جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Jonathan Wu is a distinguished academic and Professor in the Faculty of Engineering at the University of Windsor. He is recognized for his leadership in artificial intelligence, computer vision, and signal processing. His research bridges theoretical advancements with practical applications, including maritime surveillance using High-Frequency Surface Wave Radar (HFSWR) and cybersecurity protocols for vehicular networks. Wu holds notable accolades such as Fellow of the Canadian Academy of Engineering (2019) and a Canada Research Chair (2018). His work spans interdisciplinary areas such as medical imaging, robotics, and data science. Notable contributions include developing novel algorithms for semantic segmentation, authentication protocols, and radar-based target detection systems. Wu has advised graduate students like Ashirbani Saha and Kaushik Ray on projects involving facial recognition and drunk-driving prevention technologies. His research emphasizes real-world impact, with funding for AI integration in manufacturing and contributions to international conferences and journals.

• Adaptive Learning Systems
• Applied Artificial Intelligence
• Learning Analytics
• +۱۲ مورد دیگر

Professor Alexandra I. Cristea is a faculty member in the Department of Computer Science at Durham University, serving as Deputy Executive Dean (Postgraduate Research) in the Faculty of Science. She founded the Artificial Intelligence in Human Systems research group and leads the SCENE Lab , focusing on adaptive, personalized, and gamified educational technologies. Alan Turing Academic Liaison for Durham N8 CIR Digital Humanities Team Lead IEEE European Public Policy on ICT Member Research Interests include adaptive learning systems, gamification frameworks, user modeling, semantic/social web applications, and AI-driven educational analytics. Her work spans learning analytics, VR/XR environments, and ethical AI, with over 350 publications and 7100+ Google Scholar citations. Recent Publications highlight AI in education, including MOOC dropout prediction, XAI frameworks, VR safety training, and sentiment analysis tools. Notable collaborations include the Weizmann Institute, British Council, and industry partnerships with Distinctive Publishing. Scientific Awards BCS Fellow HEA Fellow IEEE Senior Member EATEL Founding Member Associate Editor roles at ACM, IEEE, and Frontiers Supervision involves mentoring postgraduate students in AI, educational technology, and data ethics. Her labs and teams, such as the SCENE Lab, drive innovations in neuro-enhanced education and cultural diversity initiatives like the TechUP project.

• Machine Learning
• Materials Science
• Physics
• +۳ مورد دیگر

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut (Storrs). Her research focuses on computational science applied to physical sciences, particularly machine learning for materials, physics, and chemistry. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.A. in Applied Mathematics/Computer Science from Harvard College. Before joining UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory Group. Her work emphasizes developing scalable models from molecular dynamics data, model reduction for nonlinear systems, and addressing challenges in scientific datasets (e.g., small, clustered, or imbalanced data). Research Interests : Machine Learning for Materials Science, Data-Driven Modeling, Uncertainty Quantification, Chemical Reaction Networks, and Multiscale Simulation. Awards : Best Instructor Award (Stanford, 2015/2016), Best Student/Postdoc Presentation Award (Materials Research Society, 2015). Teaching : Taught courses on Machine Learning for Physical Sciences, Computer Architecture, and computational methods at UConn and Stanford. Known for interdisciplinary approaches and student engagement. Labs/Teams : Leads research on computational modeling and hosts the Materials and Megabytes podcast, featuring interdisciplinary discussions with leading researchers.

• Wearable Computing
• Health Informatics
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Oliver Amft is a Professor at the University of Freiburg, leading the Intelligent Embedded Systems Lab since 2022. His work focuses on wearable computing, health informatics, and AI-driven solutions for medical applications. He has been involved in projects like Eghi (enhanced health intelligence for personalized behavior strategies) and FederLeicht (learning support in extended reality). His research spans biomedical signal processing, machine learning for healthcare, and human-computer interaction. Notable contributions include developing wearable sensors for disease detection (e.g., inflammatory bowel disease), AR-guided surgical systems, and privacy-aware data integration frameworks (e.g., FHIR standards). Education: Ph.D. in Computer Science (assumed based on role). Research interests include real-time health monitoring, digital twins for patient care, and AI models for clinical decision support. His publications highlight advancements in medical image segmentation, bioacoustic analysis, and synthetic data generation for activity recognition. He collaborates with institutions like the Department of Medicine and Engineering disciplines. Current projects emphasize cross-modal integration in medicine and nanopore-based diagnostics.

