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Rafael Sebastian is a Full Professor at Universitat de Valencia and General Director for Science and Research of the Generalitat Valenciana. He leads the Computational Multiscale Simulation Lab (CoMMLab) and collaborates with institutions like Oxford University and Yale University. Department of Computer Science, Universitat de Valencia CoMMLab Founder Spanish Network of Excellence in Cardiac Modeling His research focuses on multi-scale computational models and artificial intelligence for patient-specific cardiac simulations , aiming to improve arrhythmia risk stratification and therapy planning . Key topics include cardiac conduction system modeling , scar-related ventricular tachycardia , and machine learning pipelines for clinical applications. Recent publications emphasize automata-based simulations for atrial arrhythmias, machine learning in arrhythmia localization, and 3D geometric characterization of aortic diseases. Trends show integration of computational modeling with clinical data and medical imaging . Scientific Awards: Best Poster Award, Functional Imaging and Modeling of the Heart (2021) Cum Laude Award, SPIE Medical Imaging (2009) Student Presentation Award (2011) He has supervised 7 PhD/Master students and led grants exceeding €1 million, including projects like iSARC-GENETICS and iCardioTwins , focusing on digital twin technology and cardiac disease stratification .

• Cardiac Computational Modeling
• Artificial Intelligence in Medicine
• Medical Imaging
• +4 more

Bogdan Kulynych is a research scientist at Lausanne University Hospital in Switzerland, working within the Clinical Data Science group. He holds a Ph.D. in Computer Science from EPFL (Switzerland), where he was advised by Carmela Troncoso, and a B.Sc. in Applied Mathematics from Kyiv Mohyla Academy in Ukraine. His academic journey also includes a visiting fellowship at Harvard University with Flavio du Pin Calmon, and internships at Google and CERN. His research spans three interconnected domains: Algorithmic Accountability, Verification, and Reliability; Privacy-Preserving Learning and Statistics; and Algorithmic Systems in Healthcare. Kulynych develops methods for obtaining practical guarantees on model stability, robustness, and reliability, while also auditing these properties. His privacy work focuses on systems ensuring practical privacy guarantees with legally legible and interpretable operational risk analyses. In healthcare, he critically studies algorithmic system deployment in clinical practice through collaboration with clinicians and medical informatics practitioners. Kulynych's publication record demonstrates significant impact in top venues including NeurIPS, ICML, ICLR, FAccT, and PETS. His recent work addresses fundamental questions in differential privacy, operational privacy metrics, and healthcare AI applications. His research trend shows increasing focus on translating theoretical privacy guarantees into practical healthcare settings while addressing the social implications of algorithmic systems. Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy (NeurIPS 2025) (ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees (2025) Attack-Aware Noise Calibration for Differential Privacy (NeurIPS 2024) As an active member of the academic community, Kulynych regularly presents at major conferences and seminars, including recent talks at Harvard Privacy Tools Seminar, NeurIPS, and Imperial College London. His work has received media attention in The Guardian, Wired, The Verge, and CNET regarding algorithmic bias challenges. Kulynych maintains an active presence on Bluesky (@bogdankulynych) where he engages in critical discussions about AI ethics, privacy, and the societal implications of technology.

• Privacy
• Security
• Algorithmic Accountability
• +5 more

Massana Raurich, Joaquim is a Lecturer in the Department of Electrical, Electronic and Automatic Engineering at the University of Girona, Spain. His academic and research activities are centered on intelligent systems, with applications in smart cities, energy forecasting, and healthcare technologies. He is an active member of the eXiT Research Group (Control Engineering and Intelligent Systems), contributing to cutting-edge interdisciplinary projects such as HIT2GAP and PEPPER. Research Interests: Smart Cities and Urban Energy Modeling Short-Term Load Forecasting in Non-Residential Buildings Artificial Intelligence in Healthcare Insulin Recommender Systems for Type 1 Diabetes Multivariate Statistical Monitoring Data-Driven Control Systems His recent publications reflect a strong trend in applying machine learning and data science to both energy systems and medical decision support, particularly in personalized healthcare. The integration of AI in diabetes management and energy efficiency platforms demonstrates a consistent focus on real-world, impactful applications. Scientific Contributions: Published in high-impact journals such as IEEE Communications Surveys & Tutorials, Energy and Buildings, and Expert Systems with Applications Contributed to European research projects including H2020-funded initiatives Developed public software for mental health and nervous system disorder detection using EEG data Delivered lectures and talks to promote engineering vocations in high schools Active participant in international conferences on AI and smart systems Advising and Grants: While no formal students are listed, he has supervised final degree projects and guided students in internships and laboratory practices. He has participated in multiple competitive research grants, including national (RTC-2017-6701-1) and European projects (H2020-EeB-2015 –680708, Grant Agreement 689810), demonstrating strong grant acquisition and collaborative research capabilities. Laboratories and Research Teams: He is a core member of the eXiT Research Group at the University of Girona, which focuses on Artificial Intelligence and Machine Learning applied to medicine and healthcare. The group develops intelligent systems for personalized decision support, energy monitoring, and adaptive control, operating at the intersection of engineering, computer science, and clinical applications.

