جست‌وجوی استاد و پژوهشگر

• Autonomous Driving
• Machine Learning
• Wireless Communication
• +۱۱ مورد دیگر

Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.

Muhammad Intizar Aliمشاهده پروفایل

استادیار

• Data Analytics
• Internet of Things (IoT)
• Machine Learning & AI for IoT
• +۸ مورد دیگر

Dr. Muhammad Intizar Ali is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). He holds a PhD (with distinction) from Vienna University of Technology, Austria (2011) and has held roles including Adjunct Lecturer and Research Fellow at the Insight Centre for Data Analytics, NUI Galway. His primary research focuses on IoT, Data Analytics, Machine Learning, and Knowledge Graphs with applications in Smart Cities, Manufacturing, Farming, and Healthcare. Education: PhD in Computer Science, Vienna University of Technology (2007-2011) Research Interests: IoT and Edge Analytics Federated and Distributed Machine Learning Semantic Web and Knowledge Graphs Smart Manufacturing and Industry 4.0 Stream Processing and Real-Time Systems Recent Work Trends: His publications emphasize federated learning frameworks, IoT-enabled adaptive intelligence, and knowledge graph applications in industrial contexts. Recent projects include digital twin systems for predictive maintenance and ontology-driven manufacturing solutions. Grants & Projects: Lead Investigator in SFI-funded projects like MultiRoof (2025-2029) and Neuro-Symbolic AI for Building Management EU/Industry collaborations including Terrain-AI and Bentley-funded initiatives Labs & Teams: Active in DCU's Data Analysis and Machine Learning research groups, leading projects like Smart DCU Digital Twin for campus optimization.

• Ontologies and Terminologies
• Neuroinformatics
• Big Data Analytics
• +۳ مورد دیگر

Licong Cui, Ph.D., is an Associate Professor at the McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth). She is also affiliated with the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) and the Texas Institute for Restorative Neurotechnologies (TIRN). Her research focuses on developing informatics methods to address biomedical data challenges, with expertise in ontologies, neuroinformatics, big data analytics, and clinical text mining. Dr. Cui has authored over 100 peer-reviewed publications and secured grants from NIH and NSF. Her work emphasizes ontology quality assurance, large language model applications in healthcare, and data integration frameworks. Notable contributions include developing the VaxBot-HPV chatbot for vaccine communication and advancing seizure frequency extraction methodologies using LLMs. Her honors include the 2022 AMIA New Investigator Award and 2021 NSF CAREER Award. Current projects involve enhancing NIH Common Data Elements with AI tools and improving EHR-based cohort querying through ontology-driven approaches. She collaborates on initiatives like the National Sleep Research Resource and Vaccine Ontology harmonization efforts.

Rui Daiمشاهده پروفایل

دانشیار

• Wireless Sensor Networks
• Multimedia Communications
• Video Analytics
• +۲ مورد دیگر

Dr. Rui Dai is an Associate Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. Her research focuses on wireless sensor networks, multimedia communications, and video analytics for healthcare and surveillance applications. She directs multiple NSF and NIST-funded projects on perceptual-quality-aware video systems. Research interests include quality-of-experience optimization for video analytics, compressed domain feature extraction, and edge computing frameworks for intelligent surveillance. Recent work develops deep feature compression techniques, multi-camera fall detection systems, and quality-aware video distribution strategies for 5G networks. Publications demonstrate consistent innovation in video processing for resource-constrained environments, with applications spanning healthcare monitoring, public safety networks, and embedded vision systems. Current projects investigate metaverse communication challenges for 6G networks and PHP vulnerability detection through hybrid static-fuzzing analysis.

• Digital Manufacturing
• Sustainability Engineering
• Additive Manufacturing
• +۵ مورد دیگر

Professor Jonathan Corney holds the Chair of Digital Manufacture in the School of Engineering at the University of Edinburgh. His research focuses on advanced manufacturing technologies, including digital twin applications, smart factory optimization, sustainability engineering, and additive manufacturing. He leads projects addressing human factors in industrial environments, predictive analytics for production processes, and intellectual property challenges in modern manufacturing systems. His academic background encompasses mechanical engineering with specialization in CAD/CAM systems, hydroforming technology, and patent analysis for design innovation. Corney has pioneered methods like the Economic and Environmental Impact Assessment for Sustainability (EENIAS), and developed decision support frameworks for energy-efficient scheduling and garment reprocessing in circular economies. Key research themes include: Smart factory design through real-time worker movement analysis Cyber-physical systems for supply chain optimization Machine learning applications in manufacturing process control Human-centric automation and safety protocols His recent work emphasizes predictive modeling using spatio-temporal graph networks, digital twin integration in assembly processes, and sustainable manufacturing practices. Over 150 peer-reviewed articles demonstrate his contributions to near-net-shape manufacturing, intellectual property management, and crowdsourced design methodologies.

