Emilio Gomez Deniz is a Full Professor at the Universidad de Las Palmas de Gran Canaria, specializing in Bayesian statistics and decision-making techniques within economics and business contexts. His research focuses on distribution theory, actuarial statistics (credibility, ruin theory), and robust Bayesian analysis, with significant applications in tourism, insurance, and financial risk modeling. Key Research Areas: Bayesian statistics, asymmetric distributions, credibility theory, risk aggregation, and econometric modeling. Recent Publications: 2024–2025 work includes applications of the Gleser distribution to flood data, Bayesian credibility premiums, skew-unit models, and citation analysis using normalized Bayesian approaches. Projects: Collaborator on Spanish Ministry-funded projects related to Bayesian decision-making in health economics, risk modeling, and actuarial science. Collaborations: Works with interdisciplinary teams on topics like tourist expenditure, aircraft delay modeling, and social challenges. Books Authored: Textbooks on applied statistics for business, generalized beta distributions, and stochastic frontier models.
Víctor López Domínguez is an Assistant Professor and Principal Investigator at the Institute of Advanced Materials (INAM) of Jaume I University in Castelló, Spain, where he leads the Spintronics for Advanced Devices Lab (SPINAD Lab). His research focuses on developing next-generation spintronic devices for computing and sensing applications. His academic background includes: PhD in Nanoscience and Nanotechnology (2014) from University of Barcelona Physics Degree (2009) from University of Barcelona López Domínguez's research centers on electrical transport in magnetic materials, magnetization dynamics, and voltage-controlled spintronic devices. His work bridges fundamental physics with practical applications in neuromorphic computing, probabilistic systems, and nanoscale sensors. Key focus areas include antiferromagnetic spintronics, skyrmion manipulation, and energy-efficient computing paradigms that leverage spin-orbit torque and voltage control mechanisms. His 15 most recent publications reveal a strong emphasis on antiferromagnetic memory devices, voltage-controlled magnetism, and neuromorphic applications. The research spans from fundamental material studies to device integration, with significant contributions to silicon-compatible spintronic memory, probabilistic computing hardware, and magnetomechanical sensors. His scientific recognition includes: CIDEGENT grant (2022) from Generalitat of Valencia López Domínguez secured the CIDEGENT grant to establish his research group at INAM, supported by Jaume I University and INAM. His lab trains undergraduate, master's, and PhD students in experimental spintronics while collaborating with international clean rooms and industry partners. Current projects focus on implementing novel computing paradigms using spintronic devices. The SPINAD Lab maintains comprehensive facilities for material fabrication, device characterization, and nanofabrication through Spanish clean room networks. The group collaborates extensively with Northwestern University (where López Domínguez previously worked) and participates in international research consortia focused on next-generation computing technologies.
Professor Mark Steel is a faculty member at the University of Warwick , serving as Professor of Statistics since 2003. He previously held a Chair in Economics at the University of Edinburgh (1998-2000) and a Chair of Statistics at the University of Kent (2000-2003). He has also served as Head of the Statistics Department at Warwick (2014-2018) and as Editor-in-Chief of Bayesian Analysis (2022-2025). Education: Ph.D. in Quantitative Economics from Université Catholique de Louvain (1987) Steel's research focuses on Bayesian econometrics and statistics , with interests in distribution theory, model averaging, spatial statistics, nonparametric inference, survival analysis, and stochastic volatility. His work bridges theoretical advancements with applications in macroeconomics (growth theory) , microeconomics (stochastic frontier models) , and finance (stochastic volatility) . Recent publications highlight his contributions to Bayesian computation , copula-based classification , and spatiotemporal modeling . His editorial roles include leadership in Bayesian Analysis and Journal of Productivity Analysis . Scientific Awards: Fellow of the Journal of Econometrics Steel co-leads the Centre for Research in Statistical Methodology (CRiSM) at Warwick and has secured grants such as the EPSRC GR/T17908/01 for flexible distributional modeling in panel data. His collaborations span institutions in the UK, Spain, Italy, and the Netherlands, with co-authors like Roberto Casarin and Jairo Fúquene.
