Scholar
Daniel F. Leite
Google Scholar ID: mZKDqM8AAAAJ
Researcher, Paderborn University, Germany
Citations & Impact
Citations
1,742
H-index
20
i10-index
34
Publications
20
Co-authors
30
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- 2023 Stanford top 2% most influential scientists (AI & Image Processing #6471); 2021 Alysson Paulinelli Award for the paper with the highest impact factor of the year, UFLA, Minas Gerais, Brazil; 2019 IEEE Senior Member; 2017 NAFIPS Early Career Award; 2017 IEEE CIS Outstanding PhD Dissertation Award - IEEE Computational Intelligence Society, Naples, Italy; 2015 Best PhD Thesis Award - North American Fuzzy Information Processing Society, NAFIPS, Washington, D.C., US; 2014 Best PhD Thesis in Artificial Intelligence - Brazilian Computer Society, SBC, São Carlos, São Paulo, Brazil; 2012 IEEE CIS Outstanding Student Paper - IEEE World Congress on Computational Intelligence, Brisbane, Australia.
Research Experience
- Researcher in the Department of Computer Science, Data Science (DICE) Group, Paderborn University, Germany (since November 2023). Previously, a professor and researcher at UFLA, UFMG, Brazil, and UAI, Chile, for 11 years, in the areas of dynamic systems, fuzzy systems, neural networks, data mining, and control theory.
Education
- PhD: 2012, State University of Campinas (UNICAMP), Brazil; Postdoctoral Fellow: 2018-2019, University of Ljubljana (UL), Slovenia; 2013-2014, Federal University of Minas Gerais (UFMG), Brazil.
Background
- Research interests: human-centered systems, machine learning, and control systems. Particular emphasis on new perspectives for the study of data uncertainty and new methodologies to analyze and detect patterns in data streams for decision support and forecasting. Applications include prediction of meteorological phenomena; image-based navigation and reasoning of autonomous robots in unknown environments; classification of image and interval data streams; early detection of medical disorders, control of engineering systems, and fault detection for predictive maintenance.
