Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.
Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.
Francisco Manuel Alonso Chaves is a Professor in the Department of Earth Sciences at the Faculty of Experimental Sciences, University of Huelva, Spain. He is affiliated with the Huelva Scientific and Technological Center and leads the research group RNM276 APPLIED GEOSCIENCES. His academic focus lies in Internal Geodynamics, contributing significantly to the understanding of tectonic processes in the Betic Cordillera and surrounding regions. Education: PhD in Geology, University of Granada (1995). Thesis: "Tectonic evolution of Sierra Tejeda and its relationship with processes of crustal thickening and thinning in the Betic mountain ranges", supervised by Dr. Miguel Orozco Fernández. His primary research interests encompass Tectonics, Geodynamics, Seismology, and Structural Geology. He investigates crustal deformation, seismic activity, and the evolution of mountain belts, with a regional focus on the Betic Cordillera and the Iberian Peninsula. His work integrates field studies, geophysical methods, and advanced data analysis to unravel complex tectonic histories and assess seismic hazards. Analysis of his recent publications reveals a strong emphasis on tectonic processes, particularly in the Guadalquivir Basin and the Betic Cordillera. His work utilizes seismic noise recording, kernel density estimation, and passive seismic techniques to study basin architecture, fault reactivation, and crustal structure. There is a consistent focus on Neogene extension, earthquake analysis (including the Türkiye-Syria events), and the application of geospatial tools like QGIS for tectonic interpretation. Scientific Awards: No scientific awards mentioned in available information. Advising and Grants: No details provided regarding students supervised or research grants secured. Labs and Teams: Dr. Alonso Chaves is a key member of the RNM276 APPLIED GEOSCIENCES research group and conducts his work at the Huelva Scientific and Technological Center. His team focuses on applied geological research, including seismic microzonation, tectonic modeling, and environmental geology, contributing to both academic knowledge and practical applications in the region.
Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.
Jose Navarro Cid is a Full Professor at the Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona. He leads the PsicoSAO research group focusing on work, organizational, and environmental psychology. His teaching includes undergraduate courses in Work Psychology and Organizational Psychology at UB, as well as doctoral-level courses at ISCTE-IUL. He has coordinated multiple research projects addressing emotional influences on motivation, workplace well-being, and dynamic motivational processes. Research interests span work motivation dynamics, affective events theory, and methodological innovations in organizational research. His work emphasizes within-person change analysis and nonlinear approaches. He has secured funding from EU institutions, Spanish government agencies (e.g., Ministerio de Ciencia), and private partners like Agbar. Supervised doctoral theses include studies on cognitive-affective engagement trajectories and academic performance profiles. Notable collaborative projects include the EVENTS initiative (2022-2023) exploring work context adaptations and the MYDE license agreement (2014-2019) for motivational assessment tools. His research integrates quantitative methodologies with complex systems perspectives to address contemporary workplace challenges.
Xavier Puig is an Assistant Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the Department of Statistics and Operations Research and the School of Mathematics and Statistics (FME). He is a member of the ADBD - Analysis of Complex Data for Business Decisions and GRBIO - Biostatistics and Bioinformatics Research Group . His research focuses on Bayesian data analysis , with applications in Epidemiology Ecology Public health Political science Industrial quality control Marketing analytics Recent publications reveal a strong trend in Bayesian spatiotemporal modeling for health data, alcohol-migraine interaction studies, and industrial error rate monitoring . His work combines methodological innovation with real-world applications across diverse sectors. Collaborations include researchers from biostatistics, clinical epidemiology, and industrial engineering. He has contributed to 44 indexed journal articles and participated in 49 congress presentations , with recent projects focusing on competitive research and non-competitive industrial collaborations in statistical modeling.
