Sebastian Ventura

University of Córdoba Spain

Sebastián Ventura Soto is a Full Professor of Computing Sciences and Artificial Intelligence at the University of Córdoba, where he teaches computer programming, artificial intelligence, and data mining at undergraduate and doctoral levels. He also leads the Knowledge Discovery and Intelligent Systems (KDIS) research group, focusing on computational intelligence, data science, and their practical applications. Prof. Ventura has a record of over 270 peer-reviewed scientific publications, with 80.3% ranked in Q1 and 54.9% in D1 over the past five years. His work has achieved significant academic impact, with over 23,661 citations, an h-index of 58, and a Field-Weighted Citation Impact of 2.00. Furthermore, 24.3% of his papers are within the Top 10% Citation Percentiles in his field (SJR). These achievements have placed him in prestigious international rankings, including Stanford-Elsevier’s Top 2% of most-cited researchers in Artificial Intelligence (2020–2022) and Research.com’s Ranking of Best Scientists in Computer Science (currently ranked 43rd in Spain). As a leading researcher, Prof. Ventura has coordinated eight national and two international research projects, including the MANPREDIC project (€478k). He has also directed research contracts with private firms, managing a total budget exceeding €2M. His editorial contributions include roles as Editor-in-Chief of Progress in Artificial Intelligence and Associate/Area Editor for eight international journals. Prof. Ventura's influence extends globally, with collaborations involving over 300 co-authors from 42 countries and 140 institutions. He has supervised 19 Ph.D. dissertations and led a team of more than 12 researchers and 15 Ph.D. students. His active participation in academic conferences and recognition in global rankings highlight his significant contributions to the advancement of artificial intelligence and data science.

Sebastian Ventura

3books edited

Latest work with IntechOpen by Sebastian Ventura

The solution to many real-world problems lies in optimizing processes, parameters, or techniques, which requires dealing with immense search spaces. As such, finding solutions involves exhaustive methods to evaluate all possible solutions in the search for a global optimum. Some of these methods include evolutionary algorithms and genetic algorithms, both of which have proven to effectively deal with complex search spaces. This book focuses on genetic algorithms and their applications in various fields, including engineering and architecture.

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