جستجوی استاد راهنمای دانشگاهی

• Automating Design and Diagnosis of Information Systems
• Computer Graphics
• Web Technologies

Konstantin Nikolaevich Kasyan serves as Associate Professor at the Department of Computer Systems and Networks within the Faculty of Computer Science and Technologies at Zaporizhzhia National Technical University, where he has maintained academic activity since 1998. He graduated with honors from Zaporizhzhia Machine-Building Institute's Faculty of Electronic Engineering in 1993, specializing in Radio Engineering with qualification as Radio Engineer. His Candidate of Sciences degree (defended 1998/1999) established his expertise in diagnostic methodologies for electronic systems. His research spans three core domains: automating design and diagnosis of information systems, computer graphics methodologies, and web technologies. This interdisciplinary focus manifests in practical applications ranging from hardware diagnostics to smart home systems. His work demonstrates consistent evolution from foundational electronics reliability research in the 1990s toward contemporary IoT and computer vision applications. His publication trajectory reveals distinct chronological phases: 1990s-2000s concentrated on electro-radio diagnostics and reliability engineering; 2000s-2010s expanded into computer graphics and text recognition; while 2010s-2021 shifted toward IoT integration, smart home technologies, and machine learning applications. This progression reflects both technological advancements and his adaptability across computing subfields. Kasyan teaches modern Internet technologies, computer graphics, computer systems design, and computational methods in scientific research, directly translating his research into pedagogical practice. His academic presence is maintained through Scopus, Web of Science, Google Scholar, and ORCID profiles, indicating active scholarly engagement despite the absence of formal awards or documented grants in available records.

Vasileios Megalooikonomouمشاهده پروفایل

استاد

• Database and Knowledge Systems
• Intelligent Information Systems
• Data Mining
• +۴ مورد دیگر

Vasileios Megalooikonomou serves as a Professor at the Department of Computer Engineering and Informatics within the School of Engineering at the University of Patras. He directs the Multidimensional Data Analysis and Knowledge Management Laboratory and maintains active teaching responsibilities across undergraduate and postgraduate programs. His research spans several interconnected domains in computer science and information systems. Dr. Megalooikonomou's work focuses on foundational database technologies while extending into advanced applications through Intelligent Information Systems and Data Mining . His expertise encompasses both theoretical frameworks and practical implementations in Pattern Recognition and Data Compression , with specialized applications in Biomedical Informatics and Multimedia systems. This multidisciplinary approach bridges traditional database theory with contemporary computational challenges. Within the University of Patras infrastructure, he leads the Multidimensional Data Analysis and Knowledge Management Laboratory , which serves as the operational hub for his research activities. His teaching portfolio includes core database courses (Databases I, Databases II, Database Laboratory), advanced algorithmic instruction (Data Mining and Learning Algorithms), and specialized postgraduate topics focusing on Spatial and Temporal Databases and Knowledge Mining. Office hours are maintained on Monday 11:00-13:00 and Wednesday 13:00-14:00 for student consultation.

Jinhui Wangمشاهده پروفایل

استاد

• Artificial Intelligence
• Very-large-scale Integration (VLSI) Circuits
• Three-dimensional Integrated Circuit (3D IC) Design
• +۶ مورد دیگر

Jinhui Wang is a Professor in the Department of Electrical and Computer Engineering at the University of South Alabama's College of Engineering. His research focuses on cutting-edge technologies in Artificial Intelligence , VLSI Circuits , and Neuromorphic Computing . Education: Postdoctoral work in VLSI Design at University of Rochester, NY, USA; Ph.D. and B.S. in Electrical Engineering from Beijing University of Technology and Hebei University, China. His work addresses 3D IC Design , Emerging Memory Systems , and Cooling Techniques for Electronic Devices , with recent publications emphasizing privacy-preserving AI hardware and intelligent memory architectures for mobile and embedded systems. Collaborative projects span applications in Wireless Sensor Networks , IoT , and UAV Electronic Subsystems .

