جست‌وجوی استاد و پژوهشگر

Juan Carlos Vidal Aguiarمشاهده پروفایل

دانشیار

• Process Mining
• Business Intelligence
• Semantic Annotation
• +۵ مورد دیگر

Juan Carlos Vidal Aguiar serves as an Associate Professor in the Department of Electronics and Computer Science at the University of Santiago de Compostela, Spain. With extensive experience in academic research and teaching, he has established himself as a prominent figure in process mining and business intelligence applications. His work bridges theoretical computer science with practical implementations across healthcare and educational domains. Dr. Vidal Aguiar earned his Bachelor Engineering degree in Computer Science from the University of La Coruña in 2000, followed by several years working as a senior IT consultant. He completed his PhD at the University of Santiago de Compostela in 2010, where he has remained as faculty since. His academic journey reflects a trajectory from foundational computer science toward specialized applications in process analytics. His research interests focus on knowledge discovery, semantic annotation, semantic modeling of workflows and services, and the application of artificial intelligence for business intelligence . Recent work demonstrates significant evolution toward healthcare applications, particularly in cardiac rehabilitation and glucose monitoring, while maintaining strong foundations in process mining techniques. His publications reveal a clear progression from theoretical workflow modeling to practical AI-driven business process solutions with real-world impact. Analysis of his publication trends shows increasing specialization in predictive process monitoring with deep learning approaches, particularly evident in his 2023-2025 work. His research spans both theoretical contributions in kernel methods and biclustering algorithms, and practical implementations like the VERONA Python library for benchmarking. The interdisciplinary nature of his work is particularly notable in healthcare applications where process mining techniques are adapted for medical contexts. Dr. Vidal Aguiar leads multiple significant research initiatives including Predictive monitoring and causality for cardiac rehabilitation, Responsible AI for Process Mining 2.0, GAMification techniques for entrepreneurial teacher development, and Soft computing for gamification analytics in cardiac rehabilitation . These projects demonstrate his ability to secure research funding across diverse domains while maintaining a cohesive research vision centered on process analytics. His research ecosystem includes collaborations with numerous colleagues including Manuel Lama, Pedro Gamallo-Fernandez, and Marcos Matabuena across various projects. The SoftLearn platform represents one of his notable contributions to educational technology, applying soft computing techniques to process mining in e-learning contexts. His work consistently bridges academic research with practical implementations that address real organizational challenges.

Cedric du Mouzaمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Digital Humanities
• +۶ مورد دیگر

Cédric du Mouza is a full Professor within the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. His research activities lie at the intersection of database systems, data mining, and digital humanities, with a strong emphasis on social network analysis, knowledge base population, and spatial/temporal data management. Education details are not explicitly provided in the source material. His research interests can be summarized as follows: Database Technologies: scalable indexing, distributed repositories, spatial and temporal data structures. Data Mining & Machine Learning: community detection, influence maximization, recommender systems. Digital Humanities: prosopographic databases, historical social networks, credibility assessment of historical sources. Social Media Analytics: Twitter user profiling, early detection of popular accounts, reducing filter bubbles. Across more than two decades, Prof. du Mouza has produced a rich scholarly record that blends theoretical advances with practical applications. Recent publications (2020-2024) highlight a methodological shift toward end-to-end evaluation frameworks for knowledge base population, uncertainty modeling in historical corpora, and real-time influence maximization in advertising ecosystems. These works are published in premier venues such as SIGIR , VLDB Journal , WISE , CIKM , and leading French conferences (EGC, BDA). No specific scientific awards, funded projects, or doctoral student names are mentioned in the supplied text. Prof. du Mouza collaborates extensively within interdisciplinary teams involving historians, computer scientists, and statisticians, reflecting CEDRIC’s integrative research culture. While no dedicated laboratory or team name is provided, his continuous affiliation with CEDRIC/CNAM confirms an active and ongoing role in both research and graduate-level education.

