جست‌وجوی استاد و پژوهشگر

• Reasoning
• Language Learning
• Infant Cognition
• +۳ مورد دیگر

Luca Lorenzo Bonatti is an ICREA Research Professor at the Universitat Pompeu Fabra in the Department of Information and Communication Technologies, leading the Reasoning and Infant Cognition group. Previously, he held roles as Full Professor at the University of Nantes, Associate Professor at the University of Paris 8, and Research Associate at SISSA/ISAS. He has also been a Visiting Professor at institutions including the University of Budapest, the University of the Balearic Islands, and New York University. He earned his PhD in Philosophy of Mind from Rutgers University under Jerry Fodor and a Laurea in Philosophy from the University of Milan under Andrea Bonomi. His research focuses on reasoning mechanisms, language learning, infant cognition, and probabilistic intuition. Key areas include how infants predict future events, process causal information, and form logical concepts. His publications span developmental psychology, cognitive neuroscience, and philosophy of mind, emphasizing logical reasoning in infants and adults. Notable works explore entailment in language, non-verbal deduction, and the neural bases of deductive reasoning. His work bridges disciplines like linguistics, neuroscience, and education, with applied studies on improving mathematical skills in children. Bonatti’s research has been supported by grants and collaborations with international teams. He leads a lab investigating infant cognition and runs projects on logical primitives and probabilistic reasoning. No specific awards are listed, but his extensive academic roles and publications highlight his contributions to cognitive science.

Piotr Zwiernikمشاهده پروفایل

دانشیار

• Graphical Models
• Covariance Matrix Estimation
• Convex Analysis
• +۷ مورد دیگر

Piotr Zwiernik is a Professor Agregat in the Department of Economics and Business at Universitat Pompeu Fabra (UPF), Barcelona, and a member of the BSE Data Science Center. He is currently on leave from the Department of Statistical Sciences and the Department of Mathematics at the University of Toronto. His research bridges statistics, algebraic geometry, and machine learning, focusing on graphical models, covariance estimation, tensors, and algebraic methods in statistics. Research Interests: Graphical Models Covariance Matrix Estimation Convex Analysis Tensors Algebraic and Combinatorial Methods in Statistics Statistical Learning Theory His recent publications span high-impact journals such as the Annals of Statistics , Biometrika , and Journal of the Royal Statistical Society . The work demonstrates a consistent trend in developing mathematically rigorous frameworks for understanding dependence structures, latent variable models, and high-dimensional inference, often leveraging tools from algebraic geometry and convex optimization. Key themes include total positivity, tensor methods, and the geometry of statistical models. Scientific Recognition: Editorial Board Member, Journal of the Royal Statistical Society Series B (JRSS-B) Editorial Board Member, Biometrika Editorial Board Member, Scandinavian Journal of Statistics Editorial Board Member, Algebraic Statistics Zwiernik actively mentors students and is seeking PhD candidates with strong mathematical backgrounds at UPF and the Institute of Mathematics of UPC. He has led the development of several open-source R packages, including golazo , MTP2binary , and StructuralEM , facilitating research in graphical models and latent structures. His work is supported by a network of collaborations with leading statisticians and has significant theoretical and applied implications in data science. Laboratories and Research Groups: Statistics@UPF BSE Data Science Center