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

Alan Hanjalicمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Affective Video Content Analysis
• Recommender Systems
• +۵ مورد دیگر

Alan Hanjalic is Professor of Computer Science at Delft University of Technology, holding the Antoni van Leeuwenhoek Chair and serving as Head of the Multimedia Computing Group and Head of the Department of Intelligent Systems within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Recognized as an IEEE Fellow and AAIA Fellow, he has been a driving force in multimedia information retrieval for over two decades. Education: 1995: Dipl.-Ing. in Electrical Engineering, Friedrich-Alexander University Erlangen-Nuremberg, Germany (specialization in motion estimation for video compression) 1999: PhD in Computer Science, Delft University of Technology (specialization in video indexing and search) Research Interests: Prof. Hanjalic’s research spans the broad domain of multimedia information retrieval , with pioneering contributions to affective video content analysis . By modeling videos as trajectories in the arousal-valence emotional space, he enabled indexing and retrieval based on emotional impact, laying the groundwork for an entire subfield. His group further advances socially-responsible recommender systems , cross-modal retrieval and generation , graph learning , and network influence prediction . Recent work also delves into fairness in recommendation and deep-learning-based side-channel security . Publication Trends: His latest publications (2024–2025) showcase a diversification into fairness and reproducibility in recommender systems, security-oriented deep learning for side-channel analysis, and advanced metrics for influence prediction in complex networks. These papers reflect both foundational algorithmic contributions and critical validation studies that strengthen the reliability of machine-learning approaches across multimedia and security domains. Scientific Recognition: IEEE Fellow (2016) – for pioneering contributions to multimedia information retrieval AAIA Fellow – acknowledging outstanding impact in artificial intelligence and multimedia Named Antoni van Leeuwenhoek Professor – university-wide recognition of research excellence Education, Mentorship & Leadership: Over 25 years at TU Delft, Prof. Hanjalic has served as lecturer, course coordinator, and chair of the Computer Science Board of Studies. He has promoted more than 30 PhD students and held leadership roles such as: Head, Intelligent Systems Department (2018–present) Member, EEMCS Career Development Committee Chair, TU Delft central appointment advisory committee for full professors Management team member, 4TU.NIRICT (Netherlands Institute for Research on ICT) TU Delft representative, ICT Research Platform Netherlands (IPN) Laboratory & Research Environment: He founded the Delft Multimedia Information Retrieval (DMIR) Lab , which evolved into the current Multimedia Computing (MMC) section . Under his guidance, the group has led national (FES, BSIK) and European Union projects—including a Network of Excellence—advancing responsible and human-centered multimedia technologies.

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.

Yung-Lyul Leeمشاهده پروفایل

استاد

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +۳ مورد دیگر

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.

Oh Jin Kwonمشاهده پروفایل

استاد

• Image Processing
• Image Coding
• Digital Watermarking
• +۷ مورد دیگر

Professor Oh Jin Kwon is a faculty member in the Department of Electronic Engineering at Sejong University since 1999. His research focuses on image/video analysis, compression algorithms, image quality enhancement, fusion techniques, watermarking, and steganography. He has developed patented algorithms for region adaptive image coding, image watermarking, backward compatible HDR image coding, and progressive image coding. Education: Ph.D. in Electrical and Computer Engineering from University of Maryland, College Park (1994) M.S. in Electrical Engineering from University of Southern California, Los Angeles (1991) B.S. in Electronic Engineering from Hanyang University, Seoul (1984) His extensive research includes image processing , watermarking , and cyber resilience . Recent publications explore deep learning applications in cybersecurity , image coding standards , and 360-degree imaging . His work has been published in Electronics (Switzerland) , Applied Sciences (Switzerland) , Drones , and other journals. Scientific Awards & Grants NSF Grant 91-00655 DACA 76-92-C-0079 DACA 76-89-C-0019 DACA 76-92-C-0009 Professor Kwon has advised numerous PhD and MS students in areas like Point Cloud Coding , JPEG Privacy & Security , and AI-based image coding . He leads research projects funded by institutions including the Korea Aerospace Research Institute and Electronics and Telecommunications Research Institute . His laboratory, the Video Communication Research Lab , focuses on JPEG systems standards , image security , and smart store technologies . Current research includes operator-friendly interface technology for unmanned vehicles and composite material cultural heritage data encryption.

• Computer Vision
• Multimedia Computation
• Machine Learning
• +۲ مورد دیگر

Litao Yu is a Part-Time Lecturer and Visiting Scholar at the Faculty of Engineering and Information Technology, University of Technology Sydney. Concurrently, he serves as a Senior Data Analyst at Australia's Department of Agriculture, Fishery and Forestry. His academic appointments include Research Fellow at UTS (2019-2024) and post-doctoral positions at Griffith University and Queensland University of Technology. Education: PhD from The University of Queensland (2013-2016) MPhil from Dalian University of Technology (2009-2012) BSc from Dalian Maritime University (2003-2007) Dr. Yu's research focuses on computer vision and machine learning with applications in agriculture, multimedia systems, and multimodal learning. His work spans few-shot learning, semantic segmentation, fine-grained recognition, and multimodal fusion techniques. Key application domains include animal welfare monitoring, aquaculture quality control, and travel information enhancement. His publications demonstrate strong thematic convergence around efficient visual recognition systems , with recurring focus on few-shot learning paradigms and attention mechanisms. Recent work shows increasing emphasis on agricultural applications of computer vision, including poultry monitoring, fish processing automation, and sheep tracking. Blockchain integration for supply chain transparency represents another emerging theme. No scientific awards are mentioned in available records. Teaching responsibilities at UTS include courses on Real-time Operating Systems and Shell Programming . No information is available regarding research grants, supervised students, or laboratory affiliations.

