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Alfonso Carlos Martínez EstudilloView profile

Professor

• Machine Learning
• Evolutionary Computation
• Neural Networks
• +7 more

Alfonso Carlos Martínez Estudillo is a Full Professor in the Department of Quantitative Methods at Loyola University's School of Business and Economics. He serves as Director of the Master's Program in Research Methods Applied to the Social Sciences and is a member of the Quantitative Research Methods and Applications (MICA) research group. With a PhD in Computer Science and Artificial Intelligence (2008) and a Computer Engineering degree (1995), both from the University of Granada, his academic journey includes previous roles as Network and Systems Administrator, Database Programmer, and IT Service Coordinator at ETEA-Faculty of Economics and Business. His research focuses primarily on evolutionary computation and neural networks for solving machine learning problems, with specialization in product-unit neural networks. His doctoral thesis opened an important research line within the AYRNA Research Group, where he has been a member since 2001. This work has led to publications in leading journals such as IEEE Transactions and Systems, Man and Cybernetics, and Neural Networks, with applications spanning predictive microbiology, chemical kinetics, pollen prediction, remote sensing, and financial risk assessment. Currently, he is working on multi-objective optimization, ordinal classification, and data mining with large databases using Big Data techniques. Professor Martínez Estudillo has participated in numerous research projects including R&D initiatives funded by the Ministry of Science and Technology, excellence projects from the Regional Government of Andalusia, and international cooperation projects analyzing social aspects of indigenous populations in Honduras. His recent publications demonstrate continued application of artificial intelligence techniques to economic problems, with his most recent work (2022) examining sovereign debt ratings in Europe. His teaching portfolio includes Databases, Fundamentals of Computer Science I, Computing Infrastructures and Databases, Applied Mathematics for Business Management, and Programming I, reflecting his expertise in both theoretical and applied computational methods for business and economics.

Esperanza García VergaraView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Violence Against Women
• +3 more

Esperanza García Vergara serves as Assistant Professor in the Department of Quantitative Methods at Loyola University Andalusia, where she teaches Probability and Statistics. Her interdisciplinary work bridges computer science, criminology, and public health through AI applications for gender-based violence prevention. Her academic credentials include: PhD in Data Science (2024) Master's in Research Methods (2021) Double Degree in Psychology and Criminology (2020) Dr. García Vergara's research pioneers AI-driven femicide risk assessment, developing genetic algorithm models that incorporate victim coping strategies and legal document analysis. Her methodology transforms static risk tools into dynamic predictive systems by integrating multilevel factors from victim behavior to judicial records, creating actionable prevention frameworks for law enforcement and social services. Her 2022-2024 publications reveal consistent interdisciplinary output across computational science (Heliyon, Sci Rep) and public health journals (IJERPH), with systematic reviews establishing foundational knowledge and algorithmic papers delivering technical innovation. This dual approach balances theoretical rigor with practical implementation. Currently leading a Junta de Andalucía-funded Pre-Doctoral Project (2022-2026), she directs research on femicide prediction models while mentoring doctoral candidates. Though no formal lab structure is documented, her project team operates at the intersection of data science and criminology. Her conference presentations at international forums—including the V International Conference on Predoctoral Research in Law (2022) and I International Symposium on Feminist Studies (2021)—demonstrate active engagement with judicial and feminist academic communities to translate research into policy impact.

• Machine Learning
• Data Analytics
• Artificial Intelligence
• +3 more

Maria-Cristina Marinescu serves as a Lecturer in the Department of Mathematics and Data Analytics at IQS School of Management. With a strong foundation in computational methods and data analysis, she contributes significantly to both teaching and research within the institution. Her academic profile demonstrates consistent scholarly activity with 44 documented scientific production items spanning over two decades. Dr. Marinescu's research interests center on Machine Learning, Data Analytics, and Artificial Intelligence, with notable applications in epidemic modeling, wireless sensor networks, and programming models. Her work bridges theoretical computer science with practical applications addressing real-world challenges in public health, digital well-being, and medical diagnostics. The fingerprint analysis of her work shows strong emphasis on Machine Learning (100%), Learning Systems (100%), and Transportation Models (100%), with substantial contributions to Programming Models (84%) and Wireless Sensor Networks (77%). Her recent publications reveal a trend toward interdisciplinary research that combines computational methods with societal challenges. She has made significant contributions to epidemic modeling during the COVID-19 pandemic, developing methods for accurate incidence rate estimation in Spain and analyzing information gains from multiple epidemic model outputs. Additionally, her work extends to medical applications including research on ocular ischemia and glaucoma, as well as innovative approaches to automated metadata annotation using machine learning techniques. Dr. Marinescu actively leads and participates in multiple research projects including Uncovering patterns of unconscious reactions to fake content (as Principal Investigator), MobilePressure (focused on reducing children's smartphone exposure), and ADAMIQS: Applied Data Analytics and Modelling IQS . These projects demonstrate her commitment to addressing contemporary issues through data-driven approaches while mentoring students and collaborating with interdisciplinary research teams.

