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Chunlei WuView profile

Professor

• Biomedical Data Integration
• Semantic Knowledge Representation
• Cloud Computing in Biomedicine
• +4 more

Chunlei Wu is a Professor at The Scripps Research Institute in the Department of Integrative Structural and Computational Biology. He leads a team developing large-scale biomedical data integration systems and tools, focusing on applying data science and cloud computing to advance biomedical discovery. His work emphasizes semantic knowledge representation, API-driven resource discovery, and open science infrastructure. Affiliations: Scripps Research Institute (2021–Present: Tenured Professor; 2017–2021: Associate Professor) Education: PhD in Biomathematics/Biostatistics (2006, UT Health Science Center Houston), B.S./M.S. in Biochemistry (1997/2000, Nanjing University) Research Interests: Wu’s lab creates foundational tools like MyGene.info, MyVariant.info, and BioThings SDK, which serve as scalable APIs for querying gene, variant, and chemical data. These platforms support drug discovery, genomic analysis, and pandemic response (e.g., Outbreak.info for SARS-CoV-2 surveillance). His work bridges data interoperability challenges through semantic integration and FAIR principles. Awards: Recognized for contributions to biomedical informatics, including the 2017 ESWC Best In-Use Paper Award and hackathon victories for API development. His tools are widely adopted, with MyGene.info processing ~30M monthly API requests. Labs/Teams: A multidisciplinary team includes senior scientists (e.g., Ginger Tsueng), programmers, and developers focused on API engineering, bioinformatics pipelines, and community engagement.

• Semantic Web
• Energy Load Forecasting
• Database Systems
• +3 more

Dr. Monika Kaczmarek is a faculty member at the Department of Economic Informatics , part of the Faculty of Informatics and Electronic Economy at the Poznań University of Economics and Business . Her work focuses on semantic technologies, expert finding systems, and energy load forecasting. She has collaborated on research projects like Insemtives and ASG , and contributed to conferences such as ADBIS, eKnow, and ICEIS. Key research areas: Semantic Web, Database Systems, Information Retrieval Notable award: Best Paper Award at eKnow2011 Her publications span topics in semantic expert systems and real-time energy forecasting, with a focus on ontologies, scalable retrieval, and mobile applications. Collaborations include institutions like SAP AG and Technical University of Munich.

• Agent-based Modeling
• Evolutionary Game Theory
• Traffic Simulation
• +5 more

Dr. Simon Powers is a Lecturer at the School of Computing Engineering and the Built Environment at Edinburgh Napier University, where he is affiliated with the Centre for Algorithms, Visualisation and Evolving Systems (CAVES). His research focuses on agent-based modeling, evolutionary game theory, and the application of these techniques to understand social behavior, traffic systems, and human-AI interactions. Dr. Powers' research interests span multiple interdisciplinary domains: Agent-based modeling and simulation of complex systems Evolutionary game theory applications to social behavior Trust dynamics in human-AI interactions Leadership and consensus decision-making in social organisms Reciprocity mechanisms across human societies Traffic simulation and individual travel behavior modeling Dr. Powers' publication record demonstrates consistent contributions to agent-based modeling, with recent work focusing on trust in AI systems, leadership dynamics, and traffic simulation. His research shows a strong interdisciplinary approach, bridging computer science, social sciences, and evolutionary theory. The articles reveal a pattern of increasingly sophisticated modeling techniques applied to complex social phenomena, with particular attention to how individual behaviors aggregate to produce emergent systemic properties. Notable scientific contributions and recognitions include: Appointment as Associate Editor for the journal Adaptive Behavior (2017) Keynote speaker at the Second International Workshop of Social Learning and Cultural Evolution (2017) Invitation to contribute to the Encyclopedia of Evolutionary Psychological Science (2016) Dr. Powers has supervised several PhD students, including Johannes Nguyen (2020-2024) and Cedric Perret (2016-2020), and has collaborated extensively with researchers in the Centre for Algorithms, Visualisation and Evolving Systems. His work often involves developing novel modeling frameworks and notations, such as the Multi-Agent Modelling Notation (MAMN), to better understand complex adaptive systems. His research has practical applications in traffic management, AI governance, and understanding social dynamics, with recent work examining how regulation alone cannot ensure justifiable trust in AI systems.