Haluk Bingölمشاهده پروفایل

استاد

• Complex Networks
• Evolutionary Game Theory
• Social Network Analysis
• +۵ مورد دیگر

Prof. Haluk Bingöl serves as a Professor at Yeditepe University's Faculty of Computer and Information Sciences, Department of Computer Science and Information Technologies since 2018. Previously, he held academic positions at Boğaziçi University from 2001 to 2018, progressing from Assistant Professor to full Professor in the Department of Computer Engineering. His educational background includes a PhD (1987) and MS (1985) in Electrical and Computer Engineering from Syracuse University, with doctoral research on Boolean Comparison by Simulation , and a BS in Electrical and Electronics Engineering from Middle East Technical University (1982). His research centers on Complex Networks and Evolutionary Game Theory , with significant contributions to understanding cooperation dynamics in the Iterated Prisoner's Dilemma, community detection algorithms, and epidemic modeling in urban networks. His work integrates computational modeling with real-world applications in social dynamics and information systems, often employing agent-based simulations to explore emergent phenomena. Recent publications demonstrate consistent focus on game-theoretic models of cooperation, network structure analysis, and applications in social media and epidemiology. He maintains strong publication output in high-impact journals including Physical Review E , Advances in Complex Systems , and Journal of Theoretical Biology , with increasing interdisciplinary reach into mental health informatics and computational linguistics. Multiple Publication Incentive Awards from Boğaziçi University and TÜBİTAK Interpro IT Award for industry-academia collaboration Best Poster Award at COST MP0801 United Nations Fellowship (1984-1985) and TEV International Scholarship National competition awards for student thesis supervision Prof. Bingöl has supervised 23 graduate students across master's and PhD programs, with research spanning community detection, evolutionary dynamics, and attention economics. His grant portfolio includes leadership of the TURKCELL m-learning Project ($130,000), participation in EU-funded initiatives (EUMedGrid, ASSYST), and COST actions (TD1210, MP0801) focusing on complex systems research infrastructure. He actively contributes to COST actions and collaborative networks, developing methodologies for complex systems analysis while mentoring next-generation researchers through structured academic programs and industry partnerships.

Ljubiša Bojićمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• AI Ethics
• Recommender Systems
• +۵ مورد دیگر

Ljubiša Bojić is a Senior Research Fellow at the Digital Society Lab (DigiLab) within the Institute for Philosophy and Social Theory at the University of Belgrade and concurrently at the Institute for Artificial Intelligence of Serbia. He serves as External Faculty at the Complexity Science Hub Vienna, focusing on AI's societal implications, particularly AI alignment, recommender systems, and metaverse ethics. His work bridges communication science, futurology, and digital humanism to address critical challenges in technology governance. Education: Ph.D. in Communication Science, University of Lyon II, France (2014) Research Focus: Bojić investigates how AI systems can uphold human values through ethical design, with emphasis on recommender algorithms as the most powerful social force shaping contemporary society. His pioneering work on digital addictions examines how AI-driven platforms create echo chambers and social polarization. He advocates for declaring recommender systems a public good and digital identity a human right, exploring both the addictive risks and therapeutic potentials of immersive technologies like the metaverse. Publication Trends: His recent output (2022-2025) reveals a strategic evolution from analyzing digital addiction mechanisms toward developing concrete AI governance frameworks. Key themes include linguistic pragmatics in large language models, consciousness metrics in AI, and cross-platform social dynamics. His research increasingly informs global policy through rigorous computational social science methods applied to real-world challenges like vaccine hesitancy and political polarization. Scientific Recognition: Fellow, Institute for Human Sciences Vienna (IWM) (2023) Member, United Nations Environment Programme Foresight Expert Panel (2023) Policy Engagement: Bojić co-developed Serbia's AI ethics standards and advised Spain's AI strategy. His Horizon Europe project TWON generated Policy Handout No. 8, mandating algorithms to deliver emotionally balanced, diverse content. His 'CERN for AI' framework influenced G7 policy briefs and was featured by the World Economic Forum. He regularly testifies before international bodies on AI governance. Institutional Leadership: As founder of EMERGE: Forum on the Future of AI Driven Humanity and initiator of the Belgrade Digital Freedom Pledge (signed by OSCE officials), he drives global conversations on digital rights. At DigiLab, he leads research on social media dynamics using big data analytics, while coordinating international networks like COST Action CA21129 on opinion analysis.

