جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Syntactic Analysis
• +۳ مورد دیگر

Şaziye Betül Özateş is an Assistant Professor at the Institute for Data Science and Artificial Intelligence at Boğaziçi University, specializing in Natural Language Processing (NLP) and Machine Learning. She holds a BSc, MSc, and PhD in Computer Engineering from Boğaziçi University, where she was advised by Dr. Arzucan Özgür and Dr. Tunga Güngör. Previously, she was a researcher at the University of Stuttgart’s Institute of Natural Language Processing and a post-doctoral fellow at KUIS AI Center focusing on procedural language learning from natural instructions. Her research interests include natural language processing, computational linguistics, syntactic analysis, and deep learning. She has developed several influential resources, including the BOUN Treebank (9,761 syntactically annotated Turkish sentences), IMST Treebank, and PUD Treebank. Her tools such as BOUN-Pars (a Turkish dependency parser) and the semi-supervised deep dependency parser support morphological and dependency analysis for agglutinative and code-switched languages. Her recent work focuses on historical Turkish NLP, Ottoman Turkish corpus construction, and multilingual BERT-based dependency parsing. She has contributed to code-switching NLP, semi-supervised learning techniques, and sentence similarity kernels for summarization. Her publications span 2016–2025, emphasizing treebanking, parsing optimization, and low-resource language challenges. Key projects include the NakbaTR dataset for Turkish NER, Arabic calligraphy text extraction, and dementia caregiver detection via social media analysis. Her work bridges theoretical linguistics with practical tools, making Turkish NLP resources accessible for global research.

Lynda Hardmanمشاهده پروفایل

استاد

• Multimedia Discourse Interaction
• Augmented Reality
• User-Centric Design
• +۳ مورد دیگر

Roles: Lynda Hardman is a part-time Professor of Multimedia Discourse Interaction at the University of Utrecht's Interaction Technology group within the Institute of Information and Computing Sciences. She concurrently serves as a Research & Strategy Manager at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, leading the Human-Centered Data Analytics research group. She directs Amsterdam Data Science (ADS), fostering collaboration between academia, industry, and government in data science. Research: Hardman's work focuses on user-centric technologies, including augmented reality applications for neuroscience (e.g., Linked Brain Data), semantic web integration (LinkedTV, Fish4Knowledge), and interactive media systems. Her research bridges multimedia, AI, and human-computer interaction, emphasizing ethical digital humanism through initiatives like the Vienna Manifesto. Past projects include EventMedia, K-Space NoE, and MultimediaN/E-Culture, advancing automated media linking and user interface design. Leadership: As former president of Informatics Europe (2016-2019), she championed women's roles in informatics through the 'More Women in Informatics Research & Education' initiative. She also led EIT Digital's ICT-mediated Human Activity action line (2011-2012), promoting European ICT innovation. Publications: Her recent work explores AR tools for neuroscience literature analysis (DatAR), digital humanism frameworks, and bias in digital archives. These contributions highlight interdisciplinary approaches to technology's societal impact.

• Machine Learning
• Medical Image Analysis
• Multimodal Learning
• +۳ مورد دیگر

Mohammod Naimul Islam Suvon serves as a Research Assistant in Machine Learning for Medical Image Analysis at the University of Sheffield's School of Computer Science within the Department of Computer Science. He is an active member of the Machine Learning research group focusing on healthcare applications. His research spans Medical Image Analysis , Cardiac Hemodynamics , and Multimodal Learning with emphasis on cost-effective diagnostic solutions. Key projects include pulmonary hypertension detection using cardiac MRI and ECG analysis, leveraging tensor-based multimodal fusion and variational autoencoders. His work bridges clinical cardiology needs with advanced AI techniques. Analysis of his 15 most recent publications reveals strong specialization in medical multimodal AI (80% of works), particularly for cardiovascular diagnostics. Secondary interests include pandemic response systems (CT-scan analysis for COVID-19), agricultural AI, and educational technology applications. His methodology consistently employs deep learning architectures adapted for specific clinical constraints. Suvon maintains active collaborations with clinical researchers at Sheffield, evidenced by co-authorship with medical professionals like Swift AJ and Alabed S. His publication pattern shows increasing focus on low-cost diagnostic solutions since 2022, suggesting strategic alignment with healthcare accessibility goals.