Jose David Martin Guerrero is a Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is conducted within the Intelligent Data Analysis Laboratory (IDAL), focusing on the intersection of quantum computing, machine learning, and biomedical applications. His research interests span Quantum Machine Learning , Reinforcement Learning for Quantum Control , Deep Learning for Medical Image Analysis , and Long-COVID Symptom Trajectory Modeling . He applies advanced AI techniques to solve complex problems in physics, healthcare, and engineering, particularly in modeling prostate cancer, hemodialysis, and post-COVID neurological and functional impairments. The recent publications highlight a strong trend toward integrating quantum computing with classical machine learning, especially in developing quantum neural networks, quantum autoencoders, and reinforcement learning for quantum gate optimization. Simultaneously, a significant body of work uses clustering, deep learning, and Bayesian modeling to analyze longitudinal clinical data from multicenter studies like LONG-COVID-EXP, aiming to understand symptom persistence and recovery patterns in post-COVID patients. Dr. Martin Guerrero earned his PhD from the Universitat de València in 2004 with a thesis on unsupervised techniques for web trend detection and collaborative filtering-based recommendation systems, supervised by Dr. Emilio Soria Olivas and Dr. Paulo Jorge Gomes Lisboa. His work demonstrates active collaboration across disciplines, particularly in clinical research involving multicenter studies, and in quantum computing applications in physics and finance. While no specific grants are mentioned, his involvement in large-scale clinical studies and quantum computing research suggests significant project leadership and funding acquisition. He leads research within the Intelligent Data Analysis Laboratory (IDAL) , which focuses on self-organizing maps, clustering, learning vector quantization, and their applications in security, healthcare, and quantum systems.
Beniamino Accattoli is a Researcher in Computer Science at Inria , where he has been a member of the PARTOUT team since January 2015. His work centers on higher-order computation, particularly the lambda calculus, connecting theoretical mathematics with practical implementations of functional languages. Current Projects : CANofGAS (2022–2025), COCA HOLA (2016–2021). Recent Events : Program Committee member for LSFA 2025, ICFP 2024, and FSCD 2024; lecturer at MPRI 2023–24. Research Themes : Internal : Developing mathematical theories of sharing in lambda calculus to enhance efficiency in functional language implementations. External : Establishing reasonable cost models for time and space in lambda calculus, resolving long-standing open problems in computational complexity. Scientific Awards : Distinguished Paper Award at ICFP 2022. Best Paper Award at RTA 2013. Academic Contributions : Co-inventor of the Linear Substitution Calculus , a canonical framework for higher-order computation with sharing. Proved polynomial equivalence between lambda calculus and Turing machines for time complexity with Ugo Dal Lago. Refuted token-machine conjectures and introduced environment-based machines for space complexity with Ugo Dal Lago and Gabriele Vanoni.
Antonio Calomarde Palomino is an Associate Professor at the Department of Electronic Engineering, Polytechnic University of Catalonia (UPC), located at the Higher Technical School of Industrial Engineering of Barcelona. He holds a B.S. in Telecommunications Engineering (UPC, 1986), an M.S. in Electronic Engineering (Universitat Autònoma de Barcelona, 1993), and a Ph.D. in Electronic Engineering (UPC, 2007) with honors. His research focuses on low-power/high-performance digital circuits and soft-error resilient designs, addressing challenges in semiconductor technology and radiation-hardened electronics. Key research areas include FinFET-based memory cells, RRAM modeling, and radiation mitigation strategies for advanced nanoelectronics. He contributes to the High Performance Integrated Circuits and Systems Design (HIPICS) Group, advancing circuit design methodologies and educational initiatives such as integrating autonomous vehicle systems into undergraduate curricula. His work bridges theoretical advancements with practical applications in automotive electronics and embedded systems. Publications span topics from memristor-based associative memory to approximation techniques in object detection systems, reflecting a balance between fundamental research and applied engineering. His recent work emphasizes variability-aware design and energy-efficient architectures, as seen in 2023-2024 studies on RRAM models and automotive case studies. Calomarde actively organizes academic events like the 2022 ETS conference and develops educational materials for digital electronics and audiovisual systems courses. His contributions to both technical research and pedagogical innovation solidify his role as a key figure in electronic engineering education and innovation.
Liviu Ignat serves as a Visiting Professor at the University of Deusto, actively contributing to the Deusto Center for Computational Mathematics (DeustoCCM) in Bilbao, Spain. His work bridges theoretical mathematics with computational applications, focusing on advanced numerical methods for complex physical systems. His academic credentials include: PhD in Mathematics (2006) from Universidad Autonoma de Madrid, Spain Master's Degree (2003) from Universidad Autonoma de Madrid, Spain Bachelor in Mathematics (2001) from University of Craiova, Romania Ignat's research spans partial differential equations, evolution equations, and Fourier analysis, with recent emphasis on asymptotic properties of diffusion processes on quantum graphs. This work addresses fundamental challenges in mathematical physics through innovative numerical frameworks, particularly examining how diffusion behaves on networked structures. His methodologies combine rigorous analytical techniques with practical computational implementations. His 2025 publications demonstrate a concentrated effort on finite element approximations of critical functional analysis constants, establishing optimal convergence rates for the Hardy and Sobolev constants. These contributions advance computational mathematics by providing sharper error estimates and theoretical foundations for numerical solutions in unbounded domains. No information is available regarding student supervision or research grant funding in the provided materials. As an integral member of the DeustoCCM research team, Ignat collaborates on cutting-edge computational mathematics projects, recently presenting seminar work on constant approximations at the center's April 2025 event.