Maria Halkidiمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Recommender Systems
• +۶ مورد دیگر

Maria Halkidi is an Associate Professor in the Department of Digital Systems at the University of Piraeus, Greece, where she conducts cutting-edge research in data mining and machine learning with over two decades of academic experience. Her educational background includes: Bachelor's degree in Informatics from the University of Piraeus (1997) Master's degree from Athens University of Economics and Business (1999) Ph.D. from Athens University of Economics and Business (2003) Dr. Halkidi's research focuses on fundamental and applied aspects of data mining, particularly recommender systems, graph data mining, cluster validity assessment, and distributed data mining. Her work bridges theoretical frameworks with practical implementations in sensor networks, social media, and real-time analytics, contributing significantly to algorithmic development in these domains. Analysis of her recent publications reveals a strong trajectory toward fairness and diversity optimization in recommender systems, alongside innovative approaches to graph clustering quality assessment and scalable sentiment analysis. Her research consistently addresses real-world challenges in dynamic data environments, with increasing emphasis on multi-stakeholder optimization and privacy-aware recommendation frameworks. Scientific awards: No specific awards or honors were documented in the provided materials. Dr. Halkidi has participated extensively in National and European-funded research projects, including a prestigious Marie-Curie fellowship at the University of California, Riverside. She serves on program committees for major international conferences in data mining and machine learning, demonstrating active community engagement. While specific graduate students aren't listed in the source materials, her professorial role indicates ongoing mentorship of Master's and Ph.D. candidates. She maintains active research affiliations with the Network oriented systems & services lab and the DataStories research group at the University of Piraeus, where she collaborates on interdisciplinary projects involving big data analytics, social network analysis, and distributed systems.

• Data Privacy
• Database Security
• Information Systems
• +۳ مورد دیگر

Gaël Le Mahec serves as a Lecturer at the University of Picardie Jules Verne within the Networks and Data Domain, where he leads research activities in the REDO group. His office is located in room 308 with direct phone access (5912). As an active researcher in data privacy, he maintains strong contributions to both theoretical frameworks and practical implementations of anonymization technologies. Dr. Le Mahec's research program centers on privacy-preserving data publishing , with particular expertise in k-anonymity variants (l-diversity, t-closeness) and distributed anonymization systems . His work addresses the critical challenge of maintaining analytical utility while ensuring robust privacy guarantees, especially for large-scale datasets. Current projects focus on optimizing privacy-utility tradeoffs in distributed memory environments and developing novel metrics for information loss quantification. The REDO research group under his involvement has established itself as a significant contributor to data privacy literature, with consistent publications in top venues including IEEE Big Data and Transactions on Data Privacy. Their research demonstrates strong practical relevance to industry challenges in GDPR compliance and secure data sharing. Prospective collaborators should note the group's emphasis on implementable solutions that bridge theoretical privacy models with real-world system constraints. Dr. Le Mahec maintains active research supervision through the REDO group, with particular opportunities for students interested in distributed systems security and privacy engineering. His publication trajectory since 2020 indicates sustained research productivity with increasing focus on scalable implementations for enterprise environments. The group's work shows promising direction toward practical privacy solutions for big data ecosystems.

