CAEPIA 20/21 Speakers
Oscar Cordón was the Founder and a Leader of the Virtual Learning Center (2001-05) and the Vice President of Digital University (2015-19) with the University of Granada (UGR). He was one of the Founding Researchers with the European Centre for Soft Computing (2006-11), being contracted as Distinguished Affiliated Researcher until December 2015. He is currently a Professor with the UGR. He has been, for >25 years, an internationally recognized contributor to Research and Development Programs in fundamentals and real-world applications of computational intelligence. He has published >380 peer-reviewed scientific publications, including a research book on Genetic Fuzzy Systems (with >1400 citations in Google Scholar) and 112 JCR-SCI-indexed journal papers (68 in Q1 and 38 in D1), advised 19 Ph.D. dissertations, and coordinated 37 research projects and contracts (with an overall amount of >9M€). From May 2021, his publications had received 5422 citations (H-index=39), being included in the 1% of most-cited researchers in the world (source: Web of Science), with 14687 citations and H-index=58 in Google Scholar. He also has a granted international patent on an intelligent system for forensic identification commercialized in Mexico and South Africa.
He received the UGR Young Researcher Career Award (2004), the IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award (2011, the first such award conferred), the IFSA Award for Outstanding Applications of Fuzzy Technology (2011), the National Award on Computer Science ARITMEL by the Spanish Computer Science Scientific Society (2014), the IEEE Fellow (2018), and the IFSA Fellowship (2019). He was a member of the High-Level Expert Group that developed the Spanish R+D Strategy for Artificial Intelligence by the Spanish Ministry of Science, Innovation and Universities (2018-19). He is currently or was Associate Editor of 19 international journals. He was recognized as an Outstanding Associate Editor of IEEE Transactions on Fuzzy Systems (2008) and of IEEE Transactions on Evolutionary Computation (2019). Since 2004, he has taken many different representative positions with EUSFLAT and the IEEE Computational Intelligence Society.
His current research lines are on artificial intelligence for forensic identification (with the UGR Physical Anthropology lab and several international forensic labs and security forces) and agent-based modeling and social network analysis for marketing (with R0D Brand Consultants in projects for CAPSA, Mercedes, Jaguar-Land Rover, El Corte Inglés, Telefónica, Samsung, Coca Cola Europe, Cola Cao, WiZink, …).
Artificial Intelligence for Forensic Anthropology and Human Identification
Skeleton-based forensic identification methods carried out by anthropologists, odontologists, and pathologists represent the first step in every human identification (ID) process and the victim’s last chance for identification when DNA or fingerprints cannot be applied. They include methods as biological profiling (BP), comparative radiography (CR), craniofacial superimposition (CFS), and comparison of dental records. BP involves the study of skeletal remains to find characteristic traits (age, sex, stature, and ancestry) that support determining the identity of the individual. It plays a crucial role in narrowing the range of potential matches during the process of ID, prior to the corroboration by any ID technique. CR considers the ante-mortem (AM) and post-mortem (PM) comparison of different bones and cavities (skull frontal sinuses, clavicles, patellae, …) which have been reported as useful for positive identification based on their individuality and uniqueness. CFS aims to overlay a skull with some AM images of a candidate in order to determine if they correspond to the same person.
However, practitioners still follow an observational paradigm using subjective methods introduced many decades ago; namely, oral description and written documentation of the findings obtained and the manual and visual comparison of AM and PM data. Designing systematic, automatic ad trustworthy methods to support the forensic anthropologist when applying BP, CFS and CR, avoiding the use of subjective, error-prone and time-consuming manual procedures, is mandatory to enhance forensic ID. The use of artificial intelligence, in particular computational intelligence (evolutionary algorithms, fuzzy sets and deep learning), computer vision (3D-2D image registration and image processing) and explainable machine learning is a natural way to achieve this aim. This keynote is devoted to present three intelligent systems for CFS, CR, and skeleton-based age-at-death assessment developed in collaboration with the University of Granada’s Physical Anthropology Lab within a fifteen years long research project. One of those systems is protected by an international patent, exploited by Panacea Cooperative Research, and is under commercialization in different countries.
Yaochu Jin received the BSc, MSc, and PhD degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.
He is currently a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, a “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. Most recently, he was endowed the “Alexander von Humboldt Professorship for Artificial Intelligence” by the Federal Ministry of Education and Research of Germany. His main research interests include data-driven surrogate-assisted evolutionary optimization, trustworthy machine learning, multi-objective evolutionary learning, swarm robotics, and evolutionary developmental systems.
Dr Jin is presently the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and the Editor-in-Chief of Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer, and Vice President of the IEEE Computational Intelligence Society. He is the recipient of the 2018 and 2020 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2014, 2016, and 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He is recognized as a Highly Cited Researcher 2019 and 2020 by the Web of Science Group. He is a Fellow of IEEE.
Data-Driven Evolutionary Optimization
Many real-world optimization problems do not have analytical objective functions and evaluations of the objectives must rely on expensive computations or physical experiments. These optimization problems are known as data-driven optimization problems. This talk provides an overview of data-driven surrogate-assisted evolutionary optimization of complex systems. We start with a brief introduction to the basic ideas in data-driven evolutionary optimization, followed by advanced surrogate management strategies that make use of advanced machine learning techniques such as semi-supervised learning, transfer learning and ensemble learning. Real-world examples from engineering design optimization to neural architecture search will be given.