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Jorge Casillas Barranquero
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CATEDRÁTICO DE UNIVERSIDAD
Departamento de Ciencias de la Computación e Inteligencia Artificial |
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| E.T.S. de Ingenierías Informática y de Telecomunicación |
| C/ Periodista Daniel Saucedo Aranda S/N C.P. 18071 (Granada) Granada |
| 958248483 |
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| Importar contacto |
HORARIO DE TUTORÍAS
| PRIMER SEMESTRE | ||
|---|---|---|
| Día de la semana | Horario | Lugar |
| Martes | 10:00-11:30 | Mect (Fciencia) |
| Martes | 13:30-15:00 | Mect (Fciencia) |
| Jueves | 11:00-14:00 | D26 (Etsiit) |
| SEGUNDO SEMESTRE | ||
|---|---|---|
| Día de la semana | Horario | Lugar |
| Martes | 10:00-14:00 | D26 (Etsiit) |
GRADO INGENIERÍA INFORMÁTICA-ICC
GRADUADO/A EN INGENIERÍA ELECTRÓNICA INDUSTRIAL
GRADUADO/A EN ENFERMERÍA
GRADUADO/A EN INGENIERÍA INFORMÁTICA
- Inteligencia de Negocio (Especialidad Sistemas Información)
- Inteligencia de Negocio (Optativa Especialidad Sistemas Información)
GRADUADO/A EN INGENIERÍA INFORMÁTICA Y MATEMÁTICAS
MÁSTER UNIVERSITARIO EN CIENCIA DE DATOS E INGENIERÍA DE COMPUTADORES
Jorge Casillas received the PhD graduate degree in Computer Science in 2001 from the University of Granada, Spain. He is a Full Professor with the Department of Computer Science and Artificial Intelligence, University of Granada. He was Visiting Research Fellow at the University of the West of England (Bristol, UK), the University of Birmingham (Birmingham, UK) and the Osaka Metropolitan University (Japan).
He teaches on artificial intelligence, data science, big data, machine learning, and intelligent control and robotics. He is author of 40+ refereed journal articles and 60+ conference papers. His publications accumulate 4,500+ citations in Google Scholar (h-index 31).
His works in early 2000 in the pursuit of interpretable fuzzy models were pioneers. Since 2007, he has been working on the design of machine learning algorithms to deal with data streams in real-time. In recent years, his interest in a fairer, more equitable and minority-sensitive artificial intelligence has grown. Currently, he is leading a national research project on the use of responsible machine learning to characterize people with chronic diseases.