• Computer Vision
• Deep Learning
• Digital Forensics
• +۲ مورد دیگر

Mehdi Atamna is a Temporary Teaching and Research Attaché (ATER) at Lumière University Lyon 2, affiliated with the Imagine research team of the LIRIS laboratory. He is based at the university's Bron campus. His academic role involves both teaching responsibilities and research activities in computer vision. His primary research investigates deepfake detection methodologies, focusing on: Analyzing digital traces left by generative adversarial networks (GANs) Developing attention-based neural architectures for media forensics Examining compression artifacts in manipulated videos Building detection systems against information manipulation threats

• Artificial Neural Networks
• Traction Systems
• Building Automation
• +۴ مورد دیگر

Dr. Eng. Marek Daniel Dudzik serves as a Lecturer in the Department of Infotronics and Cybersecurity at the Faculty of Electrical and Computer Engineering, Cracow University of Technology. His academic profile demonstrates a strong research focus on artificial intelligence applications across diverse engineering domains, with particular expertise in neural network modeling for complex physical systems. Dr. Dudzik's research interests span multiple engineering disciplines, with primary focus on applying neural networks to solve challenging problems in materials science, particularly regarding aluminum foams and cellular materials under compression. His work bridges theoretical neural network development with practical applications in building automation systems, environmental monitoring, and mining engineering. The research portfolio reveals a consistent pattern of interdisciplinary work connecting electrical engineering principles with computational intelligence techniques. The publication record shows a clear trend toward increasingly sophisticated neural network applications across multiple domains. Early work focused on traction systems and electrical engineering applications, while more recent publications demonstrate expansion into building automation, environmental monitoring, and materials science. The research methodology consistently employs neural networks to model complex physical phenomena that are difficult to characterize through traditional analytical approaches, with particular emphasis on practical implementation and verification of results. Dr. Dudzik's work demonstrates strong connections between theoretical neural network development and practical engineering applications across diverse fields. His collaborations span multiple departments and institutions, reflecting the interdisciplinary nature of his research. The publication record indicates active participation in both theoretical development and practical implementation of neural network solutions for complex engineering problems.

• Video Coding for Machines
• Neural Network-Based Video Processing
• Immersive Video Coding
• +۵ مورد دیگر

Professor Marek Domański is a distinguished faculty member at Poznań University of Technology, holding the position of Professor at the Institute of Multimedia Telecommunications within the Faculty of Computing and Telecommunications. With over four decades of academic career since completing his dissertation in 1983, he has established himself as a leading researcher in video coding and processing. His research primarily focuses on advanced video coding techniques, particularly Video Coding for Machines (VCM), neural network applications in video processing, immersive video coding, and multiview video compression. Professor Domański has made significant contributions to the field through his extensive publication record and active participation in international standardization efforts, particularly at MPEG meetings. Analysis of his recent publications (2020-2025) reveals a strong emphasis on machine-oriented video coding, with numerous contributions to MPEG standardization activities. His work demonstrates a consistent evolution from traditional video coding toward specialized techniques for machine vision applications, incorporating artificial neural networks to improve coding efficiency and processing capabilities. As an academic supervisor, Professor Domański has guided 27 doctoral students to completion, with recent dissertations focusing on cutting-edge topics in video processing and coding. His research group at PUT maintains active international collaborations, particularly with South Korean institutions like ETRI, reflecting the global relevance of his work. Professor Domański's research has practical applications in virtual reality, free-viewpoint television, and machine vision systems, with numerous technical reports indicating ongoing research projects focused on improving machine vision coding techniques. His work bridges theoretical advances with practical implementations, contributing significantly to both academic knowledge and industry applications in video technology.