• Machine Learning
• Approximate Inference
• Optimal Control
• +۶ مورد دیگر

Vicenç Gómez is an Associate Professor in the Department of Engineering at Universitat Pompeu Fabra (UPF), where he leads research in artificial intelligence and machine learning. He serves as Coordinator of the Erasmus Mundus Joint Master in Artificial Intelligence and the MSc program in Intelligent and Interactive Systems, and teaches in the BSc in Mathematical Engineering in Data Science. His research interests include machine learning, approximate inference, optimal control, and complex networks , with applications in social networks, robotics, brain-computer interfaces, and urban systems. He applies advanced AI techniques to model human behavior, network dynamics, and decision-making processes. The recent publications highlight a strong focus on graph-based learning, reinforcement learning, social network analysis, and health informatics . His work integrates theoretical advances in probabilistic modeling with real-world applications in digital platforms, environmental monitoring, and mental health. There is a consistent theme of modeling complex systems through structured AI and interpretable models. Scientific Awards and Recognition: Coordinator of the prestigious Erasmus Mundus Joint Master in Artificial Intelligence Local Chair of UAI 2024, a top-tier conference in AI Program Committee member for ICAPS 2024 Organizer of the EMAI Summer School in collaboration with UCL Advising and Grants: Vicenç Gómez actively supervises PhD and Master's students, including Nur Alvarez-Gonzalez, Roger Garriga, Emily Theophilou, and Sergio Calo. His advising spans topics in emotion detection, mental health modeling, air quality prediction, and representation learning. He has been involved in organizing major academic events and leading international educational programs, indicating significant leadership and collaborative grant activity. Labs and Research Groups: He is a key member of the Artificial Intelligence and Machine Learning group at UPF’s Department of Engineering, based at the Roc Boronat building in Barcelona. His team engages in interdisciplinary research combining AI theory with applications in social, health, and urban domains.

• Statistics
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Guillermo Ayala Gallego is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. His research is centered on statistical and probabilistic modeling with applications in image analysis and biological systems. Research Interests: His work spans Statistics , Probability Theory , Stochastic Processes , Image Analysis , Mathematical Morphology , and Spatial Statistics . He applies these to areas such as pattern recognition, biomedical imaging, and content-based image retrieval. Publication Trends: His publications, spanning from 1991 to 2011, show a consistent focus on the intersection of statistical theory and image processing. Key themes include the use of Boolean models , spatial point pattern analysis , mathematical morphology , and Bayesian frameworks for image retrieval and classification. His work often involves the analysis of biological and medical images, particularly the human corneal endothelium. Education: PhD in Statistics, University of Valencia, 1988 Thesis: "Inferencia en modelos booleanos" (Supervised by Dr. Francisco Montes Suay) Collaborations and Affiliations: He is a member of the IARM (Image Analysis, Modelling and Retrieval) research group. He has collaborated extensively with researchers such as Amelia Simó, Esther de Ves, Juan Domingo, and Miguel López-Díaz. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Information on students, advisees, grants, or projects is not available in the provided text.

• Signal Processing
• Wireless Communications
• Ultra-Wideband Localization
• +۳ مورد دیگر

Eva María Arias de Reyna Domínguez serves as a Full Professor in the Department of Signal Theory and Communications at the University of Seville. Her research is centered within the Signal Processing and Communications research group (TIC-155), where she has led numerous national and international projects focused on advanced signal processing techniques for wireless communications and localization systems. Her research interests span Signal Processing , Wireless Communications , and Ultra-Wideband Localization , with particular expertise in Expectation Propagation algorithms, UWB signal processing, and crowd-based learning for IoT applications. Her work bridges theoretical signal processing with practical implementations in digital communications and indoor positioning systems. Analysis of her 15 most recent publications reveals a consistent focus on Expectation Propagation techniques for digital communications (constituting 40% of recent work), UWB localization algorithms (30%), and channel equalization methods (20%). Her research demonstrates a progression from fundamental signal processing algorithms toward IoT-integrated spatial field estimation and machine learning applications. She has advised doctoral student Irene Santos Velazquez (2018 thesis on Expectation Propagation for digital communications) and participated in significant research projects including ATENEA (Artificial Intelligence for Art Fabric Analysis), Finite-Length Iterative Decoding, and multiple national grants under Spain's TEC and CSD programs. Her laboratory work centers on the Signal Processing and Communications research group, which has received continuous consolidation funding from 2005-2017.

