جست‌وجوی استاد و پژوهشگر

• Industrie 4.0
• Smart Manufacturing
• Business Model Innovation
• +۲ مورد دیگر

Prof. Dr.-Ing. Dr. h.c. Detlef Zühlke leads research at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, Germany. His work focuses on Industrie 4.0 , Smart Manufacturing , and Business Model Innovation , particularly through the development of the SmartFactoryKL demonstrator. Research interests include: Networked production systems Human-robot collaboration Flexible manufacturing architectures Mobile industrial device interfaces Current projects involve: Generic device identification systems Task-based communication for multi-user environments Business model transformations in production technology

Vitaveska Lanfranchiمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Social Media
• Visual Analytics
• +۳ مورد دیگر

Vitaveska Lanfranchi is a Senior Research Fellow at the University of Sheffield , School of Computer Science, focusing on Human-Computer Interaction , Social Media , and Visual Analytics . Her work addresses real-time data analysis for emergency response and knowledge management, with projects funded by EPSRC, UK/SBRI, and EU Framework 6. Research Interests : Human-Computer Interaction Social Media for Emergency Response Visual Analytics Knowledge Management Mobile Interaction Recent publications (2008–2012) explore semantic user networks, knowledge dashboards, hybrid search techniques, and AI usability, particularly in aerospace engineering and emergency response. Projects like Randms and WeKnowIt emphasize collective intelligence and web-scale data analysis. Contact : v.lanfranchi@dcs.shef.ac.uk

Zheng Yuanمشاهده پروفایل

مدرس ارشد

• Educational Technology
• Language Acquisition
• Multilingual NLP
• +۳ مورد دیگر

Dr. Zheng Yuan is a Senior Lecturer (Associate Professor) in Natural Language Processing at the School of Computer Science, University of Sheffield. He holds affiliated positions at the University of Cambridge and King's College London, and is a Fellow of Trinity College, Cambridge. His research focuses on NLP applications in education, healthcare, and multilingual systems. Education: PhD in Natural Language Processing, University of Cambridge MPhil in Advanced Computer Science, University of Cambridge BSc(Eng) from Queen Mary University of London Research: Dr. Yuan's work spans educational NLP, multilingual systems under low-resource conditions, and explainable machine learning. His group develops technologies for grammatical error correction, automated assessment, and cross-lingual applications, with significant contributions to computer-assisted language learning and computational creativity. Publications: Recent works (2023-2025) demonstrate strong focus on educational NLP, multilingual systems, and LLM evaluation. Key themes include grammatical error correction for code-switched languages, creativity assessment frameworks, and robust evaluation methods for large language models across diverse linguistic contexts. Awards: Winning systems at SemEval-2021 and CoNLL-2014 Fellowship at Trinity College Cambridge Fellow of Higher Education Academy Grants & Advising: Principal Investigator for Royal Society grant 'Large Language Models as Agents for Intelligent Language Tutoring' (2025-2027). Actively supervises PhD students in NLP and machine learning, welcoming new research collaborations. Affiliations: Member of Alan Turing Institute (Data-Centric Engineering), King's Institute for AI, and ACL committees. Organizes major NLP workshops including ACL/NAACL BEA workshops and AIED tutorials.

R.L. Lagendijkمشاهده پروفایل

استاد

• Cyber Security
• Cryptography
• Privacy-Preserving Computation
• +۴ مورد دیگر

R.L. Lagendijk serves as a Professor within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology, specializing in Cyber Security research. His academic profile demonstrates sustained leadership in privacy-enhancing technologies and cryptographic systems development across diverse application domains. His core research spans Cyber Security, Cryptography, and Privacy-Preserving Computation with specialized expertise in Differential Privacy and Algorithmic Security. Lagendijk pioneers practical implementations for sensitive data protection in supply chain logistics, healthcare diagnostics, and blockchain infrastructure, consistently bridging theoretical cryptography with real-world security challenges through innovative protocol design. Analysis of his publication trajectory since 2020 reveals concentrated advancement in differential privacy applications, particularly for trajectory data obfuscation in supply chains and bin-packing optimization in logistics. His work increasingly integrates blockchain security with AI ethics frameworks, demonstrating evolving focus toward human-centric privacy solutions in emerging technologies. His distinguished career includes recognition through significant professional honors: NAE Fellow (2023) Professor Lagendijk has guided 44 students through academic supervision while actively leading European research initiatives including H2020 IRIS, SPECIES, and SESAME projects. His editorial contributions to IEEE Transactions on Information Forensics and Security underscore his influence in shaping cryptographic standards. As a core member of TU Delft's Cyber Security research group, he drives collaborative innovation in privacy-preserving computation through both theoretical exploration and industry-engaged solutions development, maintaining active participation in national and international cybersecurity discourse.

