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个人简介
Prof. Di Fatta’s research interests and expertise are in areas of Computer Science, such as Machine Learning, Data Science and Parallel and Distributed Computing, and in their intersection Big Data Analytics. He has also been developing data-driven multidisciplinary applications in scientific and industrial domains. In the area of Data Science and Big Data Analytics he has developed efficient and scalable parallel algorithm for frequent subgraph mining, space partitioning techniques for efficient classification and clustering algorithms for Big Data and data streams, efficient regression methods, etc. He was one of the authors of the first release (2006) of KNIME, a leading data science and machine learning platform. He has applied data science methods to a number of scientific domains, such as chemoinformatics, bioinformatics, neuroscience, digital marketing, accounting, etc. Prof. Di Fatta has over 120 publications to his credit. He has provided professional consultancies in the area of Data Science and Machine Learning, contributed to several research projects, led a number of successful Knowledge Transfer Partnerships projects on Big Data Analytics and Data Science, and has recently been contributing to two large European H2020 projects and an industrial research project (Innovate UK Smart Grant) for the development of an AI/ML enabled risk assessment platform to reduce fraud in the National Health Service (NHS).
研究兴趣
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International Conference on Machine Learning, Optimization, and Data Sciencepp.449-465, (2023)
International Conference on Machine Learning, Optimization, and Data Sciencepp.379-389, (2023)
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT II (2022): 531-544
user-61447a76e55422cecdaf7d19(2020)
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