Sergio Jiménez Celorrio is a Full Professor at the Department of Computer Systems and Computation of the Polytechnic University of Valencia. He has held previous positions including Ramón y Cajal fellow at the University of Melbourne, Juan de la Cierva fellow at Universitat Pompeu Fabra, and teaching assistant at Universidad Carlos III de Madrid, where he earned a Distinguished Thesis Award in 2011. His research focuses on automated planning, Bayesian inference, and machine learning synergies. He has co-organized the 7th International Planning Competition and contributed to top AI conferences. Education: PhD in Artificial Intelligence from Universidad Carlos III de Madrid (2011, Distinguished Thesis Award). Research interests emphasize automated planning frameworks, heuristic search, and integrating machine learning with planning systems. Awards include the IJCAI 2016 Distinguished Paper Award and Sister Conferences Best Paper Award at IJCAI 2022. His work spans over 50 publications in venues like AI Journal, JAIR, and IJCAI. Advises PhD student Diego Aineto García and collaborates on grants and competitions. Active in organizing conferences and workshops.
David Valentín Conesa Guillén is a Full Professor in the Department of Statistics and Operations Research within the Faculty of Mathematics at the University of Valencia. He is an active researcher and a key member of the Valencia Bayesian Research Group (VABAR), focusing on advanced statistical methodologies and their applications. His primary research interests encompass Bayesian Statistics , Statistical Modeling , Operations Research , Epidemiological Modeling , Spatial Statistics , and Ecological Statistics . His work bridges theoretical development with practical applications in public health, environmental science, and financial systems. The trends in his recent publications reveal a strong emphasis on developing and applying Bayesian hierarchical models to complex real-world problems. Key areas include dynamic forecasting of influenza outbreaks, modeling spatial distributions of ecological and bioclimatic data, correcting biases in ecological survival estimates, and analyzing efficiency in sectors like banking and higher education. His methodology frequently involves advanced computational techniques like the Integrated Nested Laplace Approximation (INLA). PhD in Statistics, University of Valencia (2000) Thesis: "Inferencia y predicción en colas con ingresos o servicios en grupos" Supervised by Dr. Carmen Armero Cervera David Conesa has supervised academic work, as indicated by his role as a thesis advisor. His research has been supported by collaborative projects, leading to publications in top-tier journals such as Bayesian Analysis , European Journal of Operational Research , and Journal of Agricultural, Biological, and Environmental Statistics . He has extensive collaborations with researchers across Spain and internationally. His research is conducted within the VABAR (Valencia Bayesian Research Group) , a collaborative team dedicated to Bayesian methodology and its applications. This group provides a platform for interdisciplinary projects and the training of future statisticians.
Ana Corberán Vallet is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València, Spain. She is a member of the PROMEDyA research group, focusing on prediction and optimization under uncertainty using dynamic stochastic models. Her research lies at the intersection of Bayesian statistics, time series forecasting, and epidemiological modeling. She specializes in developing and applying Bayesian stochastic models to real-world problems, particularly in public health, such as modeling the spread of respiratory syncytial virus. Her work often integrates simulation-based inference and spatial statistics for health survey data. Her recent publications show a strong trend toward spatial and hierarchical Bayesian modeling, especially in health applications, with a focus on small area estimation and ordinal data analysis. She frequently collaborates with experts in mathematical epidemiology and biostatistics. She earned her Ph.D. from the Universitat de València in 2009, supervised by Dr. José Domingo Bermúdez Edo and Dr. Enriqueta Vercher González. Her doctoral work centered on Bayesian multivariate exponential smoothing models. She has published in journals such as Statistics in Medicine , Biometrical Journal , and Journal of Statistical Planning and Inference . Ana Corberán Vallet advises students and collaborates on interdisciplinary research projects involving statistical modeling of health and biological systems. She is actively involved in research without mention of external grants or awards in the provided texts. She is associated with the PROMEDyA research group, which works on dynamic stochastic models for prediction and optimization under uncertainty, particularly in health and operational contexts.