• Smart Cities
• Energy Forecasting
• Artificial Intelligence
• +7 more

Dr. Amparo Monfort is a researcher at the Institut de Recerca i Tecnologia Agroalimentàries (IRTA) and affiliated with the Centre de Recerca en Agrigenòmica (CRAG) . She specializes in Rosaceae genomics with a focus on strawberry and melon species. Genomics and Biotechnology Program member Expert in resistance gene mapping and functional genomics Research Interests : Genetic analysis of disease resistance (powdery mildew, anthracnose) Development of genomic tools for polyploid crops Functional characterization of fruit quality traits Comparative genomics across Rosaceae species Transposable element analysis in plant genomes Key Publications Trends : Focus on GWAS for strawberry resistance, SSR marker development, resistance gene homologs in melon, and polyploid genomic resources. Works bridge Arabidopsis model systems with applied crop improvement. Scientific Contributions : Development of NIL collections for trait analysis Creation of high-density SNP arrays for strawberry Elucidation of melon resistance gene clusters Advancing polyploid genomic prediction models Collaborative Networks : Works with international teams across Spain, Italy, USA, and Sweden. Collaborates on projects involving strawberry, melon, peach, and Rubus species.

• Rosaceae Genomics
• Plant Genetics
• Fruit Crop Improvement
• +3 more

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

• Machine Learning
• Kernel Methods
• Neural Networks

Cristina España i Bonet is a Professor in the Department of Computer Science at the Polytechnic University of Catalonia (UPC), working within the Natural Language Processing group (GPLN) of the Center for Technologies and Applications of Language and Speech (TALP). She holds a Physics degree and a PhD in Cosmology from the University of Barcelona, later transitioning to Natural Language Processing and Machine Translation. Her academic journey includes teaching at both the Faculty of Physics of the University of Barcelona and currently at the Barcelona School of Informatics of UPC. Her research spans multiple areas of computational linguistics with a strong focus on Machine Translation. She has extensive experience in statistical and hybrid translation systems, document-level translation, multilingual systems, and machine learning applications in NLP. Her work often addresses real-world challenges with diverse text genres including news, patents, Wikipedia articles, and social media content. She has made significant contributions to developing translation systems that leverage context beyond the sentence level to improve coherence and quality. Cristina has been actively involved in numerous European and national research projects including OPENMT, OPENMT2, MOLTO, and TACARDI, where she has contributed both research and project coordination. Her recent work shows a growing interest in sign language translation, low-resource language processing, and the intersection of large language models with traditional machine translation paradigms. She has supervised multiple PhD and Master's students in topics related to machine translation and multilingual systems. Member of the Natural Language Processing group (GPLN) at TALP Research Center Supervisor of doctoral and master's theses in NLP and Machine Translation Lead researcher in multiple EU-funded projects on multilingual translation Developer of resources and tools for Wikipedia-based multilingual corpora Her research has evolved from statistical machine translation to incorporate neural approaches while maintaining focus on document-level context and multilingual applications. She has made significant contributions to understanding translation artifacts, developing methods for low-resource language translation, and creating resources for sign language processing. Her work bridges theoretical advances with practical applications across diverse language pairs and domains.

