جست‌وجوی استاد و پژوهشگر

Miloš Stojmenovićمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Image Processing
• +۶ مورد دیگر

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Natural Language Processing
• Health Informatics
• Machine Learning
• +۳ مورد دیگر

Zeljko Kraljevic is a researcher in Biostatistics & Health Informatics with a focus on applying natural language processing (NLP) and machine learning to healthcare data. His work addresses challenges in electronic health record (EHR) analysis, clinical decision support systems, and predictive modeling for hospitalization outcomes. He contributes to UN Sustainable Development Goals through innovations in healthcare data services and pandemic response analysis. Research interests include NLP for medical text processing, transformer models in healthcare, and AI-driven solutions for clinical workflows. Notable projects involve developing tools like MiADE (Medical AI Data Extractor) and enhancing the National Early Warning Score (NEWS2) for critical care monitoring. Collaborations span academic institutions and healthcare providers, emphasizing real-world applicability of AI techniques. Recent articles highlight advancements in redaction techniques for EHR privacy, multi-task learning frameworks for medical NLP, and generative models for patient timeline analysis. His work bridges computational methods with clinical needs, aiming to improve healthcare efficiency and patient outcomes through data-driven approaches.

• Signal Processing
• Communications Engineering
• Radar Systems
• +۵ مورد دیگر

Assoc. Professor Qinghua Guo holds a position at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering. He earned his PhD from City University of Hong Kong (2008) and has been affiliated with the University of Wollongong since 2019. His research focuses on signal processing, communications, radar systems, machine learning, and optical sensing, with notable contributions to graphical models and Bayesian inference. Prof. Guo is recognized as a top 2% influential scientist globally (Stanford University) and a leader in Electronics and Computer Science (Research.com). He serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters, among other roles. His teaching spans undergraduate to postgraduate levels, including specialized subjects in engineering and information sciences. His recent research emphasizes integrated sensing and communication systems, federated learning, and secure beamforming in 5G/6G networks. He has supervised over 30 PhD and Master’s students in areas like MIMO systems, signal processing for IoT, and quantum sensing. Key funded projects include AFDM-SCMA (2025–2028) for IoT connectivity and Grant-Free Multiple Access for LEO satellite networks (2023–2027). His work bridges theoretical advancements with practical applications in robotics, energy systems, and defense technologies.

• Pediatrics
• Obesity Management
• Metabolic Disorders
• +۲ مورد دیگر

Daniel Weghuber is Professor and Department Chair at Paracelsus Medical University's Department of Pediatrics. His research focuses on pediatric obesity management, metabolic disorders, and pharmacotherapy. Research areas include: Anti-obesity medications for youth Neonatal laboratory diagnostics Clinical guideline development Metabolic function in inflammatory bowel disease Publications emphasize evidence-based obesity management, novel therapeutic approaches, and optimization of pediatric care protocols. Recent works address medication efficacy in adolescents and minimally invasive diagnostics. He leads projects on mitochondrial function in IBD and EURAS (epilepsy research). Current research examines climate impacts on migration through machine learning models.

• Algorithms
• Graph Neural Networks
• Problem decomposition techniques
• +۴ مورد دیگر

Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.

Eduardo Blancoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۷ مورد دیگر

Eduardo Blanco is an Associate Professor in the Department of Computer Science at the University of Arizona. His research focuses on advancing Natural Language Processing (NLP) with an emphasis on understanding negation, hate speech dynamics, and computational linguistics applications. He explores how language models can better interpret ambiguous statements, predict conversation incivility, and counteract harmful online content through structured responses. Blanco’s work spans theoretical and applied NLP domains, including semantic role labeling, sentence embedding evaluation, and biomedical text analysis using protein language models. His research often integrates large language models with task-specific frameworks to address challenges in zero-shot learning and negation robustness. Key areas of investigation include: Negation processing and affirmative interpretation Social media discourse dynamics and hate speech detection NLP for biomedical applications (glycosylation site prediction) Semantic similarity alignment in sentence embeddings His recent articles highlight advancements in counterhate speech strategies, temporal QA systems, and LLM-based peer review tools. Notably, he secured the NSF CAREER Award for studying negation through positive interpretations (2022). Collaborative efforts focus on interdisciplinary teams addressing challenges in computational linguistics, with grants supporting projects like the ALIGN-SIM benchmarking framework and BEMEAE event argument extraction system.