Vincent Tourreمشاهده پروفایل

دانشیار

• Urban Data Visualization
• Morphological Analysis of Urban Spaces
• Natural Lighting Simulation
• +۵ مورد دیگر

Vincent Tourre is an Associate Professor in the Department of Computer Science at Centrale Nantes, affiliated with the CRENAU Research Team (UMR CNRS 6051), a joint unit involving CNRS, Université Grenoble Alpes, and multiple architecture schools. His academic career spans urban data visualization, morphological analysis of urban spaces, and natural lighting simulation. His research interests focus on Urban Data Visualization , Morphological Analysis of Urban Spaces , and Virtual Reality Applications for urban environments. Recent work examines 360° image perception, urban soundscapes classification, and immersive geospatial data visualization. Tourre's publications demonstrate consistent output in top venues including ISPRS Annals, International Journal of Geographical Information Science, and the Journal of the Acoustical Society of America. Tourre's 15 most recent articles reveal a strong trajectory in interdisciplinary urban computing , with growing emphasis on machine learning applications for urban data analysis (particularly in acoustics and visual perception) since 2020. His work increasingly integrates virtual reality with multi-sensor urban monitoring systems. As course leader at Centrale Nantes, Tourre teaches urban data analysis, scientific visualization, and knowledge representation. His research training contributions include doctoral supervision and development of the CORAULIS VR Platform for immersive urban studies. The CRENAU team's collaborative structure provides access to extensive urban datasets and interdisciplinary expertise across architecture, computer science, and environmental studies.

Vincent Itierمشاهده پروفایل

مدرس

• Multimedia Security
• Digital Forensics
• Machine Learning
• +۴ مورد دیگر

Vincent Itier serves as a Lecturer at IMT Nord Europe, where he is affiliated with the CRIStAL research laboratory (UMR CNRS 9189). His office is located in Building ESPRIT, Scientific City, at the Villeneuve d'Ascq campus. He is a member of the SIGMA research team and actively contributes to the academic community through teaching, research supervision, and scholarly publications. Dr. Itier's research interests span across multimedia security, digital forensics, and machine learning, with particular emphasis on detecting and understanding image manipulations. His work addresses critical challenges in digital media authenticity, including deepfake detection, photomontage identification, and steganalysis. He investigates how machine learning techniques can be leveraged to improve robustness against increasingly sophisticated image manipulation methods, with applications in combating 'fake news' and verifying digital content authenticity. His publication record demonstrates a consistent focus on digital image forensics, with recent work exploring deep learning approaches for detecting splicing, analyzing noise residuals in deepfakes, and developing robust steganalysis techniques. His research shows a clear evolution from traditional image processing methods toward more sophisticated deep learning frameworks that can handle the complex challenges of modern digital media manipulation. Supervised Minh Thong Doi's thesis on 'Deepfake detection: combining noise and semantic features and improving generalization to new generators' Currently offering M2 Internships and Post-doc positions for 2025-2026 Leading research within the ANR TSIA CI2(IA) project which aims to develop new tools for detecting and understanding image manipulation Dr. Itier maintains an active research presence through his GitHub profile (vitier) and professional website, and can be contacted via email at vincent.itier@imt-nord-europe.fr for potential collaborations or research opportunities.

• Human-Computer Interaction
• Usability Engineering
• Web Development
• +۵ مورد دیگر

Lee Griffiths is a seasoned Lecturer in Computer Science at the University of Salford's School of Science, Engineering & Environment , where he has held academic positions since 1998. As Programme Leader for Computer Science, he oversees the core program that serves as a gateway to Software Engineering and Cybersecurity specialisms, working closely with School Management and Associate Deans. His institutional affiliations include the Informatics Research Centre and collaborative work with the School of Health & Society on virtual environment applications for occupational therapy education. Griffiths' research focuses on Human-Computer Interaction , Usability Engineering , and Web Development , with particular emphasis on cross-cultural web usage patterns. His work examines how users from different linguistic backgrounds (Arabic RTL vs English LTR, Chinese vs European) interact with digital interfaces, utilizing innovative methodologies like mouse tracking. He has also made significant contributions to learning technologies , developing tools like MediaSurface for BBC North West Tonight and investigating flexible learning approaches in higher education. His publication record spans over two decades with consistent output in user interface and learning technology fields. Recent work shows a trend toward practical applications of HCI research in media, emergency planning, and international development contexts. His 2019 mouse tracking study represents the culmination of his cross-cultural interface research that began with his 2007 Chinese-European user comparison study. Salford Learning Technology Fellow As an educator, Griffiths has supervised hundreds of student projects across all academic levels and developed innovative teaching approaches that prioritize student engagement and real-world application. His commitment to equality, diversity, and inclusion manifests through encouraging students to explore diverse cultural contexts in their work, resulting in more socially relevant technological solutions. He maintains exceptional student relationships through flexible communication channels including Discord alongside traditional university platforms, ensuring accessibility for students balancing academic and external commitments.