Tyler Derrمشاهده پروفایل

استادیار

• Data Mining
• Social Network Analysis
• Graph Representation Learning
• +۵ مورد دیگر

Dr. Tyler Derr is an Assistant Professor in the Department of Computer Science at Vanderbilt University, with affiliate roles in the Data Science Institute and the Frist Center for Autism and Innovation. He holds a PhD from Michigan State University (2020) and focuses on data mining, machine learning, and graph neural networks with applications in drug discovery, ethical AI, and neurodiversity research. Education: PhD in Computer Science, Michigan State University (2020) M.S. in Computer Science, Pennsylvania State University B.S. in Computer Science and Mathematics, Pennsylvania State University His research emphasizes interdisciplinary social good, including drug discovery, education equity, and autism innovation. He directs the Network and Data Science (NDS) Lab, mentoring students who have won 50+ awards. Notable contributions include tools like FairNNV for fairness certification and datasets like WelQrate for drug discovery benchmarking. Awards include the NSF CAREER Award (2023), NVIDIA Academic Grant (2024), and Vanderbilt’s Career Catalyst Impact Award (2025). He co-founded the MLoG workshop series and serves on editorial boards for ACM TKDD and IEEE Transactions on Big Data . Grants & Leadership: NSF Grant for student travel to KDD2024 NVIDIA BioNeMo Platform grant for peptide design DOE Computational Science Fellowship advising His work bridges theory and practice, with tutorials on graph ML at AAAI/KDD and keynotes on ethical AI. The NDS Lab hosts interdisciplinary collaborations in computational biology, transportation, and neurodiversity advocacy.

Carlo Cavicchiaمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Machine Learning
• +۶ مورد دیگر

Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Vasiliki Kantereمشاهده پروفایل

استاد

• Big Data Management
• Big Data Analytics
• Machine Learning
• +۹ مورد دیگر

Dr. Vasiliki Kantere is a Professor at the School of Electrical Engineering and Computer Science within the Faculty of Engineering at the University of Ottawa . She has held previous academic positions, including: Senior Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens Maître d’Enseignement et de Recherche and Maître Assistante at the University of Geneva Junior Assistant Professor at the Department of Electrical Engineering and Information Technology, Cyprus University of Technology Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne Her research interests span a wide range of interdisciplinary domains, including: Big Data Management: data storage optimization, analytics workflows, and deployment on hybrid infrastructures Big Data Analytics: support for users of varying expertise and large data graphs Machine Learning: applications in resource management and query optimization Cloud Computing: cost-aware data management and service-level agreements P2P Systems: query processing and mobile peer environments Semantic Web: ontology mapping and semantic integration Distributed Systems: federated databases and grid computing Databases: query languages and semi-structured data management General: spatial data, privacy and security, and sensor networks No academic awards, funded grants, or advising roles are explicitly listed in the provided information. Her professional contributions are reflected through her extensive research collaborations and diverse academic experiences. The text does not mention any affiliated research labs or teams.

Natalia Efremovaمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Dr. Natalia Efremova is a Senior Lecturer in Digital Economy at Queen Mary University of London (QMUL), School of Business and Management. She joined QMUL in November 2021 and is a member of the Centre for Globalisation Research (CGR) and a fellow of the Digital Environment Research Institute (DERI). Her research focuses on applying machine learning and deep neural networks to address sustainability challenges such as climate change, sustainable agriculture, and environmental monitoring. She holds a Ph.D. in Computer Science (Neural Networks for Computer Vision) from Kyoto University and an MBA from the University of Oxford, where she also worked as a Teradata Research Fellow at the Said Business School. Education: Ph.D. in Computer Science, Kyoto University, Japan (2012) MBA, University of Oxford, Said Business School (2021) Previous Roles: Associate Professor, Plekhanov University of Economics, Russia (2012–2016) Teradata Research Fellow, University of Oxford (2016–2021) Research Interests: Dr. Efremova’s work emphasizes developing transparent ML models for sustainable land-use and climate-related applications, as well as ethical AI frameworks for sustainable development goals. Her projects involve satellite data analysis (e.g., Sentinel imagery) for precision agriculture, soil moisture estimation, and crop monitoring. Teaching & Supervision: She teaches courses in Business Analytics (e.g., Group Projects in Business Analytics) and supervises PhD students in AI applications for sustainability. She is currently co-director of the MSc in Environmental Analytics program (2023). Key Themes in Publications: Her articles focus on AI-driven solutions for environmental challenges, including crop mapping, soil carbon estimation, and regenerative grazing monitoring. Methodologies include deep learning (e.g., Transformers, GANs) and remote sensing data fusion. Affiliations: Member of CGR and fellow of DERI, contributing to interdisciplinary research on globalization and environmental AI.

Anthony Constantinouمشاهده پروفایل

مدرس ارشد

• Causal Machine Learning
• Decision Systems
• Bayesian Networks
• +۶ مورد دیگر

Anthony Constantinou is a Senior Lecturer at Queen Mary University of London, part of the School of Electronic Engineering and Computer Science. He leads the MInDS Research Group and the Bayesian AI Lab, focusing on causal machine learning for decision-making under uncertainty. His research spans healthcare, defense, sports, economics, and gaming, with a strong emphasis on Bayesian networks and causal inference. Research Interests: Causal machine learning, decision systems, Bayesian networks, uncertainty quantification, and their applications in healthcare, military defense, sports analytics, and economics. He collaborates with academia and industry to advance AI-driven decision-making systems. Grants: Noted for securing funding such as the Engineering and Physical Sciences Research Council grant (EP/S001646/1) for Bayesian AI research. His work emphasizes practical applications in diverse fields through collaborative projects. Labs & Teams: Head of the MInDS Research Group and Bayesian AI Lab, fostering interdisciplinary research in AI and decision systems.