• Machine Learning
• Spatial Data Mining
• AI in Mental Health
• +۴ مورد دیگر

Dr. Saida Elmi is an Assistant Professor Adjunct at Yale University School of Medicine and an Assistant Professor at the University of New Haven. She holds a Ph.D. in Computer Science from the National School of Mechanics and Aerotechnics (2017), followed by postdoctoral research at Korea University of Technology and Education (2017–2018) and National University of Singapore (2018–2021). Her research focuses on AI applications in healthcare, transportation systems, and spatial data mining. She developed the Automated Test of Embodied Cognition (ATEC), an AI-driven system using motion capture and machine learning for mental disease detection. Key contributions include predicting taxi fares, travel times, and energy consumption in transportation networks using deep learning architectures like RNNs and CNNs, and designing group-oriented recommendation systems for points-of-interest (POIs) using graph convolution networks and attention mechanisms. Education : Ph.D., Computer Science, National School of Mechanics and Aerotechnics (2017) Postdoctoral Research, Korea University of Technology and Education (2017–2018) Postdoctoral Research, National University of Singapore (2018–2021) Her research interests span machine learning, spatial data mining, and AI ethics. She has pioneered frameworks for spatio-temporal data analysis, including a transfer learning approach for travel time prediction in data-scarce regions. Her work on energy consumption modeling and POI prediction has been published in top-tier conferences like WWW, MOBIQUITOUS, and ECIR. Dr. Elmi’s interdisciplinary projects integrate AI with healthcare and urban systems, emphasizing automated cognitive assessment and smart city technologies. Her current efforts focus on refining action recognition algorithms for clinical applications and optimizing recommendation systems for group mobility decisions.

Emilio Serranoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Dr. Emilio Serrano is an Associate Professor at the Department of Artificial Intelligence, Technical University of Madrid (UPM). He holds an M.Sc. in Computer Science from the University of Murcia (2006) and a Ph.D. with European Mention from the same institution (2011). His research focuses on AI ethics, machine learning, bioinformatics, and multi-agent systems. He has authored over 50 international publications, including 10 in top-tier JCR journals. He has participated in EU projects like FP7’s SMARTOPENDATA, EUROSentiment, and Omelette. Dr. Serrano has received the National Research Fellowship (FPU) and the Extraordinary Ph.D. Award. His teaching spans 20+ education science courses, 12 university modules, and postgraduate seminars. His work integrates AI with social simulations, environmental forecasting, and healthcare applications, emphasizing ethical AI and bias mitigation. Awards: FPU Fellowship (2008), Extraordinary Ph.D. Award (2012) Grants: FP7 Projects (SMARTOPENDATA, EUROSentiment, Omelette) Labs/Teams: Active in AI ethics, bioinformatics, and multi-agent systems research

• Data Science
• Artificial Intelligence Explainability
• Web Science
• +۵ مورد دیگر

Marco Brambilla is a Professor at Politecnico di Milano and a Lecturer at Università della Svizzera italiana (USI) . He leads the Data Science Lab at Politecnico di Milano's Department of Electronics, Information and Bioengineering (DEIB) and serves as director of Computer Science and Engineering B.Sc. and M.Sc. curricula . Research focus on data science, AI explainability, web science, crowdsourcing, model-driven development, and social media analytics Co-inventor of the OMG IFML standard and holder of 2 patents on crowdsourcing and multi-domain search Founder of four startups: WebRatio, Fluxedo, Servitly, and Quantia His recent publications explore: Graph-based Retrieval Augmented Generation for energy efficiency Human-AI interaction analysis through live streaming platforms Model-driven approaches for multiexperience AI interfaces Explainable AI with human-in-the-loop validation Scientific contributions include: Best paper awards at international conferences Associate editor role at SIGMOD Records Editorial contributions to the PERISCOPE H2020 project on pandemic impacts He actively participates in European research initiatives such as: TRIGGER project on EU global governance PERISCOPE for pandemic preparedness

• Data Management
• Database Systems
• Graph Data Management
• +۴ مورد دیگر

Professor Chengfei Liu is a faculty member at Swinburne University of Technology, leading the Web and Data Engineering research group and serving as focus area leader for Knowledge and Data Intensive Systems in the SUCCESS Centre. His research focuses on web data management, advanced database systems, graph data, and workflow models. He has held academic positions at the University of South Australia, University of Technology Sydney, and the University of Queensland's DSTC. His work spans data management, distributed systems, and information systems, with notable contributions to cohesive subgraph discovery and efficient algorithms. Research interests include keyword queries, uncertain data, graph databases, and artifact-centric workflows. His awards include the Vice-Chancellor's Research Excellence Award (2007) and multiple best paper/demo awards at top conferences. Professor Liu supervises numerous PhD students and holds grants from the Australian Research Council (ARC), including projects on dynamic networks, heterogeneous graphs, and information resilience. He has authored over 297 publications, with recent work addressing topics like Bitcoin transaction prediction, federated subspace clustering, and multimodal eating disorder detection. His professional roles include program committee memberships at SIGMOD, ICDM, and CIKM, reflecting his influence in data science and database research.