• Artificial Intelligence
• Autonomous Systems
• Smart Living Technologies
• +۷ مورد دیگر

Dr. sc. nat. Hilko Hoffmann is a researcher at the German Research Center for Artificial Intelligence (DFKI) within the Agents and Simulated Reality department in Saarbrücken, Germany. His work focuses on the intersection of artificial intelligence, cloud-based IoT systems, and smart living environments, with particular emphasis on security, contextual awareness, and human-AI collaboration. Role: Researcher Institution: DFKI Saarbrücken Research Unit: Agents and Simulated Reality Key research interests include: Secure orchestration of heterogeneous IoT devices Context-sensitive smart living services Federated data ecosystems for autonomous systems Human-centric AI integration in safety-critical domains Recent publications analyze technical and sociotechnical aspects of AI in cloud-IoT architectures, with specific applications in building automation and energy systems. Collaborations span 11 European countries through the InterConnect project, demonstrating cross-domain integration of smart homes, buildings, and electricity grids.

Sandeep Reddivariمشاهده پروفایل

دانشیار

• Software Engineering
• Requirements Engineering
• Visual Analytics
• +۷ مورد دیگر

Sandeep Reddivari is an Associate Professor and Interim Graduate Director at the School of Computing, University of North Florida . Holding a Ph.D. in Computer Science and Engineering from Mississippi State University, his work focuses on software engineering , particularly requirements engineering , visual analytics , and software maintenance , with funding from the NSF, US Army Corps of Engineers, NSA, and UNF Foundation. Education : Ph.D. (2014, Mississippi State University), M.S. (2009, Texas A&M University), B.Tech. (2006, JNTU) Research Themes : Requirements Engineering, Visual Analytics, Data Mining, Machine Learning, GenAI, Software Security Teaching : Courses in Software Engineering, Data Science, and Database Systems Awards : NSF Grant Recognition (2024), Best Doctoral Symposium Poster (2013), Outstanding Graduate Teaching Award Nominee (2018) Publications span top venues like IEEE RE, ICSE, and COMPSAC, with a focus on combining visual analytics and machine learning to enhance software decision-making. His lab at UNF mentors students in directed independent studies, emphasizing code navigation , blockchain , and educational software tools .

• distributed systems
• networking
• blockchain systems
• +۳ مورد دیگر

Dr. Onur Ascigil is a Lecturer in Computer Science at Lancaster University's Department of Computing and Communications. He contributes to the Data Science Institute and focuses on distributed systems, networking, and decentralized technologies like blockchain and IPFS. Research Interests: His work addresses scalability and security in decentralized systems, including blockchain and content addressable networks. He has investigated resource allocation in edge-computing and traffic engineering in the Internet. Trends in Publications: Recent articles explore blockchain privacy (k-anonymity), Ethereum's data availability, content moderation in decentralized networks (IPFS), and Internet decentralization. Earlier work includes congestion control, ICN routing, and edge-cloud resource allocation. PhD Supervision: He advises Edvinas Kruminis, a PhD student researching decentralized systems. Activities: He served as Publication Chair for the 9th ACM Conference on Information-Centric Networking and as a Technical Programming Committee Member for IEEE CCNC. He delivered an invited talk on Ethereum service discovery. Contact: Office: B42, B-Floor, InfoLab21. Email: o.ascigil@lancaster.ac.uk .

• Distributed Computing
• Data-Centric Systems
• Software Engineering
• +۳ مورد دیگر

Tilman Zuckmantel is a Research Assistant at the Software, Data, People & Society (SDPS) section of the Department of Computer Science , University of Copenhagen. His work focuses on distributed computing and data-centric systems, particularly in asynchronous choreographies and microservices architecture. Recent research outputs include: DACEO (2025): A declarative framework for asynchronous choreographies with event-ordering and object-oriented extensions. Event-based Data-Centric Semantics (2022): A model for consistent data management in microservices environments.