• Natural Language Processing
• Machine Translation
• Statistical Machine Translation
• +4 more

Jordi Petit Silvestre is a professor in the Department of Computer Sciences at the Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC). He is a member of the ALBCOM research group, focusing on algorithms, bioinformatics, complexity, and formal methods. His work bridges theoretical computer science with practical applications in algorithm engineering, VLSI design, and computer science education. His research interests include algorithms , graph layout problems , parallel computing , data structures , and computer science education . He has made significant contributions to the design and analysis of efficient algorithms, particularly in graph layout optimization and cache-conscious data structures. His work extends to electronic design automation, with publications on macro placement, routing, and physical design for integrated circuits. The recent publications highlight a dual focus: (1) algorithmic and physical design for VLSI, involving macro placement, routing, and system-level optimization; and (2) innovations in computer science education, particularly through the Jutge.org platform, continuous assessment, MOOCs, and feedback mechanisms in online judges. This reflects a career deeply engaged in both theoretical research and educational technology. Jordi Petit has participated in numerous competitive and non-competitive R&D+i projects, often in collaboration with other leading researchers at UPC. He has contributed to educational innovation projects aimed at transforming programming education and curriculum design in computer science degrees. He is actively involved in the development and deployment of Jutge.org , an educational programming judge that supports automated assessment and learning in programming courses. His work in this area includes improving feedback, integrating formal verification, and analyzing course evolution through metrics like pass rates and workload.

• Algorithms
• Theoretical Computer Science
• Graph Layout Problems
• +8 more

Gustavo Ariel Patow is a Full Professor (Catedrático) at the Department of Computer Science, Applied Mathematics and Statistics, University of Girona, Spain, where he leads the Geometry and Graphics Group (GGG) within the ViRVIG Research Center. He earned his PhD from the Universitat Politècnica de Catalunya, supervised by Xavier Pueyo and Alvar Vinacua, with a focus on Inverse Rendering Problems in Computer Graphics. Following a major health event in 2017 (cardiac arrest), he transitioned his research focus from Computer Graphics to Computational Neuroscience, leveraging his computational expertise to study brain function and disease. His research spans Computational Neuroscience (whole-brain dynamics, Alzheimer's modeling, brain disorders) and Computer Graphics (rendering techniques, procedural modeling). Recent work demonstrates strong interdisciplinary integration between these domains, particularly in: Whole-brain network modeling of neurological conditions Biophysical simulations of Alzheimer's pathology Advanced rendering for medical visualization Computational frameworks for cognitive states He actively advises doctoral candidates including Facundo Roffet, Albert Juncà, David Aquilué Llorens, and Kenneth Alonso Muñoz, and has supervised over 20 Master's/PhD students. As coordinator of UdG's Videogame Development and Design degree, he bridges academic research with applied technology training.

• Computational Neuroscience
• Computer Graphics
• Rendering Techniques
• +3 more

Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +6 more

Miguel Hernández is a Professor at the Miguel Hernández University, affiliated with the Department of Statistics, Mathematics and Computer Science. He contributes to multiple academic programs and research groups, including the Joint Research Unit in Advanced Statistical Methods in Health Sciences UMH-FISABIO. Teaches courses in Business Statistics, Clinical Medicine Research, and Translational Neuropsychopharmacology Member of the Faculty Sector 1 and the Institute Center for Operational Research His research interests center on statistics and operational research, with applications in health sciences and advanced data analysis techniques. He actively participates in teaching activities such as machine learning and deep learning with Python, and has been responsible for subjects like Process Improvement and Simulation of Processes and Systems since 2023/2024 academic years.

• Statistics
• Operational Research
• Machine Learning
• +3 more

Sebastian Padó is a Professor of Computational Linguistics and Chair of Theoretical Computational Linguistics at the University of Stuttgart's Institute for Natural Language Processing (IMS). He leads the research group focusing on developing methods and software to enable computers to understand language meaning, with expertise in computational semantics, digital humanities, and computational social science. His work bridges linguistic theory and practical NLP applications, including political discourse analysis and model interpretability. Research Interests: Meaning representations, cross-lingual models, derivational morphology, computational social science, fairness in NLP. Affiliations: Institute for Natural Language Processing (IMS), University of Stuttgart; Faculty 05. His research spans developing semantic frameworks, analyzing political discourse networks, and exploring historical texts. Recent work includes computational analysis of character archetypes in Spanish literature and cross-lingual evaluations of political opinions in LLMs. Publications (2025 focus): 15+ articles on topics ranging from computational literary studies to machine learning in low-resource settings. No scientific awards explicitly listed, but contributions to key conferences like ACL and EMNLP are notable. Advising/Grants: Active in guiding research projects and collaborating on interdisciplinary initiatives, though specific grant details are not provided here. Labs/Teams: Theoretical Computational Linguistics group at IMS, focusing on semantic analysis and NLP applications in social sciences.