Nicolò Cesa-Bianchiمشاهده پروفایل

استاد

• Machine Learning
• Online Learning
• Multi-Armed Bandits
• +۴ مورد دیگر

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, Department of Computer Science (Dipartimento di Informatica), and affiliated with the DEIB Department at Politecnico di Milano. His research focuses on foundational aspects of machine learning, particularly online learning, multi-armed bandits, reinforcement learning, and graph analytics. He is an ELLIS Fellow and a corresponding member of the Accademia Nazionale dei Lincei. Research interests include the design and analysis of algorithms for prediction, clustering, and online decision-making, with applications to digital markets, social networks, and bioinformatics. Notable contributions span cooperative online learning, multitask learning, and bandit algorithms. He co-authored the influential book Prediction, Learning, and Games (2006). Professional roles include Board member of ELLIS, co-director of the Milan ELLIS unit, and involvement in EU initiatives like ELSA (Secure & Safe AI) and ELIAS (AI for Sustainability). He teaches graduate courses on statistical methods, machine learning, and reinforcement learning, with a focus on theoretical foundations. Key awards: ELLIS Fellowship (2020), Corresponding Member of the Accademia Nazionale dei Lincei (Italian National Academy of Sciences). His work bridges theory and practice, addressing challenges in adaptive systems, market design, and algorithmic fairness. Current projects explore distributed learning, regret minimization in adversarial environments, and interpretable models.

Milena Dobrevaمشاهده پروفایل

مدرس ارشد

• Cultural Heritage Digitization
• Digital Libraries
• Media Literacy
• +۵ مورد دیگر

Dr. Milena Dobreva is a Senior Lecturer in Information Behavior at the Department of Computer and Information Sciences, University of Strathclyde, Scotland. She holds roles in the Faculty of Science and is affiliated with the Strathclyde iSchool. Her work focuses on innovation diffusion in cultural and scientific heritage sectors, media literacy, and combating misinformation. She has led projects funded by the European Commission, including the Bulgarian Romanian Observatory of Digital Media and the LEARN & EXCHANGE initiative to develop international MOOCs on digital transformation. Her research explores user needs in digital heritage, open innovation tools for cultural institutions, and digital skills training in Sub-Saharan Africa. Professional Activities Principal Investigator on projects addressing digital divide, GLAM innovation labs, and disinformation policy. Member of the Scientific Board of DARIAH and the Europeana Network Association. Peer reviewer for journals like Transformations: A DARIAH Journal and conferences such as ACM/IEEE-CS JCDL. Research Interests Innovation labs for GLAM sectors (Galleries, Libraries, Archives, Museums). Data spaces and digital transformation frameworks for cultural heritage. Information behavior in the context of disinformation and media literacy. Open science infrastructures and policy implementation. Grants & Projects Bulgarian Romanian Observatory of Digital Media (2022–2025): Combats disinformation through regional collaboration. LEARN & EXCHANGE (2022–2024): Develops multilingual MOOCs for digital transformation skills. eCHOIng (2021–2024): Uses open innovation to revitalize cultural heritage institutions. Labs & Collaborations Co-leads GLAM innovation labs research, focusing on digital divide mitigation and heritage digitization. Active in the CEDCHE (Common European Data Space for Cultural Heritage) initiative.

Paul Franzonمشاهده پروفایل

استاد

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +۴ مورد دیگر

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)

Emanuele Crisostomiمشاهده پروفایل

دانشیار

• Smart Grids
• Electric Vehicles
• Control Systems
• +۲ مورد دیگر

Emanuele Crisostomi is Associate Professor of Electrical Systems at the University of Pisa's Department of Energy, Systems, Territory and Constructions Engineering. He holds PhD and graduate degrees in Automation Engineering from the University of Pisa, with research visits to University of Cambridge, University of Leicester, and Hamilton Institute. His research bridges control theory, energy systems, and smart mobility. His research integrates: Smart grid control and renewable energy integration Electric vehicle charging infrastructure and routing algorithms Urban traffic modeling and optimization Nonlinear system dynamics and Lyapunov stabilization Machine learning applications in energy systems Crisostomi's publications demonstrate consistent innovation in electric vehicle-grid integration, with recent focus on predictive charging control, highway EV circulation patterns, and hydropower monitoring. His work shows increasing application of deep reinforcement learning for multi-regional power systems and hybrid network models for infrastructure monitoring. He leads research on IoT-enabled energy efficiency in buildings and contributes to epidemic modeling through network science. Current projects examine battery sizing optimization, charging station scheduling under uncertainty, and synergetic building management systems integrating environmental monitoring with energy conservation.