• Artificial Intelligence
• BIM Collaboration
• Decentralized systems
• +9 more

Seppo Törmä is a Senior Lecturer and Principal Lecturer at Metropolia University of Applied Sciences, specializing in computing and construction. He holds a D.Sc (Tech) in Computer Science and Engineering from Helsinki University of Technology (1997), an M.Sc (Tech) in Electrical Engineering (1988), and a Teacher's pedagogical qualification (AmO) from Haaga-Helia (2022). As the Head of the International Master's Programme in Computing in Construction, he focuses on advancing digitalization in construction through innovative methodologies. His competences include Artificial Intelligence, BIM Collaboration, Decentralized systems, Digital Twin, Information visualization, Interoperability, IoT, Knowledge graphs, Linked Data, Ontologies, Semantic Web, and Sustainable built environment. He leads projects such as RADIAL (digital models for construction), Digipurku (digital solutions for deconstruction), and HereUkraine! (educational support for rebuilding efforts). His research emphasizes semantic interoperability, ontology-based systems, and sustainable construction practices. Notable projects include developing the DRUMBEAT platform for federated building models and exploring peer-to-peer networks for linked building data. His work bridges information technology with construction workflows to enhance efficiency and sustainability. He has no listed scientific awards but has contributed to over 70 publications since 1988, focusing on BIM, Linked Data, and construction digitalization. His advising and grants are not explicitly detailed, but his leadership in academic and applied projects underscores his commitment to advancing the field. He is part of the Sustainable Development in Real Estate Team at Metropolia.

Sean BechhoferView profile

Professor

• Semantic Web
• Ontologies
• Knowledge Representation
• +3 more

Sean Bechhofer is a Professor in the Department of Computer Science at the University of Manchester, specializing in teaching and scholarship. His research focuses on Semantic Web technologies, ontologies, knowledge representation, and linked data. He has held roles including Discipline Head of Education for Computer Science (2019–2023) and contributed to projects like the Information Management Group and WF4EVER initiative. Education: BSc Mathematics from the University of Bristol (1988), postgraduate work in Category Theory at the University of Manchester. Research interests include ontology engineering, semantic web languages, and workflow preservation. He co-developed the OWL API and contributed to standards like SKOS and the Research Object Model. Teaching responsibilities include courses on Operating Systems, Computational Thinking, and Algorithms. Key projects include the SCAPE preservation framework and myExperiment workflow repository. His work aligns with UN Sustainable Development Goals through contributions to Digital Futures and data science initiatives.

Vivek RaoView profile

Professor

• Engineering Design Theory and Methodology
• Product Design, Development & Innovation
• Emerging Technologies in Design
• +4 more

Vivek Rao serves as Associate Dean for Master's & Professional Programs and Executive Director of the Design and Technology Innovation Master's Program at Duke University's Pratt School of Engineering. He is also an Executive In Residence in Engineering Graduate and Professional Programs, teaching core courses including Design Ethics & Social Innovation and Design Innovation Studio while leading the development of Duke's new Masters of Engineering program. He earned his B.S. (2007), M.S. (2012), and Ph.D. (2018) in Mechanical Engineering from the University of California, Berkeley. His research spans Engineering Design Theory and Methodology , Product Design Innovation , and Emerging Technologies in Design , with significant focus on Sociotechnical Systems , Human-Computer Interaction , and Sustainability applications in water infrastructure. His interdisciplinary work integrates business innovation frameworks with engineering design principles. Analysis of his 15 most recent publications (2021-2024) reveals a dominant trend in AI-enhanced design tools, cybersecurity integration, and human-centered methodologies across disaster response, global health, and virtual collaboration contexts. These works demonstrate consistent interdisciplinary collaboration bridging engineering, business, and social sciences through journals like Design Studies and Journal of Mechanical Design. No scientific awards were documented in the source material. Rao has secured substantial external and internal grant funding during his postdoctoral research at UC Berkeley and maintains an active consulting practice with clients ranging from pre-seed startups to the US Department of Defense. His pedagogical approach emphasizes project-based learning, as evidenced by his development of foundational courses for Berkeley's Masters of Design program and Duke's new initiative. As Executive Director of Duke's Design and Technology Innovation program, Rao leads curriculum development and industry partnerships focused on translating design theory into real-world technological solutions, particularly in infrastructure and sustainability domains.

