جست‌وجوی استاد و پژوهشگر

Vivek Raoمشاهده پروفایل

استاد

• Engineering Design Theory and Methodology
• Product Design, Development & Innovation
• Emerging Technologies in Design
• +۴ مورد دیگر

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 Figueroaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Healthcare Informatics
• Clinical Outcome Prediction
• +۷ مورد دیگر

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 Doyenمشاهده پروفایل

پژوهشگر

• Game Theory
• Automata Theory
• Formal Verification
• +۷ مورد دیگر

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 Muñozمشاهده پروفایل

استاد

• 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 Palopoliمشاهده پروفایل

استاد

• Hybrid Systems
• Real-Time Scheduling
• Quality of Service Control
• +۱۲ مورد دیگر

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
• +۷ مورد دیگر

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. Zoisمشاهده پروفایل

دانشیار

• Digital Signal Processing
• Image Processing
• Pattern Recognition
• +۴ مورد دیگر

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 Yangمشاهده پروفایل

دانشیار

• Measurement Systems
• Quality Engineering
• Smart Technologies
• +۵ مورد دیگر

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
• +۶ مورد دیگر

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 Wübbelingمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۴ مورد دیگر

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).

Prateek Jainمشاهده پروفایل

مدرس

• Knowledge Graphs
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Prateek Jain is a Lecturer at the School of Information, San José State University, where he teaches courses in Big Data Analytics and Python programming. He holds a Ph.D. in Computer Science and Engineering from Wright State University and has over a decade of industrial experience in data science and artificial intelligence at leading institutions such as IBM T.J. Watson Research Center, Nuance Research Labs, and Liveperson. His research interests include Knowledge Graphs, Natural Language Processing, Machine Learning, and Big Data, with over 30 peer-reviewed publications and patents in these areas. His academic and professional background bridges advanced research and practical deployment of AI-driven systems. Ph.D. (Computer Science and Engineering), Wright State University (2007–2012) Ph.D. (Computer Science), University of Georgia (2006–2007, transferred) BS (Information and Communication Technology), Dhirubhai Ambani Institute of ICT (2002–2006) Prateek's research publications focus on the development and application of Knowledge Graphs, particularly in areas like NLP, enterprise search, healthcare, and financial services. His work emphasizes scalable, explainable, and practical AI solutions, with recurring themes in graph embeddings, semantic reasoning, and large-scale data integration. He has served as an Adjunct Instructor at Chabot College and Northwestern Polytechnic University, and currently contributes to academic instruction at SJSU while maintaining an active role in industry as Principal Data Scientist at Liveperson. His industrial roles have spanned: Principal Data Scientist, Knowledge Graphs – Liveperson Inc. (2021–Present) Engineering Manager, Machine Learning – AppZen Inc. (2018–2021) Principal Research Engineer – Nuance Inc. (2016–2018) VP/Data Scientist – BlackRock Inc. (2015–2016) Data Scientist – Ignition One (2015) Research Staff Member – IBM T.J. Watson Research Center (2012–2015) There are no listed scientific awards or advisees in the provided information.

• Philosophy of Artificial Intelligence
• Cognitive Science
• Neuroscience
• +۳ مورد دیگر

Carlos Zednik is an Assistant Professor for Philosophy of Artificial Intelligence at Eindhoven University of Technology , affiliated with the Industrial Engineering and Innovation Sciences department. He leads the Eindhoven Center for Philosophy of AI and participates in the alignAI (ERC) and ROBUST AI (NWO) consortia. His work bridges philosophy with AI and neuroscience, focusing on explainable AI (XAI), mechanistic explanation, and cognitive modeling. Education: BSc in Computer Science and Philosophy, Cornell University MSc in Philosophy of Mind, University of Warwick PhD in Cognitive Science, Indiana University Bloomington Zednik’s research investigates philosophical questions about biological and artificial intelligence, emphasizing: Methodological principles in cognitive psychology and neuroscience Norms and best practices for XAI in machine learning Knowledge representation in transformer models and large neural networks His recent publications explore the integration of cognitive models into XAI, the role of Bayesian reverse-engineering in cognitive science, and the mechanistic explanation of network neuroscience. Zednik also contributes to international standardization efforts through ISO/IEC TS 6254 and DIN SPEC 92001 . Scientific Awards include fellowships from: DAAD Alexander-von-Humboldt Foundation StandICT Fellowship Zednik supervises PhD students Zeynep Kabadere , Michela Ghezzi , Céline Budding , Miriam Gorr , and Hannes Boelsen , while mentoring postdocs Manuel Barbosa de Oliveira and Philippe Verreault-Julien . His teaching spans philosophy of AI, ethics of machine learning, and decision theory, with innovation projects on generative AI in higher education.