Wolfمشاهده پروفایل

استاد

• Civil Law
• European Private Law
• International Civil Procedure
• +۳ مورد دیگر

Prof. Dr. Wolf is a full Professor at Leibniz University Hannover, holding the Chair of Civil Law, German, European and International Civil Procedure Law within the Faculty of Law. His affiliations include the Institute for Procedural and Attorney Law and the Institute for International Law. He leads research initiatives, supervises moot court teams, and organizes academic events such as the 'Studentenfutter' lecture series and the Hans Soldan Moot Court competition. Recent activities include publishing newsletters, hosting conferences on advocacy, and engaging in social media outreach via LinkedIn and Instagram. Research Focus: Civil procedure law, international legal frameworks, and legal informatics Key Projects: Development of legal education programs, participation in interdisciplinary research networks, and advocacy science initiatives Awards: No specific prizes mentioned in the text. Office details include Königsworther Platz 1, Hannover, with contact via lg.zpr@jura.uni-hannover.de.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Jakub Neumann holds the following positions at the University of Gdańsk: Associate Professor, Division of Artificial Intelligence, Institute of Informatics, Faculty of Mathematics, Physics and Informatics Deputy Director of the Institute for Development and Promotion, Institute of Informatics Deputy Dean for Education, Faculty of Mathematics, Physics and Informatics His research interests include: Artificial Intelligence and Machine Learning (focus: imbalanced data classification using MapReduce) Autonomous Systems (path planning for autonomous surface vehicles) Educational Technology (XML-based tools for online course development) Theoretical Computer Science (tree automata and complexity) Publication trends: Neumann's work evolved from theoretical computer science (2002) to e-learning (2003-2007), autonomous systems (2012-2013), and recently big data and machine learning (2017-2018). His recent focus is on distributed computing solutions for imbalanced datasets, reflecting the growing importance of big data in AI applications.

Jun Liمشاهده پروفایل

مدرس

• Data Analytics
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. Jun Li is a Lecturer in Mathematics and Computer Science at Cranfield University's Centre for Computational Engineering Sciences. His expertise spans Data Analytics, Machine Learning, Deep Learning, and Mathematical Modelling applied to diverse domains including aerospace, urban planning, and cybersecurity. BSc in Computer Science from QingDao University MSc in Software Engineering from LondonMet University PhD in Neural Networks from LondonMet University Dr. Li's research focuses on integrating Machine Learning with High Performance Computing and Mathematical Modelling. Key areas include: Large-scale Neural Network simulations in Neuroscience Deep Learning for aircraft engine defect detection via computer vision Neural Network language modeling with noise resilience Transport flow analysis using Complex Networks Urban agglomeration formation and imbalanced economy recovery modeling His recent publications highlight interdisciplinary applications of Machine Learning in transport systems, aerospace diagnostics, and cybersecurity. These works emphasize data-driven methods, GANs, and predictive analytics for real-world challenges. Current grants include funding from China's National Key Research and Development Program (2016YFC0503506) and CAS's Bingwei Excellent Young Scientists Programme (2015RC202). Dr. Li supervises MSc and PhD students in Artificial Intelligence, Machine Learning, and Big Data analytics. He collaborates with Cranfield's Centre for Computational Engineering Sciences, leading projects like: Airport service quality evaluation using Skytrax reviews Urban land-use transport modeling for policy impact analysis Imbalanced economic recovery frameworks post-disasters

• Computer Vision
• Pattern Recognition
• Computer Aided Diagnosis
• +۱ مورد دیگر

Łukasz Jeleń is a researcher at the Department of Computer Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology. He works in the intersection of computer vision and biomedical image processing, focusing on medical diagnostics applications. Research Interests: Computer Vision, Pattern Recognition, Computer Aided Diagnosis, Biomedical Image Processing Contact: lukasz.jelen@pwr.edu.pl | C-3 building, office 230 | ul. Z. Janiszewskiego 11/17, Wrocław, Poland His recent publications demonstrate expertise in applying machine learning techniques to histopathology analysis and fluorescence imaging. Key trends include developing texture description algorithms, CNN-based dot counting systems, and evolutionary undersampling methods for imbalanced medical data. Current research focuses on improving breast cancer diagnostics through advanced pattern recognition frameworks and feature set optimization strategies. His work bridges computer science methodologies with biomedical imaging challenges.

• affective computing
• emotion recognition from physiological data
• machine learning
• +۳ مورد دیگر

Dr. Stanisław Saganowski serves as a Professor at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, specializing in the Department of Artificial Intelligence. With expertise in affective computing and machine learning, he focuses on emotion recognition from physiological data, group evolution prediction in complex networks, and mobile technology applications. His research interests include: Affective Computing Emotion Recognition using Wearables Machine Learning for Imbalanced Data Network Analysis of Social Dynamics Predictive Modeling in Complex Systems Mobile Healthcare Applications Recent publications demonstrate interdisciplinary approaches combining data science with behavioral analysis and network theory. While no formal awards are listed, his work contributes to both theoretical advancements and practical implementations in human-centric AI systems.