Emilio Martín Gutiérrez is a Professor of Medieval History at the University of Cadiz , Spain. His work focuses on rural landscapes and society-environment interaction in Western Andalusia during the 13th to 15th centuries . He is affiliated with the Institute of Viticulture and Agri-Food Research (IVAGRO) and leads the HUM182 Medievalism of Cadiz research group. PhD in Medieval History (University of Cadiz, 2002) Specializes in Agrarian History , Landscape History , and Peasant History Active in Archaeology and Climate History research His 15 most recent publications (2003-2025) examine: Environmental conflicts in medieval cities Tidal mill ecosystems Linen retting pollution Coastal governance systems Wine landscape evolution Salt exploitation dynamics Articles frequently employ documentary analysis , archaeological evidence , and comparative environmental studies . Key scientific contributions include: Establishing the Riparia Project framework Documenting 15th-century resource management Mapping Genoese commercial networks
Yanlei Diao is a Professor of Computer Science at Ecole Polytechnique in France and also holds a professorship at the University of Massachusetts Amherst. She joined Ecole Polytechnique in September 2015 and leads the CEDAR research team focusing on Rich Data Exploration at Cloud Scale. Her work bridges theoretical computer science with practical big data systems that address real-world challenges in data analytics. Professor Diao's research spans big data analytics, scalable intelligent information systems, and cloud data processing infrastructure. Her work emphasizes practical solutions for explainable anomaly detection, interactive data exploration, and uncertain data management. She has pioneered systems like UDAO (a next-generation optimizer for cloud analytics), EXAD (explainable anomaly detection), AIDEme (interactive data exploration), and GESALL (genomic scalable analysis) that have influenced both academia and industry. Her recent publications reveal a strong focus on making big data analytics more explainable, efficient, and accessible. She has developed frameworks for unsupervised anomaly detection across heterogeneous domains, created benchmarks like Exathlon for evaluating explainable anomaly detection systems, and advanced human-in-the-loop approaches for interactive database exploration. Her work consistently bridges theoretical foundations with practical implementations in distributed systems. Selected Awards: ERC Consolidator Award (2017-2023) for "Charting a New Horizon of Big and Fast Data Analysis through Integrated Algorithm Design" CRA-W Borg Early Career Award (2013) NSF CAREER Award (2008) IBM Innovation Award on Scalable Data Analytics (2010) Professor Diao actively mentors PhD and Master's students, with former students now holding positions at top technology companies including Google, Facebook, Amazon, Netflix, and Huawei. Her research is supported by diverse funding sources including the European Research Council, National Science Foundation, ANR, and industry partners like Google, IBM, and Alibaba. She serves as PC Co-Chair of PVLDB 2025-2026 and has delivered keynotes at major industry events including Amazon Machine Learning Workshop (2024), SWIFT AI Forum (2023), and Berlin Institute for the Foundations of Learning and Data (2022). Her CEDAR research team at Inria/LIX develops cutting-edge technologies for big data analytics, with current projects focusing on foundation models for big data, explainable AI for anomaly detection, and genomic data analysis at scale. The team maintains strong collaborations with industry partners including Alibaba Cloud, where joint work has led to significant publications at top database conferences.
Jose Vicente Frances Villora is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is an active researcher in digital systems and biomedical signal processing, with a strong focus on real-time embedded systems and hardware acceleration. His research interests include: Biomedical Signal Processing, particularly ECG and microelectrode recordings FPGA-based hardware implementation of neural networks and real-time algorithms Machine learning applications in healthcare, including arrhythmia detection and Parkinson’s disease treatment Neuromorphic computing and spiking neural networks Digital signal processing in embedded and real-time systems The analysis of his recent publications (2017–2015) reveals a consistent focus on leveraging machine learning—especially KNN and neural networks—for clinical applications such as ventricular fibrillation detection and deep brain stimulation. A significant portion of his work emphasizes hardware implementation on FPGAs, aiming to achieve real-time performance and low resource consumption in medical and robotic systems. He has contributed to educational materials, including textbooks and OCW resources, and supervises academic theses. While no specific scientific awards are mentioned in the provided text, his sustained publication output and integration of theory with hardware systems underscore his technical expertise. He advises students in thesis work and has supervised doctoral research, though specific student names are not listed. His research is supported by practical applications in medical diagnostics and robotics, and he is affiliated with the GPDD (Group for Digital Design and Processing), which focuses on digital system design and processing. He is actively involved in laboratory development, including a remote laboratory for industrial robots, and his team works on co-design environments for neural network hardware, FPGA acceleration, and real-time biomedical systems.