Antonio Manuel López Quilez is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with several research groups, including POpE (Public Opinion and Elections), VABAR (Valencia Bayesian Research Group), and VIO-STRAT (Advanced research strategies in family and gender violence). His email is antonio.lopez@uv.es. Doctor by the Universitat de València (1997) Thesis: Modelos lineales generalizados espaciales Supervisor: Dr. Juan Ferrándiz Ferragud His research focuses on statistical methodology with applications in spatial statistics, Bayesian inference, public health, environmental science, and social sciences. Key areas include spatial modeling, disease mapping, electoral analysis, and Bayesian hierarchical models. He frequently applies his methods to public health surveillance, such as influenza outbreak detection, and environmental modeling, including acoustic mapping with barriers. His recent publications highlight a strong trend in Bayesian modeling, particularly using integrated nested Laplace approximations (INLA), spatial smoothing, and Dirichlet regression for compositional data. He has contributed to dynamic forecasting of infectious diseases and the statistical analysis of bioclimatic indices. His work bridges theoretical statistical development with practical applications in epidemiology and social policy. No scientific awards are mentioned in the provided text. There is no information available about student advisement or research grants. However, his extensive collaborative work suggests active participation in research projects. He is a member of multiple interdisciplinary research teams, including those focused on public opinion, Bayesian methods, and gender violence research, indicating a strong team-based research approach. He leads or contributes to research in advanced statistical strategies for social and health issues, particularly through the VIO-STRAT group, which focuses on family and gender violence. His methodological expertise supports applied research in public policy and epidemiological decision-making.
Miguel Ángel Martínez Beneito is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. His research focuses on Bayesian statistics, spatial epidemiology, and disease mapping, with applications in public health and risk cluster detection. Education: PhD in Statistics from the University of Valencia (2005), thesis on statistical methods for detecting risk foci in epidemic outbreaks. His research interests include Bayesian modeling, spatial statistics, and computational epidemiology. He is a member of the Valencia Bayesian Research Group (VABAR), contributing to advanced statistical methodologies in health sciences. The recent publications attributed to him span topics in stochastic processes, biomechanics, and mathematical biology. However, there is a possibility of name disambiguation, as some works on biomechanics appear more aligned with a researcher from the University of Zaragoza. The core research at UV remains in statistical and epidemiological modeling. Scientific Contributions: Development of Bayesian methods for disease mapping. Application of statistical models to public health surveillance. Potential contributions to stochastic modeling in biological systems. He advises students in statistics and public health, though no specific advisees are listed. He has collaborated extensively with researchers in applied mathematics and biomechanics, though the nature of these collaborations requires further clarification due to potential name overlap. He is affiliated with the VABAR research group, focusing on Bayesian inference and its applications in real-world health problems.
Germà Coenders Gallart is a full Professor in the Department of Economics at the University of Girona, where he holds the Chair in Quantitative Methods for Economics and Business. He is affiliated with the Faculty of Economic and Business Sciences and actively contributes to research through the Research Group in Statistics, Econometrics and Health (GRECS), the consolidated Catalan research group COSDA (Compositional and Spatial Data Analysis), and the Campus of Food and Gastronomy. He earned his PhD in Management Sciences from Ramon Llull University in 1996. PhD in Management Sciences, Ramon Llull University (1996) His primary research focus is compositional data analysis (CoDa), a statistical methodology for analyzing parts of a whole, with wide applications in accounting, finance, marketing, tourism, and health. He has pioneered the use of CoDa in financial statement analysis, developing compositional structural equation models, spatiotemporal models, generalized linear models with compositional predictors, and models incorporating totals. His methodological innovations address key statistical issues in traditional financial ratio analysis. His recent publications (2023–2025) reflect a strong trend in applying CoDa to real-world economic and social challenges, including financial resilience during geopolitical crises (e.g., Ukraine-Russia war), post-pandemic recovery in tourism and fisheries, air pollution and child mental health, biodiversity accounting in apiculture, and systemic risk in the Eurozone. His work combines rigorous statistical modeling with practical relevance across multiple domains. Scientific recognition includes: 5 Sexennis (Research Quinquennia) from CNEAI h-index of 29 (Web of Science and Scopus), 44 (Google Scholar) Over 3000 citations (Web of Science) Founding member and former Secretary General of the Association for Compositional Data He has supervised 7 doctoral theses and collaborates extensively with researchers across disciplines. He has secured multiple research grants and leads a dedicated lab on financial statement analysis as compositional data. His teaching spans research methods, statistics, and econometrics across undergraduate, master’s, and doctoral programs at the University of Girona and other institutions including ESADE and the University of Ljubljana. He is a member of editorial boards for journals such as Metodoloski Zvezki , ReHuSo , and Tourism Analysis , and maintains a strong digital presence through ORCID, ResearchGate, Google Scholar, and other academic platforms.
