Monica Bianchini

Monica BianchiniMonica Bianchini received the Laurea cum laude in Mathematics and the Ph.D. degree in Computer Science from the University of Florence, Italy, in 1989 and 1995, respectively. After receiving the Laurea, for two years, she was involved in a joint projct of Bull HN Italia and the Department of Mathematics (University of Florence), aimed at designing parallel software for solving differential equations. From 1992 to 1998, she was a Ph.D. student and Postdoc Fellow with the Computer Science Department of the University of Florence. Since 1999, she has been with the University of Siena, where she is currently Associate Professor at the Information Engineering and Mathematics Department. Her main research interest is in the field of artificial intelligence & applications, machine learning, with emphasis on neural networks for structured data and deep learning, approximation theory, information retrieval, bioinformatics, and image processing. M. Bianchini has authored more than seventy papers and has been Editor of books and special issues on international journals in her research field. She has been a participant in many research projects focused on machine learning and pattern recognition, founded by both Italian Ministry of Education (MIUR), and University of Siena (PAR scheme), and she has been involved in the organization of several scientific events, including the NATO Advanced Workshop on Limitations and Future Trends in Neural Computation (2001), the 8th AI*IA Conference (2002), GIRPR 2012, the 25th International Symposium on Logic Based Program Synthesis and Transformation, and the ACM International Conference on Computing Frontiers 2017. Prof. Bianchini served as Associate Editor for IEEE Transactions on Neural Networks 2003-2009), Neurocomputing (from 2002), and International Journal of Computers in Healthcare (from 2010). She is a permanent member of the Editorial Board of IJCNN, ICANN, CPR, ICPRAM, ESANN, ANNPR, and KES. Read More Read Less

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Advanced Computing and Intelligent Technologies37 %
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Advanced Computing and Intelligent Technologies37 %
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Machine Learning for Robotics Applications37 %
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Advanced Computing and Intelligent Technologies37 %
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Computationally Intelligent Systems and Their Applications37 %
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Machine Learning for Robotics Applications37 %
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Computationally Intelligent Systems and Their Applications37 %
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Advanced Computing and Intelligent Technologies37 %
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Innovations in Neural Information Paradigms and Applications37 %
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Handbook on Neural Information Processing37 %
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Innovations in Neural Information Paradigms and Applications37 %
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Handbook on Neural Information Processing37 %
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Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications22 % NR
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Artificial Intelligence for Future Generation Robotics18 % NR
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Rechnergestutzte intelligente Systeme und ihre Anwendungen
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