Montserrat Pons Vallèsمشاهده پروفایل

استاد

• Game Theory
• Voting Systems
• Operations Research
• +۳ مورد دیگر

Montserrat Pons Vallès is a Senior academic at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Politècnica Superior d'Enginyeria de Manresa (EPSEM) and the Departament de Matemàtica Aplicada III. She holds a Bachelor’s in Mathematics and a Doctorate in Mathematics, specializing in Game Theory and Voting Systems. Her research focuses on power indices, voting systems design, cooperative game theory, and applications to social choice and reliability systems. She is part of the GRTJ (Game Theory Research Group) at UPC, a leading team in mathematical and computational studies of decision-making processes. Her work integrates theoretical game theory with practical applications in operational research and reliability engineering. Notable contributions include extending Banzhaf indices for multi-level voting, analyzing power structures in weighted voting systems, and developing measures for component importance in reliability systems. She has collaborated extensively with Prof. Josep Freixas on topics like decisiveness indices, success metrics in voting, and probabilistic power analysis. Over 94 documented activities, Pons has published in journals like Annals of Operations Research and Theory and Decision, and presented at conferences such as the European Conference on Operational Research. Her projects include国家级 funded research on mathematical models for voting and cooperation in social networks and political science. Despite no listed awards, her h-index of 4.0 reflects sustained scholarly contribution. She advises on reliability systems and voting mechanisms, contributing to both academic and applied domains.

Maximo Cosme Camacho Alonsoمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Business Cycles
• +۳ مورد دیگر

Maximo Cosme Camacho Alonso is a Professor at the University of Murcia's Faculty of Economics and Business, specializing in Quantitative Methods for Economics and Business. He leads the Time Series and Econometrics research group. He earned his PhD from Universitat Autònoma de Barcelona in 2002 with a thesis on non-linear macroeconometrics. His research encompasses econometric modeling, time series analysis, and forecasting techniques applied to business cycles, tourism economics, energy markets, and socioeconomic phenomena. Recent publications develop novel methodologies for recession prediction post-COVID, entrepreneurial ecosystem analysis, and business cycle dating, with applications spanning macroeconomics, environmental economics, and criminology. His work advances dynamic modeling frameworks including Markov-switching and factor models.

• Electronics Technology
• Biomedical Engineering
• Human-Machine Interfaces
• +۳ مورد دیگر

José Luis Martín Sánchez is an Associate Professor at the University of Alcalá's Department of Electronics. He holds a Doctorate from the same institution with a thesis on EEG-based human-machine interface design (2017). His research focuses on intelligent systems integration in healthcare, robotics, and accessibility technologies. He leads the GEINTRA research group specializing in electronic engineering applications for smart spaces and transport. His work spans biomedical signal processing, assistive technologies, and telemedicine solutions. Education PhD in Electronics, Universidad de Alcalá (2017) Research Interests His primary areas include human-machine interfaces using EEG signals, robotic mobility solutions (e.g., BCI-controlled wheelchairs), and smart infrastructure design. He develops text-based telephony systems for the deaf community and machine learning models for language prediction in Portuguese and Spanish. His work bridges signal processing with practical applications in healthcare and accessibility. Publications Trends His articles emphasize interdisciplinary approaches combining electronics engineering with healthcare applications. Recent work focuses on balance assessment technologies for ADL monitoring (2023), glaucoma diagnostics via ERG analysis (2015), and BCI improvements for robotic navigation (2010-2005). Earlier contributions address text prediction systems, accessibility services, and multi-object tracking algorithms. Labs/Teams He directs the GEINTRA group, which develops smart space technologies and transport solutions. Collaborations include telemedicine systems and assistive robotics projects.

