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Elpiniki Papageorgiou |
Contact information University of Thessaly, Dept. of Energy Systems, email: |
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Field of expertise: Expert Systems and Knowledge Representation, Fuzzy Cognitive Maps, Artificial Intelligence, Modeling and Prediction, Decision Support Systems, Data mining, Machine learning, Medical Decision Making. Short CV: Dr. ELPINIKI I. PAPAGEORGIOU is a Professor in Artificial Intelligence, at Energy Systems Department, University of Thessaly, Larissa, Greece. She holds a PhD in Computer Science from the University of Patras (Sept. 2004) and an MSc in Medical Physics from the same University (2000). She specializes in developing and applying artificial intelligent models and algorithms to decision support problems for modeling, prediction, strategic decisions, scenario analysis and data mining, solving important emerging problems, arising in engineering, energy, business, medicine, agriculture and environment. Her main research specialization is the development of novel algorithms and fuzzy models for intelligent decision support systems focused on Fuzzy Cognitive Maps. Prof. Papageorgiou is ranked first author worldwide in the field of “Fuzzy Cognitive Maps”, regarding her published work in this field, (source: Scopus, academic.microsoft.com). Based on her pioneering research on Fuzzy Cognitive Maps (FCMs), FCMs have been found great applicability and popularity in discipline research areas. For the fourth consecutive year, 2020-2023, Prof. Papageorgiou is included in the World's Top 2% of Scientists List at Stanford University ranking, in the field of “Artificial Intelligence”, which is among her main scientific fields of expertise. (Elsevier Data Repository, V6, doi: 10.17632/btchxktzyw.6). Moreover, she is within the Top Scientists in Computer Science - Guide–2–Research (https://www.guide2research.com/scientists/GR). Dr. Papageorgiou is actively involved in various European and Greek projects, working as project manager, technical manager and/or senior researcher. Also, her strong relationship with external funding bodies and especially EU and national funding environments, gave her an experience in managing research teams in different application domain projects. She is Academic Partner in iBO-CERTH and Team Leader in AI Group holding the position of R&D project manager in Horizon projects. Up-to-date, she is Principal Investigator in the Horizon2020 project, entitled OPTIMAI: Optimizing Manufacturing Processes through Artificial Intelligence and Virtualization (UTH partner) and in the national project HFRI (Hellenic Foundation for Research and Innovation), entitled EMERALD: “Fuzzy Cognitive Explainable Analytics for Translating Model Complexity in Nuclear Medical Diagnosis”. Prof. Papageorgiou is Director of ACTA Lab in Dept of Energy Systems, and Director in MSc program in “Energy and Automation Systems”, University of Thessaly, Larissa. She has more than 260 publications in journals, conference papers and book chapters and has more than 10000 citations from independent researchers (h-index=56 in GoogleScholar). She is also the Editor of the Springer book “Fuzzy Cognitive Maps for Applied Sciences and Engineering - from fundamentals to extensions and learning algorithms”, Intelligent Systems Reference Library 54, Springer 2014. Dr. Elpiniki Papageorgiou organized for first time worldwide the First Summer School for Fuzzy Cognitive Maps in July 2016 at Volos, Greece. Furthermore, she organized a number of Special Sessions on Fuzzy Cognitive maps entitled as “Methods and Applications of Fuzzy Cognitive Maps” in FUZZ-IEEE conferences 2011, 2012, 2013, WCCI2014 (IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014), FUZZ-IEEE2015, WCCI2016 and FUZZ-IEEE2017, and she was Chair in 6 of them. She is IEEE Senior Member, IEEE in Women in Computational Intelligence, and member in IEEE CIS. Her research interests include intelligent systems, expert systems, fuzzy cognitive maps, knowledge representation, soft computing methods, decision support systems, cognitive systems, prediction, medical decision making, data mining and machine learning.
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