Santiago Manuel López GarcíaView profile

Associate Professor

• Economic History
• Science and Technology Studies
• Technological Change
• +4 more

Santiago Manuel López García is an Associate Professor at the University of Salamanca, affiliated with the Institute of Science and Technology Studies (IeCyT) and the Department of Economics and Economic History. He has served as Head of the Master and PhD program in Social Studies of Science and Technology at Salamanca University since 2010 and was Vice-rector of Prospective and Development at the university from 2004 to 2009. His educational background includes a BA in Contemporary History from the Autonomous University of Madrid (1984) and a PhD in Economics (Technological Change) from the University Complutense of Madrid (1996). His doctoral thesis focused on 'El saber tecnológico en la política industrial del primer régimen de Franco' (Technological knowledge in the industrial policy of the first Franco regime). López García's research spans economic history, science and technology studies, and innovation policy, with particular focus on technological change, industrial clusters, and renewable energy development. His recent work has examined Spain's position in wind power development, technological innovation through patent analysis, and the historical evolution of science policy in Spain. His publications demonstrate consistent engagement with both historical analysis of technological development and contemporary issues in science policy, showing a trajectory that connects Spain's industrial past with its current innovation challenges. He has published extensively in journals related to economic history, science policy, and technology studies. As Editor of Investigations of Economic History (Journal of the Spanish Association of Economic History) from 2009 to 2011, he contributed significantly to the field of economic history scholarship. His research connects historical patterns of technological development with contemporary innovation policy challenges. He is involved with multiple research groups including CaUSAL (Academic Culture, Heritage and Social Memory) and HISCYT (Unit for the Study of the History of Industry and Science and Technology Systems), demonstrating his interdisciplinary approach to the study of science, technology, and society.

• Fuzzy Data Analysis
• Statistical Analysis of Imprecise Data
• Random Sets
• +3 more

Ana Belén Ramos Guajardo is a Professor in the Department of Statistics and Operations Research and Mathematics Education at the University of Oviedo. She is affiliated with the GRINAT Research Group (Grupo de Investigación en Riesgos Naturales) and holds a doctorate from the University of Oviedo with her thesis titled "Contrastes de hipótesis tratamiento de la variabilidad y la imprecisión" (2011), supervised by Dr. Gil González Rodríguez, Dr. María Angeles Gil Alvarez, and Dr. Ana María Colubi Cervero. Her research focuses on statistical methodologies for imprecise data, particularly fuzzy data analysis, random sets, interval data analysis, and hypothesis testing. She has developed innovative approaches for distance-based statistical analysis, fuzzy clustering, and regression models for random intervals. Her work bridges theoretical statistics with practical applications in environmental sciences and other domains requiring analysis of imprecise measurements. Her publications reveal a strong focus on developing statistical methods for non-standard data types, with particular emphasis on random fuzzy sets and interval-valued data. She has contributed significantly to hypothesis testing frameworks for fuzzy data, distance metrics for imprecise data analysis, and computational methods for implementing these techniques. Her most recent work continues to advance the theoretical foundations while exploring practical applications. Ramos Guajardo has collaborated extensively with researchers in the fuzzy data analysis community, particularly with Ana Colubi, Gil González-Rodríguez, and María Asunción Lubiano. Her work has been published in top statistical journals including International Journal of Approximate Reasoning, Fuzzy Sets and Systems, and Information Sciences. She has advised or collaborated with numerous researchers in the field, contributing to the development of statistical fuzzy data analysis as a specialized subfield. Her methodological contributions have applications in environmental risk assessment, particularly through her affiliation with the Natural Risks Research Group at the University of Oviedo.

• Big Data Analytics
• Data Science
• Statistical Methods
• +1 more

Maria Concepcion Ausin Olivera serves as a Professor at Carlos III University of Madrid, where she directs the Master's Degree in Analytical Methods for Big Data. Her academic base is at the Getafe campus in room 10.1.18 of the Campomanes building. Her research expertise spans critical domains in modern data science: Big Data Analytics methodologies for complex datasets Advanced Data Science frameworks and applications Rigorous Statistical Methods development Machine Learning algorithm implementation Professional engagement includes leadership in graduate education and research supervision. Contact is available via email concepcion.ausin@uc3m.es or telephone 916245852.