María Margarita Zango Pascual serves as a Collaborating Professor at Pablo de Olavide University in Seville, Spain, within the Department of Physical, Chemical and Natural Systems specializing in Environmental Technologies. Her academic profile centers on the critical intersection of natural hazards, disaster risk reduction frameworks, and human rights protections, with particular expertise in seismic risk assessment and environmental governance mechanisms applicable to vulnerable regions globally. She earned her PhD from Pablo de Olavide University in 2011 with doctoral research titled "The integrated management of natural risks within the framework of third-generation human rights. The case of the effects induced by seismic activity in El Salvador (Central America)", supervised by Dr. Guillermina Garzón Heydt, Dr. José Jesús Martínez Díaz, and Dr. Eva Martínez-Sampere. This foundational work established her trajectory in examining how geological expertise interfaces with legal frameworks and human rights obligations. Professor Zango Pascual's research program demonstrates exceptional breadth across Natural Hazards, Disaster Risk Reduction, Environmental Geology, Seismic Risk, Environmental Law, and Ethics in Geosciences. Her scholarship consistently investigates the integration of scientific knowledge with ethical considerations in disaster management systems, emphasizing community resilience building and the protection of human rights in geologically vulnerable contexts like Central America. She critically examines governance structures, policy implementation gaps, and the role of geoscientists in translating technical knowledge into actionable risk reduction strategies. Analysis of her 15 most recent publications reveals an evolving research trajectory from technical hazard assessment toward increasingly sophisticated examinations of legal-ethical dimensions in disaster contexts. Early works focused on geological case studies including the Biescas camping disaster and El Salvador landslide events following Tropical Storm Stan. Recent scholarship explores macrocriminality in environmental disasters, forensic geosciences applications, and the critical role of ethics in geological practice. A unifying thread throughout her career examines how environmental governance frameworks can better incorporate geological expertise while upholding human rights standards, particularly evident in her 2024 analysis of asbestos exposure disasters and 2023 work on natural risk management as a human rights imperative. While specific details of her current advising load and grant funding remain undocumented in available sources, her extensive publication record spanning two decades and her role as doctoral supervisor indicate sustained engagement in research mentorship. Her work consistently bridges theoretical frameworks with practical applications in disaster risk reduction, suggesting active participation in interdisciplinary research initiatives and policy development processes focused on improving governance through science-technology integration.
Carlos Javier Gómez Ariza is a Professor in the Department of Psychology at the University of Jaen, Spain, specializing in cognitive and educational psychology. His research integrates neuromodulation techniques with memory studies to explore fundamental cognitive mechanisms and their educational applications. He earned his Doctorate from the University of Granada in 2003 with the thesis "Resistencia a la interferencia durante la recuperación: un estudio evolutivo" (Resistance to interference during retrieval: an evolutionary study), supervised by Dr. María Teresa Bajo Molina. His academic career centers on the Department of Psychology within the University of Jaen's institutional framework. Dr. Gómez Ariza's research spans memory inhibition , active forgetting mechanisms , and neuromodulation applications using transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS). His work investigates how brain stimulation modulates prefrontal and temporal lobe functions during semantic processing, false memory formation, and cognitive control. Key contributions include establishing causal roles of specific brain regions in retrieval-induced forgetting and developing educational interventions based on retrieval practice. Analysis of his 15 most recent publications reveals a dominant focus on prefrontal-temporal network interactions during memory tasks, with increasing application of tDCS/tACS to modulate default mode network connectivity. His research bridges basic cognitive neuroscience with practical educational contexts, particularly through school-based studies on retrieval practice and concept mapping. He actively collaborates with the "Development and Research in Education in Andalusia" research group, focusing on translating cognitive principles into educational strategies while maintaining strong ties to cognitive neuroscience methodologies.