Johan Pauwelsمشاهده پروفایل

مدرس

• Audio Signal Processing
• Music Information Retrieval
• Machine Learning for Audio
• +۳ مورد دیگر

Dr. Johan Pauwels is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he is affiliated with the Centre for Digital Music and the Centre for Multimodal AI. His educational background includes: Master of Science in Electrical/Electronics Engineering from KU Leuven (2006) Master of Science in Artificial Intelligence from KU Leuven (2007) PhD from Ghent University (2016) on automatic harmony recognition from audio Johan's research focuses on making machines understand audio to the level of a trained professional. His work combines machine learning, signal processing, data science, and music theory to develop tools for musicians, listeners, and music learners. He has been working on narrowing the gap between academic research and user-centric applications, web-based music services, and the personalization of spatial and immersive audio. His specific interests include machine learning for audio, audio signal processing, music information retrieval, and binaural audio. His recent publications show a strong focus on music representation learning, with particular attention to limited data scenarios, multimodal approaches, and spatial audio processing. His work bridges theoretical music concepts with practical machine learning applications, especially in chord recognition, beat detection, and instrument recognition. He has made significant contributions to HRTF (Head-Related Transfer Function) research and development of tools for spatial audio processing. Dr. Pauwels is actively involved in research funding, with current grants including the AIM CDT Internship with Sofilab (2025), AIM CDT Studentship - Stem (2024), and AIM CDT Internship with stem.tech (2024). He currently supervises multiple PhD students, primarily through the UKRI Doctoral School in AI and Music, with research topics spanning intelligent audio editing, neural drum synthesis, source separation, graph neural networks for music recommendation, and more. In addition to PhD supervision, he typically guides 8-10 undergraduate and 8-10 master's students through their final year projects. His teaching responsibilities include ECS7013P Deep Learning for Audio and Music (MSc/PhD level) and ECS411U Signals and Information (first-year undergraduate).

Wei Liمشاهده پروفایل

استاد

• Computer Security
• Network Security
• Artificial Intelligence
• +۳ مورد دیگر

Wei Li is a Professor in the Department of Computing at Nova Southeastern University's College of Computing & Engineering. He joined NSU in 2005 after earning his Ph.D. from Mississippi State University. His research focuses on enhancing computer system security through interdisciplinary approaches, including network security, artificial intelligence applications, and software engineering practices. Dr. Li’s research areas encompass computer security, network security, software engineering, artificial intelligence, and database systems. His work bridges theoretical advancements and practical cybersecurity solutions, such as improving intrusion detection systems, analyzing social engineering attack vulnerabilities, and developing machine learning models for IoT security. His recent publications (2020–2025) address topics like password policy effectiveness, remote worker cybersecurity risks, and adaptive deep learning models for anomaly detection. These contributions highlight his focus on mitigating organizational cybersecurity risks through empirical studies and innovative technical solutions. No scientific awards or grants are explicitly listed in the provided information. His advising record remains unspecified, and no active labs or teams are mentioned.

Mario Harperمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Robotics
• +۲ مورد دیگر

Mario Harper is an Assistant Professor in the Department of Computer Science at Utah State University. His research emphasizes Machine Learning, Data Science, Robotics, and their intersections with Finance and Artificial Intelligence. He specializes in autonomous systems, energy-efficient robotics, and AI-driven solutions for transportation and urban sustainability. His work includes developing tools like simulators for electric vehicle systems and stealth-centric navigation algorithms inspired by biological systems. Key research areas include multi-robot coordination, reinforcement learning applications in robotics, and algorithmic approaches to environmental and economic challenges. He has contributed to projects like POSEIDON-SAT for satellite-based fishing vessel detection and electrified transportation equity analysis in urban settings. His publications span topics from trajectory planning in legged robots to AI modeling for economic systems. Beyond research, he designs interactive visualization tools to support policy decisions on sustainable transportation infrastructure. No scientific awards are explicitly listed in the provided information. Mario Harper’s advising and grants focus on robotics, energy systems, and AI applications, though specific student advisees or grant details are not detailed here. He collaborates on projects involving lab tools such as the Unknown Building Exploration Simulator (UBES) and Stealth Centric Autonomous Robot Simulator (SCARS), advancing robotic autonomy in unstructured environments.

Paul R Flemingمشاهده پروفایل

استاد

• Sports Surface Science
• Transport Infrastructure
• Geotechnical Engineering
• +۴ مورد دیگر

Professor Paul R Fleming is a Professor of Sports Surface and Ground Engineering at Loughborough University’s Wolfson School. He leads the Sports Technology Institute, focusing on innovative research in sports surfaces and transport infrastructure. With over 30 years of experience in geotechnical engineering, his work spans novel field testing methods, soil-structure interaction, and sustainable solutions for sports and transport systems. He holds a BEng and PhD, alongside professional memberships in MCIHT, GMICE, and SFHEA. His research themes include sports surface science (player safety, biomechanical performance, injury risk) and transport infrastructure resilience under climate change. He has supervised 21 PhD students and published over 150 articles. Recent projects include FIFA-funded studies on artificial turf safety, HS2 trackbed foundation innovations, and climate-resilient transport networks. His work bridges academic research with industry applications, emphasizing practical solutions for surface maintenance, drainage systems, and material durability. Key Research Areas: Artificial turf degradation, rotational traction mechanisms, sustainable drainage, and high-speed railway foundations. Collaborations: Nanyang University of Technology, Technical Surfaces Ltd., and EPSRC-funded initiatives. Expertise: Geotechnical testing, surface biomechanics, and infrastructure resilience. His publications analyze surface performance under dynamic loads, injury mitigation strategies, and the integration of sensor technology for real-time monitoring. Current work explores predictive modeling of artificial turf systems and advanced athlete-surface interaction analysis.