Chaoxiong Yeمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Neuroscience
• Visual Memory
• Working Memory
• +۳ مورد دیگر

Dr. Chaoxiong Ye is an Academy Research Fellow (PI) at the Department of Psychology, University of Jyväskylä. He holds two PhDs: one in Cognitive Science (2015–2018) and another in Psychology (2018–2020). He holds four docentships (Associate Professor titles) at Finnish universities in Cognitive Neuroscience and Cognitive Psychology. As a PI, he has secured over €1.5M in funding and supervises nine doctoral students. He serves on editorial boards of BMC Psychology and PLOS ONE , and fosters international collaborations with leading institutions globally. Education: PhD in Cognitive Science, University of Jyväskylä (2015–2018) PhD in Psychology, University of Jyväskylä (2018–2020) Master’s in Applied Psychology, MinNan Normal University (2012–2015) Research Interests: Focuses on cognitive and neural mechanisms of visual memory, working memory, and emotional face processing. Current projects include studying memorability’s impact on long-term memory and developing diagnostic strategies for memory disorders. Principal Investigator: Cognitive and Neural Mechanisms for Memorable Visual Stimuli (€0.93M, 2023–2027) Previous project: Two-Phase Resource Allocation Model of Visual Working Memory (2020–2023) Publications: Over 110 publications in top journals, emphasizing working memory mechanisms, emotional interference, and neural basis of depression. Notable contributions include studies on retro-cue effects, dimension-based attention, and face distractor impacts. Awards & Grants: Secured €1.5M+ in grants from the Research Council of Finland. Recognized for editorial work and international collaborations. Labs & Teams: Leads Ye’s Lab at the University of Jyväskylä, fostering research on cognitive processes and neural mechanisms.

Frauke Liersمشاهده پروفایل

استاد

• Optimization under Uncertainty
• Distributionally Robust Optimization
• Quantum Computing
• +۵ مورد دیگر

Prof. Dr. Frauke Liers holds the Professorship of Optimization under Uncertainty & Data Analysis at the Department of Data Science (DDS), Friedrich-Alexander-University Erlangen-Nürnberg. Her research focuses on robust and distributionally robust optimization, mathematical programming, and applications in energy systems, healthcare logistics, and quantum computing. Email: frauke.liers@fau.de ResearchGate: Frauke Liers Research Interests span optimization under uncertainty, data-driven mathematical programming, and interdisciplinary applications. Key areas include: Distributionally robust optimization with scenario reduction and chance constraints Quantum computing optimization for gate routing and noise suppression Energy system modeling (photovoltaics, gas networks, electricity networks) Healthcare logistics (patient transport scheduling under uncertainty) Nanoparticle technology and chemical process optimization Recent Publications emphasize: Advancements in quantum circuit optimization (2025) Explainable optimization methods (2024) Robust approaches for particle precipitation control (2024) Dynamic trajectory optimization (2023) Time-expanded models for network flows (2022)

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +۷ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

• Network Science
• Information-Centric Networking (ICN)
• Urban and Transportation Networks
• +۷ مورد دیگر

Wei Koong Chai is a Senior Lecturer in the Department of Computing and Informatics at Bournemouth University and Deputy Head of Department at University College London (UCL), also within the Department of Computing and Informatics. He has held these academic positions since 2016 and 2024 respectively, demonstrating sustained contributions to higher education and research leadership. His research focuses on network science, information-centric networking (ICN), urban and transportation networks, smart grids, and resilience in complex systems. Key areas include traffic congestion modeling using SIR epidemic models, robustness assessment of urban road networks, deep learning for traffic prediction, and content delivery resilience in distributed caching systems. His work bridges theoretical network models with practical applications in smart cities and critical infrastructure. The recent publications (2021–2025) highlight a strong trajectory in applying network analytics, machine learning, and distributed systems to urban mobility and cyber-physical systems. Themes include spatio-temporal prediction, multi-scale topological analysis, and adaptive communication in flying ad hoc networks. His collaborations span institutions and disciplines, indicating interdisciplinary engagement. Scientific Awards: No scientific awards listed in the provided text. Wei Koong Chai has advised students and researchers in computing and network systems, though specific names are not listed. He has contributed to peer review, notably for PLOS Complex Systems , and is involved in impactful research projects related to smart grids, urban analytics, and information resilience. His work has been supported through institutional affiliations and collaborative grants, though specific funding sources are not detailed. He is associated with research labs and teams focused on network science, smart cities, and information-centric systems at both Bournemouth University and UCL. His future work likely extends into AI-driven urban infrastructure optimization, resilient IoT networks, and next-generation content delivery architectures.

• Artificial Intelligence
• Machine Learning
• Energy Systems
• +۳ مورد دیگر

Elissaios Sarmas is a researcher in the field of AI and machine learning applications for energy systems and smart cities. He has collaborated extensively with researchers like Vangelis Marinakis, Haris Ch. Doukas, and Ioannis Papias across institutions. Research Interests: AI in Energy Sector Smart Grid Analytics Data-Driven Decision Making Demand Response Programs Energy Poverty Mitigation Climate Change Adaptation Publication Trends: His recent work focuses on ensemble AI models for energy measurement, clustering methodologies for electricity loads, and large language models in energy digital twins, covering 2023-2025. Articles span journals like IEEE Access , Applied Soft Computing , and Information Sciences .