Martel Escobar, María Carmen is a University Professor at the Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria. She is affiliated with the GIR TIDES research group focused on Bayesian Statistical Techniques and the IU of Tourism and Sustainable Economic Development. Her research spans Bayesian statistics, meta-analysis, and applications in tourism economics and health. Articles like "Managing score heterogeneity between online consumer review websites" (2023) and "Contagious statistical distributions" (2022) highlight her work in Bayesian frameworks and interdisciplinary applications. She applies these methods to tourism management, hospitality, and clinical studies such as the Pickwick trials on obesity hypoventilation syndrome. Key collaborations include work with Vázquez Polo, Francisco José, Masa, Juan F., and others. Her publications in journals such as PLOS ONE , The Lancet , and Thorax reflect her impact in both statistical and medical fields. She contributes to textbooks on business mathematics and is active in cost-effectiveness analysis and decision-making models.
Emilio Gómez Déniz is a Professor at the University of Las Palmas de Gran Canaria, affiliated with the Department of Quantitative Methods in Economics and Management and the IU of Tourism and Sustainable Economic Development research group. His academic rank is University Professor, and he is actively involved in Bayesian statistical techniques and decision-making methodologies in economics and business. His research focuses on actuarial science, econometrics, Bayesian methods, health economics, tourism economics, and risk analysis. He has contributed to projects funded by national and regional bodies, including studies on health economics Bayesian solutions, tourism sustainability, and risk analysis using actuarial data. Key projects include 'Economic Evaluation and Meta-Analysis: Bayesian Solutions in Health Economics' (2022–2026) and 'Laboratory of Tourism Experiences and Sustainability in the Multimedia Environment' (2019–2022). His publications span topics like distribution modeling, Bayesian credibility theory, and tourism expenditure analysis. Awards and grants are not explicitly mentioned in the provided text.
Isabel Sanmartín Bastida is a Researcher and Deputy Director of Research and Documentation at the Real Jardín Botánico of the Spanish National Research Council (CSIC). She specializes in biogeography, focusing on evolutionary and ecological processes shaping biodiversity patterns, particularly using Bayesian inference methods and genomic data. Her work addresses macroevolutionary dynamics, island biogeography, and the impact of historical climate change on species distributions. Her research integrates phylogenetic, genomic, and paleoclimatic data to study topics such as plant distribution modeling, extinction patterns in African lineages, and the role of aridification in shaping biomes. Key projects include the ODDMANOUT initiative exploring low plant diversity in tropical Africa and the EUGENIA project on conservation genomics of Euphorbia species. Publications span biogeography, phylogenetics, and evolutionary ecology, with contributions to Journal of Biogeography , Molecular Ecology , and Systematic Biology . She has led multiple research grants and co-authored over 80 peer-reviewed articles. Her work emphasizes bridging micro- and macroevolutionary scales, leveraging Bayesian methods and interdisciplinary approaches. Notable collaborations include studies on insular floras, steppe biota resilience, and the biogeographic history of diverse taxa. She has also contributed to policy frameworks for molecular collections and biodiversity preservation strategies. Her lab at Real Jardín Botánico focuses on advancing methodological tools for biogeographic inference and addressing global biodiversity challenges.