• Cybersecurity
• Artificial Intelligence
• Blockchain Technology
• +۴ مورد دیگر

Dr. Shun-Wen Hsiao is an Associate Professor in the Department of Management Information Systems at National Chengchi University (NCCU), Taiwan. He earned his PhD in Information Management from National Taiwan University and has been with NCCU since 2017, first as an Assistant Professor before his promotion to Associate Professor in 2023. His research spans: Cybersecurity : Malware analysis, threat detection, IoT security, and blockchain applications Artificial Intelligence : Neural networks for security, NLP, and recommendation systems FinTech : Blockchain frameworks, secure voting systems, and P2P lending risk models E-commerce : Livestreaming recommendation algorithms and consumer behavior analysis He has received multiple awards including: National Science Council Research Award (2023) Three consecutive Outstanding Team awards for Information Security R&D (2016-2018) Research grants from Taiwan's Ministry of Science and Technology Dr. Hsiao leads several major research projects funded by Taiwan's National Science and Technology Council, focusing on explainable AI for cybersecurity, financial technology security, and big data analytics platforms. His work frequently appears in top IEEE transactions and cybersecurity conferences.

Emanuel Kitzelmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +۴ مورد دیگر

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

• Operations Research
• Theoretical Operations Research
• Applied Operations Research
• +۸ مورد دیگر

Evangelos F. Magirou is a Professor of Operations Research at the Department of Informatics, School of Information Sciences and Technology, Athens University of Economics and Business (AUEB), where he has served since 1986. His academic career includes significant administrative roles including Chairman of the Faculty Union at AUEB since 1994, Deputy Chairman of the Department of Informatics since 2003, and Member of the Governing Board of the Public Power Corporation since 2004. His educational background includes: PhD in Decision and Control Sciences, Harvard University (1972-76) MS in Decision and Control Sciences, Harvard University (1971-72) BS in Electrical Engineering, Princeton University (1967-71) Professor Magirou's research spans theoretical and applied operations research with particular emphasis on applications to Greek business environment challenges. His work demonstrates consistent focus on finance, shipping, and production systems, with fundamental contributions to optimization, probabilistic models, and quantitative methods. His research trajectory shows evolution from early theoretical work in control systems and game theory to applied research in shipping economics and financial mathematics, with recent work addressing contemporary challenges including infectious disease response and American option pricing. His publication record reveals a strong interdisciplinary approach, bridging operations research with computer science, economics, and maritime transport. The 15 most recent publications indicate continued research productivity with applications spanning finance (option pricing algorithms), public health (infectious disease response), shipping (vessel positioning), and computer security (spam modeling). Professor Magirou has held numerous leadership positions including: Chairman of the Executive Committee for the MSc Program in Business Mathematics Chairman of the Hellenic Procurement Institute (1994-97) Vice Chairman of the Energy Coordination Committee (1993-95) Member of the National Energy Council's Scientific Secretariat (1977-85) He has been actively involved with professional organizations including INFORMS (Institute for Operations Research and the Management Sciences) and the Technical Chamber of Greece. His teaching responsibilities include courses in Elementary and Intermediate Mathematics of Finance, Operations Research, Game Theory, and Decision Theory, for which he has developed extensive teaching materials available in Greek.