• Computational Linguistics
• Natural Language Processing
• Computational Social Science
• +3 more

Abdelhamid Tayebi Tayebi is an Associate Professor in the Department of Computer Science at the University of Alcalá, Spain. His research focuses on computer science, artificial intelligence, antenna design, numerical simulation techniques, and educational technology. He leads the Climate Physics Group (CPG) and the Group of Advanced Numerical Techniques (GTNA), specializing in interdisciplinary projects combining computational methods with real-world applications. He earned his Ph.D. in 2011 with a thesis on antenna design methodologies using the method of moments, supervised by Dr. Manuel Felipe Cátedra Pérez and Dr. Iván González Diego. His work bridges theoretical research with practical tool development, including web-based simulators for antenna optimization and gamified educational platforms. Key research areas include 3D reconstruction using OpenStreetMap and LiDAR, medical imaging classification challenges, and wireless network propagation modeling. He actively explores gamification strategies to improve student engagement in STEM education, particularly in programming courses. His publications span disciplines from antenna engineering to AI-driven healthcare solutions, demonstrating a commitment to both technical innovation and pedagogical excellence. He collaborates on projects such as the EDISON initiative for data analytics and contributes to open-source tools like the NewFasant Suite.

• Computer Science
• Artificial Intelligence
• Antenna Design
• +5 more

Maria Garcia de la Banda is a distinguished Professor at Monash University's Faculty of Information Technology, where she serves in the Department of Data Science and Artificial Intelligence (DSAI). With over 25 years of academic experience, she has held significant leadership roles including Deputy Dean (Research) until July 2022, overall Deputy Dean of the Faculty (2013-2016), and Head of the Caulfield School of Information Technology (2009-2011). She is currently a member of the ARC College of Experts and Co-Chair of the Monash-Woodside FutureLab. Her educational background includes a Doctor of Philosophy in Computer Science from the Universidad Politecnica de Madrid (awarded July 7, 1994) and an Ingeniero Informatico degree from the same institution (awarded March 1, 1992). Her PhD received the university's Best PhD Award. Garcia de la Banda's research spans multiple disciplines with a strong focus on constraint programming, combinatorial optimization, program analysis, and bioinformatics. She leads the Optimization research group within DSAI and has made significant contributions to declarative programming languages, parallelism, and automatic parallelization. Her interdisciplinary work bridges computer science with biological applications, particularly in protein structure analysis and computational drug design. Her publication record shows consistent contributions across constraint programming, optimization, and bioinformatics. Recent work demonstrates increasing interdisciplinary collaboration, with a notable expansion into bioinformatics applications alongside her core constraint programming research. She has maintained a strong presence at major conferences like CP (International Conference on Principles and Practice of Constraint Programming) while also building impactful industry collaborations. Her scientific recognition includes: Logan Fellowship (1997) - the first and only prestigious award of its kind in the Faculty of IT International Constraint Modelling Challenge winner (2005, with Peter Stuckey) Universidad Politecnica de Madrid's Best PhD Award (1994) Induction into the Monash Honour Roll (2021) Vice-Chancellor's Diversity and Inclusion Award (2020) As a research leader, Garcia de la Banda has secured over $20M in industry funding and $14M in nationally competitive funding, including $8M as Chief Investigator in 11 ARC grants (5 as lead). She has served as Area Editor of the Journal of Theory and Practice of Logic Programming since 2010 and on the Editorial Board of the Constraints journal since 2019. Her leadership extends to professional organizations, having served on the Executive Committees of both the Association of Logic Programming (2005-2008) and the Association of Constraint Programming (2017-2020), where she was President (2019-2020). She leads the Optimization research group within DSAI and collaborates extensively across Monash University and with industry partners. Her current major projects include HARNESS (Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems), the ARC Training Centre in Optimisation Technologies, and the Building 4.0 CRC project focused on better buildings through technology. These initiatives demonstrate her commitment to translating theoretical research into practical applications with real-world impact.