Pablo Frías Marín is a Senior Associate Professor at the Electrical Engineering Department and researcher at the Institute for Research in Technology (IIT) in Universidad Pontificia Comillas' Engineering School ICAI. He holds Industrial Engineering and Ph.D. degrees from 2001 and 2008, respectively, and serves as Vice-Dean for Economic and Institutional Affairs at ICAI since 2018. His research focuses on renewable energy integration, smart grids, distributed resources, electric vehicles, and energy system economics. Senior Associate Professor, Comillas (2004–present) Head of 'Smart and Sustainable Grids' Research Group (2009–2016) Deputy Director of IIT (2016–2018) Director of Observatory of Electric Vehicles and Sustainable Mobility International collaborations at Lawrence Berkeley National Lab, Imperial College, and INESC His 200+ publications span power systems optimization, electric mobility integration, and regulatory frameworks. Recent work emphasizes battery storage for network resilience, TSO-DSO coordination , and flexibility procurement in distribution planning. Key projects include the EU-funded IELECTRIX and GRID4EU initiatives. Scientific awards include: ACAP National Certification for Ph.D. Assistant Professor Two Six-Year Research Accreditations from Spanish Ministry He leads the Electric Machines and Drives Lab (2013–2019), coordinates post-graduate programs, and advises Ph.D. students on topics like grid integration and energy modeling. His projects with institutions like MIT, European Commission, and Iberdrola address smart grid technologies, renewable policies, and demand response mechanisms.
Paulo Félix Lamas is a Full Professor at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He obtained his PhD in Physics from USC in 1999 and became an Associate Professor in 2002. As a foundational leader of CiTIUS (Research Centre in Intelligent Technologies), he served as Head (2010–2019) and Deputy Director (2019–2020). His research focuses on probabilistic learning , temporal abductive reasoning , and biomedical signal interpretation . Key applications include ECG analysis, glucose monitoring, and AI-driven healthcare solutions. His work bridges machine learning with clinical diagnostics, emphasizing interpretability and real-time systems. Recent publications (2015–2024) demonstrate interdisciplinary innovation, spanning: Healthcare AI : Personalized drug dosing, arrhythmia detection, and continuous glucose monitoring. Computational methods : Stochastic embeddings, kernel-based missing data handling, and adaptive clustering. Theoretical advances : Abductive reasoning frameworks for time-series interpretation. He leads major projects like: SOSFood (2024–2028): AI for sustainable food systems. XAI4SOC (2022–2025): Explainable AI for healthy aging. INSIDE (2022–2024): Predictive cardiac rehabilitation. At CiTIUS, he directs research on intelligent healthcare technologies, integrating sensor data with machine learning for chronic disease management.
Dr. Mario Castro Ponce is a Professor at the Escuela Técnica Superior de Ingeniería (ICAI) , Universidad Pontificia Comillas, where he has worked for 28 years. He holds a Ph.D. in Physics from Universidad Complutense de Madrid and serves as a Visiting Professor at the University of Leeds since 2016-2017. His research bridges Statistical Mechanics with applications in Complex Systems , Biophysics , and Epidemiology , with a focus on modeling experimental data through analytical and computational methods. Education: Ph.D. in Physics, Universidad Complutense de Madrid B.S. in Physics, Universidad Complutense de Madrid Research Themes: Statistical mechanics of complex systems Ion-beam nanopatterning Theoretical immunology Wildfire dynamics Biofluid microrheology Machine learning in social science Publications (89+ peer-reviewed) span interdisciplinary topics, including: SARS-CoV-2 geometry and T-cell receptor dynamics Non-Newtonian blood flow modeling Wildfire risk assessment via Bayesian networks Viscoelastic thermosyphon systems Complex pattern formation in biological and physical systems Funding includes 6 principal investigator projects for Spain's Ministry of Science and participation in 3 EU Horizon 2020/Marie Skłodowska-Curie actions. His work has been cited over 2000 times.