• Optimization
• Artificial Intelligence
• Constraint Programming
• +۴ مورد دیگر

Rui SA SHIBASAKI is a Lecturer at the University of Picardie Jules Verne (UPJV), France, affiliated with research unit UR 4290. Her work bridges optimization theory and industrial applications, with a growing focus on sustainable manufacturing and robust decision-making under uncertainty. She maintains active collaborations across France, Brazil, and the USA. Her research spans optimization , artificial intelligence , and operations research , specializing in constraint programming applications for assembly line balancing, network design, and energy efficiency. Recent work innovates by applying MaxSAT solvers to combinatorial problems and developing robustness metrics for production systems. Analysis of her 13 publications (2020-2025) reveals three dominant trends: (1) Energy-aware manufacturing optimization (40% of recent work), (2) Advanced decomposition techniques for network problems (30%), and (3) Diversified solution approaches for satisfiability (20%). Her research increasingly addresses sustainability through energy peak minimization in production systems. Dr. Shibasaki actively supervises graduate students and collaborates internationally with researchers from Federal University of Minas Gerais (Brazil), CNRS (France), and IBM Research (USA). Her work is supported by competitive grants enabling participation in major conferences like ROADEF and IEEE ICTAI. As part of UPJV's UR 4290 research unit, she contributes to optimization and cryptography initiatives. While no formal lab name is specified, her work centers on developing practical AI-driven optimization tools for industrial challenges, particularly in manufacturing and logistics.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +۵ مورد دیگر

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

José Ramón García Cascalesمشاهده پروفایل

استاد

• Thermodynamics
• Heat Pump Technology
• Refrigeration Engineering
• +۳ مورد دیگر

José Ramón García Cascales is a Professor at the Polytechnic University of Cartagena specializing in thermal engineering and sustainable energy systems. His research focuses on advancing CO 2 -based heat pump technology for domestic and industrial applications, with particular expertise in transcritical cycles and mechanical subcooling systems. His primary research interests include: Thermodynamics of alternative refrigerants Numerical modeling of heat transfer systems Experimental characterization of CO 2 heat pumps Domestic hot water production optimization Sustainable heating and cooling solutions Transient thermal system behavior Analysis of his 15 most recent publications (2021-2025) reveals a consistent research trajectory centered on CO 2 heat pump optimization. Key trends include mechanical subcooling strategies, gas cooler pressure control, and integration with thermal storage systems. His work combines high-fidelity numerical modeling with experimental validation across diverse operating conditions, demonstrating significant contributions to improving energy efficiency in residential and commercial heating applications. No scientific awards or notable grants were explicitly mentioned in the source material. Similarly, information regarding student supervision, laboratory facilities, or collaborative teams was not provided in the available documentation.

Tatyana Sharpeeمشاهده پروفایل

استاد

• Computational Neuroscience
• Information Theory in Neural Systems
• Sensory Processing
• +۷ مورد دیگر

Tatyana Sharpee is a Professor at the Salk Institute for Biological Studies, where she leads the Computational Neurobiology Laboratory. She holds the Edwin K. Hunter Chair and is affiliated with the Crick-Jacobs Center for Theoretical and Computational Biology and the Center for Theoretical Biological Physics at UCSD. Her research focuses on understanding how the brain processes information despite constant changes in its components and environment. Dr. Sharpee's research interests center on computational principles of sensory processing, particularly how the brain creates robust representations of the external world. Her work integrates methods from statistical physics, mathematics, and information theory to study neural coding in vision, audition, and olfaction. She has made significant contributions to understanding how neural feature selectivity is shaped by natural stimuli statistics and how invariant representations are achieved despite neural variability. Her recent publications reveal groundbreaking discoveries in odor perception (structured by a hyperbolic metric similar to Pringles potato chips), visual processing (how neurons combine selectivity for shape and texture), and theoretical frameworks for understanding neural coding. Her lab's work on Maximally Informative Dimensions has revolutionized how scientists analyze neural responses to complex stimuli. 2024 Prebys Research Hero 2022 Delano Prize in Computational Biology from ASBMB American Physical Society Fellow, 2018 National Science Foundation Career Award, 2013 WM Keck Foundation Research Excellence Award, 2009 2009 McKnight Scholar Award Ray Thomas Edwards Foundation Career Award, 2008 Alfred P. Sloan Research Fellow, 2008 Dr. Sharpee mentors numerous graduate students and postdoctoral fellows, and her lab develops open-source computational tools widely used in neuroscience. Her research has significant implications for understanding neurological disorders including autism, Alzheimer's disease, schizophrenia, depression, and anxiety, with potential applications in brain-machine interfaces and novel treatments for sensory disorders.