular Classification Neural Networks. International Joint Conference on Neural Networks (IJCNN), Atlanta, 1736–1743. doi: 10.1109/IJCNN.2009.5178895
Data-Driven Modeling and Control
Data-Driven Modeling and Control
[10] Leite, D., Palhares, R.M., Škrjanc, I., Gomide, F. (2026). Evolving Granular Fuzzy Control: Overview, Case Study on the Chaotic Henon Map, and Research Outlook. Applied Soft Computing, 190, 114639, 13p. doi: 10.1016/j.asoc.2026.114639
[9] Leite, D., Gomide, F., Škrjanc, I., Andonovski, G. (2026). Incremental Level-Set-Based LQR Residual Control for Nonstationary Crane Systems. 17th APCA International Conference on Automatic Control and Soft Computing (CONTROLO'26), Coimbra, Portugal, 8p.
[8] Andonovski, G., Leite, D., Precup, R.-E., Gomide, F., Pratama, M., Škrjanc, I. (2025). Advancements in Data-Driven Fuzzy and Neuro-Fuzzy Control: A Comprehensive Survey. Applied Soft Computing, 186(A), 114058. doi: 10.1016/j.asoc.2025.114058
[7] Leite, D., Škrjanc, I., Gomide, F. (2025). Inverse Fuzzy Learning Control of Unknown Nonlinear Dynamic Systems. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Reims, France, 6p. doi: 10.1109/FUZZ62266.2025.11152207
[6] Leite, D. (2022). State-Space Evolving Granular Control of Unknown Dynamic Systems. 1st Workshop on Online Learning from Uncertain Data, IEEE World Congress on Computational Intelligence, Padua, Italy, 18p. CEUR Proceedings, Vol. 3380
[5] Leite, D., Gomide, F., Yager, R. (2022). Data-Driven Fuzzy Modeling Using Level Sets. IEEE World Congress on Computational Intelligence, Padua, Italy, 6p. doi: 10.1109/FUZZ-IEEE55066.2022.9882555
[4] Leite, D., Coutinho, P., Bessa, I., Camargos, M., Cordovil Jr., L., Palhares, R. (2021). Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model. 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), 80–87. doi: 10.2991/asum.k.210827.011
[3] Aguiar, C., Leite, D., Pereira, D., Andonovski, G., Škrjanc, I. (2021). Nonlinear Modeling and Robust LMI Fuzzy Control of Overhead Crane Systems. Journal of the Franklin Institute, 358(2), 1376–1402. doi: 10.1016/j.jfranklin.2020.12.003
[2] Leite, D., Palhares, R.M., Campos, V.C.S., Gomide, F. (2015). Evolving Granular Fuzzy Model-Based Control of Nonlinear Dynamic Systems. IEEE Transactions on Fuzzy Systems, 23(4), 923–938. doi: 10.1109/TFUZZ.2014.2333774
PhD Thesis
PhD Thesis
[1] Leite, D. (2012). Evolving Granular Systems. PhD Thesis, University of Campinas (UNICAMP), School of Electrical and Computer Engineering, Campinas, Brazil, 188p. Thesis
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