Alexei FigueroaView profile

Researcher

• Natural Language Processing
• Healthcare Informatics
• Clinical Outcome Prediction
• +7 more

Alexei Figueroa is a Researcher at the Data Science and Text-based Information Systems (DATEXIS) group at Berlin University of Applied Sciences and Technology. He has been actively contributing to NLP and healthcare-related AI research since February 2020, following the completion of his MSc in Data Science at the same institution. His research interests span Natural Language Processing , Healthcare Informatics , Transformer Networks , and Knowledge Graphs . His work emphasizes clinical decision support, information retrieval, and deep learning for long-tail classification problems. He is involved in key projects such as INDREX, IDEL, and Smart-MD, focusing on in-database text mining and clinical NLP systems. The recent publications reflect a strong trend in integrating domain knowledge into deep learning models, improving clinical outcome prediction, and enhancing transformer architectures for specialized tasks. His work bridges foundational NLP methods with real-world healthcare applications. Scientific Awards: No awards listed in the provided text. Advising and Grants: There is no mention of students advised or grants received. However, his active role in multiple research initiatives suggests involvement in collaborative, grant-funded projects. Labs and Teams: Alexei is a core member of the DATEXIS research group, which conducts cutting-edge research in NLP, with a strong focus on healthcare applications, explainability, and benchmarking. The team develops systems like TASTY, SECTOR, and Smart-MD, demonstrating a strong applied research orientation.

Laurent DoyenView profile

Researcher

• Game Theory
• Automata Theory
• Formal Verification
• +7 more

Laurent Doyen is a CNRS Researcher at the Laboratoire Méthodes Formelles (LMF), ENS Paris-Saclay. He holds a PhD from the Université Libre de Bruxelles (2006) and an HDR from ENS Cachan (2012). His research focuses on formal methods, game theory, automata, and verification of quantitative and probabilistic systems. PhD: Université Libre de Bruxelles, 2006 HDR: ENS Cachan, 2012 Research Interests: Laurent's work centers on algorithms and tools for the verification and synthesis of reliable software, hardware, and embedded systems. His primary areas include game and automata theory, with a focus on discrete quantitative and probabilistic systems, timed and hybrid systems. He investigates synchronization, mean-payoff objectives, and imperfect information in games, contributing significantly to theoretical foundations and practical tools. The recent publications highlight a strong trend in stochastic games, synchronization in Markov decision processes, and quantitative verification. His work spans theoretical computer science, formal methods, and practical applications in system design, often appearing in top venues like LICS, ICALP, and CONCUR. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Laurent has advised several PhD students including Mahsa Shirmohammadi, Julien Reichert, Thomas Soullard, and Pranshu Gaba. He leads and participates in multiple research projects such as QuaVerif (PI), IFCPAR SMILeS (coPI), Cassting, ARiSE, and Quasimodo. He has been a Rutherford Visiting Fellow at the University of Warwick and is involved in various academic communities like GAMES, CFV, and GDR-IM. Labs and Teams: He is a key member of the Laboratoire Méthodes Formelles (LMF) at ENS Paris-Saclay and has been associated with the LSV (Laboratoire Spécification et Vérification) in the past. He contributes to the development of tools like Alaska and Alpaga for automata analysis and model checking.

Luis Antonio Belanche MuñozView profile

Professor

• Machine Learning
• Kernel Methods
• Neural Networks

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.

