
Dr. Daniel Leite
Data Science / Heinz Nixdorf Institut
Postdoc
Scientific Researcher (E14 Human-Centered AI)
- E-Mail:
- daniel.leite@uni-paderborn.de
- Telefon:
- +49 5251 60-5186
- ORCID:
- 0000-0003-3135-2933
- Web:
- Homepage
- Homepage (Extern)
- Social Media:
- Büroanschrift:
-
Fürstenallee 11
33102 Paderborn - Raum:
- FU.201.1
Über Daniel Leite
Daniel Leite is a researcher in the Department of Computer Science, Data Science (DICE) Group, Paderborn University, Germany. For 11 years he was a professor and researcher at the UFLA and UFMG, Brazil; and UAI, Chile, in the areas of dynamic systems, fuzzy systems, neural networks, data mining, and control theory. He earned his PhD from the State University of Campinas, UNICAMP, Brazil, 2012, and was a postdoctoral fellow at the University of Ljubljana, Slovenia, 2018-2019, and Federal University of Minas Gerais, UFMG, Brazil, 2013-2014. He received the North American Fuzzy Information Processing Society NAFIPS Early Career Award (2017); and PhD Thesis awards from the IEEE Computational Intelligence Society (2017), NAFIPS (2015), and Brazilian Computer Society (2014). He was granted for outstanding student papers in fuzzy systems and neural network events (FUZZ-IEEE and IJCNN) in the US, Australia, Brazil, and Scotland. He supervised 46 students/researchers from undergraduates to postdocs, and participated in 42 MSc/PhD thesis committees. He is an IEEE Senior Member, coordinated or collaborated in 13 R&D projects on AI and applications, and contributes as an Associate Editor of the Evolving Systems journal.
Forschung
Forschungsschwerpunkte
- Lifelong machine learning
- Human-Centered AI
- Deep neural networks
- Granular computing
- Control theory
Publikationen
Aktuelle Publikationen
Trading-Off Interpretability and Accuracy in Medical Applications: A Study Toward Optimal Explainability of Hoeffding Trees
A. Sharma, D. Leite, C. Demir, A.-C.N. Ngomo, in: 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2024.
Interpretability Index Based on Balanced Volumes for Transparent Models and Agnostic Explainers
D. Leite, A. Sharma, C. Demir, A.-C. Ngomo, in: 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2024.
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams
D. Leite, A. Silva, G. Casalino, A. Sharma, D. Fortunato, A.-C. Ngomo, in: 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), IEEE, 2024.
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