• Control systems
• System identification
• Pattern recognition
• +۳ مورد دیگر

Professor Ewaryst Rafajłowicz is affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, serving as Head of the Department of Control Systems and Mechatronics. His research spans control systems, pattern recognition, image processing, and spatio-temporal dynamics, with recent work focusing on iterative learning control, signal classification, and vibration analysis in industrial systems. Control systems System identification Pattern recognition Image processing Statistical process control Mechatronic control Recent publications highlight his expertise in functional data analysis, Bernstein polynomial applications, and shape-preserving descriptors for classification tasks. He has contributed to both theoretical advancements in control algorithms and practical implementations in industrial settings, particularly in vibration damping and mechatronic systems.

Hugo Alonso Vilares Monteiroمشاهده پروفایل

استادیار

• Dynamical Systems Analysis
• Identification and Control
• Neural Networks
• +۴ مورد دیگر

Hugo Alonso Vilares Monteiro is an Assistant Professor at the Universidade Lusófona do Porto, affiliated with the Faculty of Economic, Social and Business Sciences, and an Invited Assistant Professor at the Department of Mathematics, University of Aveiro. His research bridges mathematical modeling with applications in economics, marketing, and computational statistics. Education: BSc (2002) and PhD (2008) in Applied Mathematics, University of Porto. Monteiro specializes in dynamical systems analysis , neural networks , and statistical modeling , with recent work focusing on consumer behavior prediction in the wine industry, time series forecasting for tourism, and imbalanced data techniques in machine learning. His interdisciplinary approach integrates mathematical rigor into business and societal challenges. Collaborative projects include medical applications (e.g., neuromuscular blocking control) and hybrid methods for economic modeling. He contributes to the INTREPID LAB, emphasizing research for business and society. Monteiro’s work reflects a blend of theoretical mathematics and practical problem-solving across domains. His recent publications (2020–2025) highlight expertise in Control Systems (medical and industrial applications) Statistical Learning (over-sampling, forecasting) Economic Modeling (price sensitivity, consumer willingness to pay)

• Edge Computing
• Federated Learning
• Internet of Things
• +۵ مورد دیگر

Sasu Tarkoma is a Professor at University of Helsinki specializing in next-generation computing systems with over two decades of research experience. His work bridges theoretical computer science with practical applications in smart cities, environmental monitoring, and industrial systems. His research interests focus on edge computing infrastructure , federated learning architectures , and AI-driven environmental monitoring systems . Tarkoma's work addresses critical challenges in distributed intelligence, particularly in resource-constrained environments where privacy, energy efficiency, and real-time processing are paramount. Recent work explores the integration of large language models with edge systems and novel approaches to 6G network architectures. The publication record reveals a strategic evolution from foundational mobile computing research to cutting-edge work at the intersection of AI, networking, and sustainability. His recent articles demonstrate particular strength in solving practical implementation challenges for federated learning in industrial settings and developing energy-efficient approaches to AI deployment in constrained environments. Tarkoma maintains an extensive collaborative network across European institutions, frequently partnering with researchers from Aalto University, University of Oulu, and international partners in Asia. His work shows increasing emphasis on environmental applications, particularly air quality monitoring systems using UAVs and mobile sensors.

• Underwater Image Enhancement
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Alzayat Saleh is an active academic researcher specializing in computer vision and deep learning applications for underwater and agricultural domains. He has co-authored over 28 publications from 2015-2025, focusing on self-supervised learning, image enhancement, and environmental impact mitigation. Key collaborators: Mostafa Rahimi Azghadi, Marcus Sheaves, Dean R. Jerry, Alex Olsen Research Focus: His work addresses critical challenges in underwater visual analysis and precision agriculture , including: Fish segmentation and tracking without human annotations Weed detection in long-tailed datasets Environmental monitoring through adaptive deep learning Herbicide reduction in robotic spot-spraying systems The 2024-2025 publications reveal a trend toward self-supervised and semi-supervised approaches for ecological applications, with recent work on 3D underwater reconstruction and real-time shadow removal.

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.

Ante Panjkotaمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Signal Processing
• +۳ مورد دیگر

Assistant Professor Ante Panjkota holds a Ph.D. and is affiliated with the Department of Information Sciences and Technologies at the University of Zagreb. His office is located in Office 45, and he can be reached at apanjkot@unizd.hr. Regular consultations are held every Friday from 9:30 to 11:30 AM via prior email notification. His academic background includes teaching courses such as Basics of Information Technology, Programming Basics, Introduction to Network Systems, Advanced Programming, Data Mining, and Information Search and Retrieval. His research interests span programming, algorithms, machine learning theory, data mining, signal processing, and business intelligence with notable contributions to e-government frameworks and motion analysis systems. Key research activities include the TRIPLE project focusing on interdisciplinary research practices. His publications reflect expertise in data-driven methodologies, including neural networks for financial forecasting, e-government maturity models, and innovative motion-tracking systems. He has also explored applications of inductive logic programming in robotic vision and developed frameworks for analyzing human motion in sports contexts. While no scientific awards are explicitly listed, his work demonstrates sustained engagement with cross-disciplinary challenges in information sciences and technology. His teaching and research activities are further detailed in his CV available in Croatian and English formats.