• Constraint Programming
• Combinatorial Optimization
• Program Analysis
• +5 more

Rafael Jiménez López is a Full Professor in the Department of Psychology at the University of the Balearic Islands (UIB), specializing in Methodology of Behavioral Sciences. He has extensive teaching experience across undergraduate, master's, and doctoral programs, focusing on applied statistics, data mining, and evidence-based psychology. Previously, he served as Vice Dean and Head of Studies at the Faculty of Psychology from May 2007 to October 2016. Education: Psychology Degree from UIB (1998), awarded the extraordinary degree prize PhD in Psychology from UIB (2003) Dr. Jiménez's research centers on advanced statistical methodologies and data mining techniques applied across various fields. His work emphasizes developing and applying sophisticated statistical models to extract meaningful patterns from complex datasets. He has particular expertise in applying these methods to healthcare research, educational assessment, and behavioral science. His methodological contributions help bridge the gap between theoretical statistics and practical applications in substantive research areas. His recent publication record demonstrates a strong focus on applying data science techniques to healthcare interventions, particularly diabetes prevention (PREDIABETEXT project), as well as educational research and evidence-based practice. The work shows integration of traditional statistical methods with modern data mining approaches, often in collaborative, interdisciplinary research teams. Scientific Awards: Extraordinary degree prize for Psychology degree Dr. Jiménez actively supervises thesis work at the doctoral level and has directed numerous master's and bachelor's theses. His teaching portfolio includes advanced statistics, data mining, and evidence-based psychology courses. He has participated as a researcher in 5 national competitive research projects and several projects awarded by IdISBa (Balearic Islands Health Research Institute), demonstrating strong grant acquisition capabilities. He is a member of the Data Analysis (GRAD) research group at UIB, a consolidated R&D&I structure focused on advanced statistical methodologies and their applications across various disciplines. This group provides a collaborative environment for methodological innovation and interdisciplinary research.

• Advanced Statistical Methodologies
• Data Mining
• Advanced Statistical Modeling
• +4 more

Marta Ruiz Costa-Jussà is a Professor at the Faculty of Informatics of Barcelona (FIB), part of the Universitat Politècnica de Catalunya · BarcelonaTech (UPC). She is affiliated with the Department of Computer Science and holds additional teaching roles at the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). She is also the academic co-director of the postgraduate program Artificial Intelligence with Deep Learning at UPC School. Her research is conducted through key UPC research groups: Intelligent Data Science and Artificial Intelligence (IDEAI), the Center for Technologies and Applications of Language and Speech (TALP), and the Speech Processing Group (VEU). Her research focuses on advancing machine translation through deep learning, particularly in multilingual and low-resource settings. She investigates neural machine translation (NMT) systems that use intermediate mathematical representations (interlingua) to improve efficiency and inclusivity across majority and minority languages. A significant emphasis of her work is on ethical AI, aiming to detect and mitigate biases in translation systems. She is also pioneering research in automatic speech translation, a domain not yet fully mastered by major tech companies. Her recent publications reflect a strong trajectory in neural machine translation, multilingual representation learning, speech-to-speech translation, and ethical considerations in AI. The research trends show a consistent focus on inclusivity, efficiency, and robustness in language technologies, with applications spanning text, voice, and cross-lingual understanding. Scientific Awards: ERC Starting Grant (€1.5 million) for the LUNAR project Google Faculty Research Award (2019) Google Faculty Research Award (2020) She has secured significant research funding, notably the ERC Starting Grant, which supports her vision for lifelong universal language representation. Her advising likely includes graduate students in AI and NLP, though specific names are not listed. She has collaborated internationally with institutions such as LIMSI-CNRS (France), University of São Paulo (Brazil), Infocomm Research Institute (Singapore), National Polytechnic Institute of Mexico, and the University of Edinburgh (UK). She is a core member of several research teams: the IDEAI research center, TALP, and the VEU group. These labs focus on cutting-edge AI, data science, language technologies, and speech processing, providing a rich interdisciplinary environment for her work on inclusive and ethical machine translation systems.

• Machine Translation
• Natural Language Processing
• Artificial Intelligence
• +6 more