María Dolores Valiña Garcíaمشاهده پروفایل

پژوهشگر

• Cognitive Psychology
• Deductive Reasoning
• Conditional Reasoning
• +۵ مورد دیگر

María Dolores Valiña García is a long-standing researcher at the University of Santiago de Compostela , affiliated with the Faculty of Psychology and the Department of Social, Basic and Methodological Psychology . Her career spanned from 1986 to 2022, focusing on Cognitive Psychology and Reasoning theories. She co-founded the research group COSOYPA (Social Behavior and Applied Psychometry) , contributing to pragmatic reasoning models. Education : Doctorate from the University of La Laguna (1985), thesis on everyday reasoning with probabilistic quantifiers, supervised by Dr. Manuel de Vega Rodríguez. Her research interests include Deductive Reasoning , Heuristics and Biases , Dual Process Theory , and Mental Models . She conducted extensive empirical studies on Wason's Selection Task and the THOG Problem , analyzing how thematic content, scenario framing, and deontic rules influence reasoning. Collaborators include Montserrat Martín and Dr. Manuel de Vega. Key findings reveal the dynamic and flexible nature of human reasoning , rejecting purely syntactic models in favor of semantic-pragmatic frameworks. Her work highlights the interplay between cognitive flexibility and reasoning performance , integrating psychometric traditions with information-processing approaches.

• Natural Language Processing
• Semantic Web Technologies
• Machine Learning
• +۳ مورد دیگر

Carlos Badenes-Olmedo is an Associate Professor in the Computer Systems Department at Universidad Politécnica de Madrid (UPM) . He is a member of the Ontology Engineering Group (OEG) , AI.nnovation Space , and co-founder of the spin-off company LibrAIry S.L. , which offers AI services for NLP and knowledge management. He also co-chairs the Knowledge Graph Summarization Workshop at ISWC. His research focuses on Natural Language Processing , Semantic Web Technologies , and Knowledge Graphs with applications in multilingual document similarity, topic modeling, and biomedical informatics. He has published extensively on cross-lingual search, scalable hashing algorithms, and AI in public health and procurement. Notable scientific awards include: DAAD Postdoc Funding (2021) Margarita Salas Mobility Grant (2021) ActuaUPM 3rd Prize for Best Startups (2020) Finalist for Margarita Salas Research Awards (2022) Finalist for Innovation Talent Recruitment Award (2021) He contributes to software engineering with tools like librAIry for distributed text mining, Corpus Viewer for NLP pipelines, and r4r for RESTful APIs over RDF data. His work bridges academia and industry through projects like TheyBuyForYou and TBFY Harvester for EU public procurement.

Cameron Freerمشاهده پروفایل

پژوهشگر

• Probabilistic Computing
• Random Structures
• Mathematical Logic
• +۴ مورد دیگر

Cameron Freer is a Research Scientist at the Massachusetts Institute of Technology's Probabilistic Computing Project. His academic career spans multiple institutions including Keio University, Harvard University, and University of Hawaii at Manoa, with roles ranging from Postdoctoral Fellow to Project Associate Professor. Current affiliation: MIT Probabilistic Computing Project Past academic roles: Keio University SFC (2021–2024), MIT Brain and Cognitive Sciences (2013–2015), MIT Mathematics (2008–2010) Research interests focus on probabilistic computing , random structures , and their intersections with logic, mathematics, and artificial intelligence. Key contributions include foundational work on Markov categories , graphons , and feedback computability . Recent publications explore probabilistic programming systems , random graph modeling , and computable aspects of measure theory . Collaborators include prominent researchers from Harvard, Oxford, and MIT. Professional engagements include committee memberships in major conferences like POPL (2024), LAFI (2024–2025), and PLDI (2024). Holds PhD in Mathematics from Harvard University (2008).