Luigi PalopoliView profile

Professor

• Hybrid Systems
• Real-Time Scheduling
• Quality of Service Control
• +12 more

Luigi Palopoli is a Full Professor and Director at the Department of Information Engineering and Computer Science (DISI), University of Trento, Italy. He is also a member of the University Council. His research spans hybrid systems, real-time scheduling, quality of service control, robotics, embedded systems, and cyber-physical systems. His research interests include: Hybrid Systems and Real-Time Scheduling Service and Assistive Robotics for Elderly Support AI Applications in Robotics and Embedded Systems Outlier Detection and Anomaly Explanation Audio Super-Resolution and Signal Processing Biological Network Analysis and Graph Alignment His recent publications reflect a strong interdisciplinary focus, combining AI, robotics, real-time systems, and bioinformatics. Trends show increasing work in explainable AI, assistive technologies, and multimodal learning (e.g., vision transformers for audio). He has made notable contributions to robotic navigation for the elderly, real-time embedded systems, and logic-based outlier detection. Scientific awards include: Outstanding Paper Award at RTAS 2017 He advises students and collaborates widely, especially with Daniele Fontanelli, Luca Abeni, and other researchers in robotics and AI. He maintains an active research group, evidenced by a dedicated YouTube channel for the Embedded Intelligence and Robotic Systems group. He is involved in teaching, including courses on Robot Planning and Real-Time Operating Systems, and participates in AI seminar series. There is no indication of retirement or former status; he is actively engaged in research, teaching, and academic service.

• Logic Programming
• Knowledge Representation
• Graph Databases
• +7 more

Ana Edelmira Pasarella Sanchez is a Professor in the Department of Computer Sciences at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). She is a member of the ALBCOM research group, focusing on algorithms, bioinformatics, complexity, and formal methods. Her work bridges theoretical computer science with practical data systems. PhD in Computer Science, Universitat Politècnica de Catalunya Her research centers on logic programming, knowledge representation, and graph databases. She explores how formal methods can enhance data processing, particularly through dynamic pipelines and trust-aware access control. Her work integrates Datalog, semantic reasoning, and big data frameworks to improve scalability and correctness in knowledge systems. She has contributed to foundational semantics of logic programs and their applications in security and data integration. Her recent publications highlight a trend toward efficient, adaptive data processing systems, especially for graph analytics and knowledge graphs. She compares paradigms like MapReduce and pipelining, advocating for dynamic, functional approaches to big data. Her work increasingly addresses real-world challenges in federated knowledge graphs and access control. SACMAT 2017 Best Paper Award Pasarella has been involved in multiple competitive R&D+i projects, such as 'Modelos y Técnicas para el Procesamiento de Información a Gran Escala' and 'Modelos y métodos basados en grafos para la computación en gran escala,' indicating sustained funding and collaborative leadership. She advises on research direction within her group and mentors through collaborative publications. She has served on the scientific committee of the Latin American Informatics Conference (CLEI), contributing to the broader academic community. She is part of the ALBCOM research group and collaborates extensively with researchers like Fernando Orejas, Maria-Esther Vidal, and Elvira Pino, working on logic-based frameworks for data and security systems.

Elias N. ZoisView profile

Associate Professor

• Digital Signal Processing
• Image Processing
• Pattern Recognition
• +4 more

Elias N. Zois is an Associate Professor in the Department of Electrical & Electronics Engineering at the University of West Attica, where he has taught since 2019. Previously, he held positions as Lecturer (since 2009), Assistant Professor (2015-2022), and Adjunct Professor at multiple institutions including the Hellenic Army Academy and Hellenic Police Academy. He received his B.Sc. (1994), M.Sc. (1997), and Ph.D. (2000) in Physics and Electronics Engineering from the University of Patras, Greece. His research focuses on digital signal processing , image processing , pattern recognition , and specialized applications in handwriting biometry and offline signature verification . Recent work extends to smart grid optimization , including load forecasting and non-technical loss detection using machine learning techniques. Analysis of his 15 most recent publications reveals strong interdisciplinary trends: 60% focus on advanced biometric verification using Riemannian geometry and metric learning, while 40% apply machine learning to energy systems. Key methodologies include sparse coding, manifold learning, and neural network ensembles, with consistent applications in security systems and smart grid resilience. He has led multiple funded projects including: BioControl (2021-2023): Experienced Researcher for biometric systems Ireact-NG (2018-2021): Experienced Researcher in smart grid technologies ESA SimSat Engine Enhancement (2011-2015): Theoretical Project Manager for aerospace simulations He directs research at the TELSIP Laboratory (Building Z, University of West Attica), specializing in signal processing and pattern recognition systems.