Renata Medeiros de Carvalhoمشاهده پروفایل

استادیار

• Business Process Management
• Process Mining
• Declarative Business Processes
• +۳ مورد دیگر

Renata Medeiros de Carvalho is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Process Analytics and EAISI Health groups. She holds a PhD in Computer Science from Federal University of Pernambuco (Brazil), an MSc and BSc in Computer Engineering from University of Pernambuco, and has conducted postdoctoral research at UQAM (Canada). Her research focuses on adaptive and declarative business processes, with particular emphasis on healthcare and data privacy. Education: PhD in Computer Science, Federal University of Pernambuco (2015) MSc in Computer Engineering, University of Pernambuco BSc in Computer Engineering, University of Pernambuco Research Interests: Flexible business processes and Process Mining Declarative modeling (e.g., OCBC language) Healthcare process optimization GDPR compliance frameworks Key Projects: PATIENCE 2 : Patient-centric healthcare through nomadic sensing BPR4GDPR : GDPR compliance toolkit Awards: Xerox University Affairs Committee Grant NSERC Engage Grant Teaching & Leadership: Local coordinator for EIT Digital Data Science and Erasmus Mundus BDMA master programs Teaches courses like Advanced Process Mining and DBL Data Challenge

Michael Ferrisمشاهده پروفایل

استاد

• Optimization
• Algorithms
• Computational Economics
• +۳ مورد دیگر

Michael Ferris is a Professor at the University of Wisconsin-Madison, holding the John P. Morgridge Chair in Computer Sciences and a courtesy appointment in Mathematics. His primary affiliation is with the Department of Industrial and Systems Engineering, and he serves as Director of Hub Central at the Wisconsin Institutes for Discovery. He earned his PhD from the University of Cambridge in 1989. His research focuses on algorithms, environments, and applications of optimization, with contributions to complementarity solvers, large-scale variational inequalities, and mathematical programming. Key areas include energy systems, economics, and engineering applications such as radiation therapy and transportation. Ferris has developed influential software tools like the PATH solver for complementarity problems and interfaces for optimization frameworks like AMPL and GAMS. Notable awards include SIAM Fellow, INFORMS Fellow, and the Beale-Orchard-Hays Prize. He has advised numerous PhD students and contributed to significant projects, including optimizing Great Lakes fishery barrier removal and modeling the energy transition. His work bridges theoretical optimization with practical applications, impacting policy, technology, and environmental conservation.

• Structural Dynamics
• Machine Learning Applications
• Non-Linear Systems
• +۳ مورد دیگر

Professor Keith Worden is a Professor of Mechanical Engineering at the University of Sheffield's School of Mechanical, Aerospace and Civil Engineering. His research focuses on applications of advanced signal processing and machine learning to structural dynamics, particularly in aerospace systems. He has held this position since 1995 and has contributed significantly to the field of structural health monitoring (SHM), including work on nonlinear system analysis and damage detection. His work emphasizes pragmatic engineering solutions and collaboration with industries like aerospace and offshore sectors. Education: Holds a degree from York University and a PhD in Mechanical Engineering from Heriot-Watt University. Career highlights include research at Manchester University before joining Sheffield. Research Interests: Specializes in structural dynamics, SHM using machine learning, nonlinear systems, and vibration analysis. Key themes include population-based SHM frameworks, damage prognosis, and environmental adaptation in monitoring systems. His group develops algorithms for automated inspection and diagnosis, leveraging neural networks, genetic algorithms, and other biological-inspired methods. Articles Trends: Recent publications focus on population-based SHM methodologies, transfer learning applications, and algorithm development for novelty detection, damage localization, and risk-informed decision frameworks. His work bridges theoretical advancements with practical engineering challenges. Grants & Advising: Extensive grants and collaborations in SHM, wind turbine monitoring, and aerospace structures. Advises on projects involving machine learning in structural dynamics and probabilistic modeling. Labs & Teams: Leads research groups exploring computational tools for SHM, including the application of Gaussian processes, Bayesian methods, and data-driven models. Collaborates internationally on projects such as the RAPTOR telescope system and offshore wind farm monitoring.