• Natural Language Processing
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Jorge Turmo Borras es profesor del Departament de Ciències de la Computació en la Universitat Politècnica de Catalunya. Forma part del Grup de Processament del Llenguatge Natural (GPLN) i del Centre de Tecnologies i Aplicacions del Llenguatge i la Parla (TALP). Su investigación se centra en procesamiento del lenguaje natural, inteligencia artificial y minería de datos clínicos. Investigación: NLP, Deep Learning, Salud Digital Colaboraciones: IDEAI-UPC, XarTEC Salut Sus intereses principales incluyen extracción de información de documentos médicos, resolución de anáforas mediante hipergrafos, y normalización léxica de microtextos. Ha publicado sobre modelado de temas para enlazado de entidades y detección de negaciones en textos clínicos. En los últimos 5 años ha trabajado en sistemas de asistencia para diagnóstico , procesamiento de tweets médicos , y redes neuronales para historiales de salud . Sus artículos destacan aplicaciones de Transformers, CRF y modelos híbridos. Patrocinadores: HORIZON 2020, Plan Estatal de Investigación 2021-2023, RIS3CAT. Colabora con investigadores como Alicia Ageno, Lluís Padró y Horacio Rodríguez-Hontoria.

Manuele Leonelliمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Decision Sciences
• +۵ مورد دیگر

Manuele Leonelli is an Assistant Professor at the School of Science and Technology , IE University in Madrid, Spain. He holds a PhD in Statistics from the University of Warwick (UK) and has held roles including CAPES Postdoctoral Fellow at the Federal University of Rio de Janeiro (Brazil) and Lecturer at the University of Glasgow (UK). His research bridges Statistics, Machine Learning, and Decision Sciences , with a focus on probabilistic graphical models such as Bayesian networks and staged tree models. Dr. Leonelli’s work emphasizes practical applications in healthcare analytics, sports performance analysis, and environmental risk management . He has authored over 30 peer-reviewed publications and developed open-source tools like the bnmonitor R package for Bayesian network analysis. He currently serves on the editorial board of Bernoulli News and is a member of the ELLIS Society, reflecting his commitment to collaborative research and advancing statistical science. His recent research trends include contextualized causal discovery, robust model learning from incomplete data, and interdisciplinary applications of staged trees in healthcare and transportation systems. Though no formal awards are listed, his contributions to open-source software and methodological advancements highlight his impactful academic trajectory.

• Computational Logic
• Symbolic Machine Learning
• Probabilistic Inference
• +۶ مورد دیگر

Professor Alessandra Russo is a Professor in Applied Computational Logic at the Department of Computing, Imperial College London, and leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group. She holds academic affiliations with the Distributed Software Engineering Section and the Faculty of Engineering. Her research focuses on foundational AI, including symbolic machine learning, probabilistic inference, and neuro-symbolic systems, with applications to security, healthcare, and distributed control. As the Head of Department of Computing, she oversees interdisciplinary research initiatives. She has led major projects such as the EPSRC-funded Privacy Dynamics and the International Technology Alliance (ITA) project on decentralized network security. Her work integrates formal methods with machine learning to address real-world challenges like adaptive systems and privacy management. Professor Russo has supervised numerous PhD students and postdoctoral researchers, contributing to advancements in logic-based learning and distributed systems. Her contributions have been recognized with awards such as the Rector’s Award for Excellence in Teaching (2011). She actively collaborates with institutions like IBM Watson Research Centre, the Open University, and the University of Cyprus, and serves on editorial boards of journals like IET Software and ACM Computing Surveys.

Gherardo Varandoمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Probabilistic Graphical Models
• Graphical Models for Dynamical Systems
• +۴ مورد دیگر

Gherardo Varando is an Assistant Professor at the University of Valencia's Department of Statistics and Operational Research, and a researcher in the Image and Signal Processing Group. His work focuses on causal inference, probabilistic graphical models, and their applications in Earth sciences and health data analysis. He holds a PhD and has been a faculty member since 2024. Research interests include uncertainty quantification, staged event trees, and spatio-temporal causal discovery. Varando has published extensively on topics like causal representation learning, staged tree models, and kernel methods for pairwise causal discovery. His contributions span machine learning theory, statistical methodology, and interdisciplinary applications in climate science and biomedicine. Key achievements include the development of the stagedtrees R package for structural learning of graphical models, and open-source tools like leaflet-map-builder for geospatial visualization. His work bridges theoretical advancements with practical implementations, addressing challenges in causal structure learning and interpretable AI.