Qingping YangView profile

Associate Professor

• Measurement Systems
• Quality Engineering
• Smart Technologies
• +5 more

Dr. Qingping Yang is a Reader (Associate Professor) in the Department of Mechanical and Aerospace Engineering at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. He directs the Brunel Quality Engineering and Smart Technology (QUEST) and Robotics and Automation Research Groups, focusing on sensor systems, AI, and industrial metrology. Education: PhD in Engineering, Brunel University London (1992) BEng in Instrumentation and Measurement Technology, Chengdu Aeronautical Polytechnic (1983) Research Interests: Dr. Yang integrates measurement systems, quality engineering, and smart technologies (AI/robotics). His work spans industrial metrology, IoT-driven manufacturing optimization, renewable energy forecasting, medical diagnostics, and autonomous inspection systems. Key methodologies include machine learning, Bayesian risk modeling, and non-destructive evaluation. Publication Trends: Recent articles emphasize AI applications in manufacturing (defect detection, robotic path planning), IoT-based monitoring (3D printing, building safety), and healthcare analytics (COVID-19 diagnosis). Methodological innovations include graph theory optimization, computer vision, and ontology-driven quality frameworks. Awards: Three Brunel University Performance Bonuses Marquis Who's Who in the World (1998) Marquis Who's Who in Science and Engineering (2000) Advising & Grants: Supervised 39+ PhD/MPhil students (23 as primary supervisor). Secured £2.7M as PI and £1.2M as Co-I across 18 projects (EU/UK industry/government-funded). Current PhD projects include AI for non-destructive testing and sustainable manufacturing. Leadership: Heads QUEST and Robotics research groups. Former EU research coordinator and MSc program director. Editor-in-Chief of the International Journal of Metrology and Quality Engineering .

• Computer Vision
• Machine Learning
• Pattern Recognition
• +6 more

Xiangyang Xue is a Professor at Fudan University in Shanghai, China, with an extensive research portfolio spanning computer vision, machine learning, and artificial intelligence. His work demonstrates significant contributions to object-centric representation learning, 3D reconstruction, person re-identification, and semantic segmentation. With over two decades of publication history from 1999 to present, he maintains an active research program with numerous collaborations, particularly with researchers like Yanwei Fu, Bin Li, and Yu-Gang Jiang. Professor Xue's research interests focus on advancing computer vision through innovative approaches to object-centric representation learning, 3D scene understanding, and multi-modal learning. His recent work explores the integration of large vision-language models with 3D understanding, diffusion models for data synthesis, and brain-inspired approaches to robotic scene understanding. His research bridges theoretical advances with practical applications in robotics, autonomous systems, and security. Analysis of his recent publications (2023-2026) reveals a strong trend toward multi-modal learning, with increasing integration of vision-language models, 3D understanding, and diffusion-based generation techniques. His work shows a progression from traditional computer vision problems toward more complex, embodied AI challenges that require understanding of both visual scenes and their semantic interpretations. Key themes include object-centric representations, cross-modal alignment, and the application of these techniques to robotics and security domains. Professor Xue has mentored numerous researchers through collaborative projects, with extensive co-authorship indicating a strong advising presence. His work spans multiple funding areas including NSF-supported research in computer vision, AI security, and robotics applications. His publications appear consistently in top venues including CVPR, ICCV, ECCV, AAAI, and IEEE TPAMI. His research group appears to focus on computer vision and machine learning, with particular emphasis on object-centric scene understanding, 3D reconstruction, and person re-identification systems. The team works at the intersection of theoretical computer vision and practical applications, with projects spanning autonomous driving, robotics, security systems, and human-computer interaction. Recent work suggests active exploration of large vision-language models and their integration with 3D scene understanding.

Frank WübbelingView profile

Researcher

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +4 more

Dr. Frank Wübbeling is a faculty member at the Institute for Analysis and Numerical Analysis within the Faculty of Mathematics and Computer Science at the University of Münster. His research focuses on numerical analysis, machine learning, and scientific computing, particularly in the context of applied mathematics and medical imaging. Research Interests: Numerical analysis, machine learning, scientific computing, and medical imaging applications. Projects: Co-investigator in the MED4D collaborative project, addressing motion artifacts in dynamic medical imaging (PET/MR) for cardiovascular and renal diagnostics. Recent Publications: Contributions to knowledge graphs and ontologies for applied mathematics models and algorithms. Contact: Email frank.wuebbeling@uni-muenster.de or attend consultation hours via Zoom (ID: 92355292463).