Tim Andersenمشاهده پروفایل

استاد

• Computer Science
• Bioinformatics
• Data Storage
• +۷ مورد دیگر

Tim Andersen is a Professor in the Computer Science Department at Boise State University. He holds a Ph.D. in Computer Science from Brigham Young University (1999) and previously worked as Chief Scientist at IArchives, developing OCR and image processing algorithms. His research focuses on interdisciplinary areas including nucleic acid-based data storage, blockchain applications in AI, machine learning optimization, and bio-inspired computational models. Education : Ph.D. in Computer Science, Brigham Young University, 1999 Research Interests : Dr. Andersen’s work spans molecular data storage (e.g., DNA-based memory systems), computational security protocols leveraging blockchain, optimization of neural network training, and bioinformatics applications such as probe design and protein docking. He pioneered methods for encoding digital information in nucleic acids and developed algorithms for enhancing AI model trustworthiness through decentralized verification. Publications Trends : Recent work emphasizes nucleic acid memory optimization (2023–2025), blockchain integration in AI security (2023–2024), and legacy contributions to OCR algorithms and evolutionary computation (1995–2000s). His research often bridges computer science with molecular biology, addressing both theoretical and applied challenges in emerging technologies. Awards & Grants : Not explicitly listed in available texts, but his sustained contributions across multiple disciplines suggest substantial external funding and recognition in interdisciplinary computing. Advising & Curriculum Development : Co-developed the “Computer Science Professionals’ Hatchery” program, integrating inclusion/diversity initiatives into Boise State’s curriculum. Focus on preparing high-school teachers in computer science through sustainable models like IDoCode. Labs & Collaborations : Associated with Boise State’s computational biology and AI security research groups. Collaborates across disciplines to advance nucleic acid storage and blockchain-audited systems.

• Comparative Literature
• Digital Humanities
• Literary Criticism
• +۵ مورد دیگر

Simone Rebora is an Associate Professor in the Department of Foreign Languages and Literatures at the University of Verona, specializing in Comparative Literature with a strong focus on Digital Humanities. His academic work bridges traditional literary scholarship with computational approaches, making significant contributions to how we understand literature in the digital age. Professor Rebora's research interests span Comparative Literature , Digital Humanities , Empirical Study of Literature , and Computational Methods for Literary Analysis . He investigates how digital technologies transform reading practices, literary analysis, and the preservation of textual heritage, with particular attention to the relationship between literature and prosocial behavior. His recent scholarly output demonstrates a consistent focus on digital approaches to literary studies, with publications covering computational methods, digital editions of literary works (particularly Robert Musil's writings), OCR technology for historical documents, and the impact of digital media on reading comprehension. These works reflect his interdisciplinary approach that combines literary theory with empirical research methods. Member of EREAD research group studying differences between reading on paper and on screen Participant in ELIT (Empirical Study of Literature Innovative Training Network), a Horizon 2020 Project Member of Internationale Robert Musil Gesellschaft (IRMG) Involved with DAIH (Digital Arena for Inclusive Humanities), CRIER, and SKENÈ research centers Professor Rebora actively supervises students across multiple degree programs and serves in significant institutional roles including Department Council membership, Teaching Committee participation, and as Departmental Teaching Representative. His commitment to both research excellence and educational innovation makes him a central figure in advancing Digital Humanities at the University of Verona.

• Temporal Data Mining
• Biomedical Decision Support Systems
• Temporal Logics
• +۵ مورد دیگر

Pietro Sala is an Associate Professor at the University of Verona , Department of Computer Science. His research intersects Temporal Data Mining , Biomedical Decision Support Systems , and Formal Verification of temporal logic specifications. Key Research Areas: Interval Temporal Logic and Model Checking Temporal BPMN for healthcare processes Data fusion in clinical environments Algorithm design for repetitive task automation Notable Projects: Dioxin impact analysis on cardiovascular development Open-source clinical data warehousing (DW-SAN) Real-time system verification frameworks Teaching: Lecturer for Biomedical Decision Support Systems (Master's) Instructor for Data Mining and Software Engineering courses The articles highlight his work in temporal logic verification (2025), clinical process modeling (2024-2025), and data integration for NLP pipelines (2023). His publications span theoretical computer science conferences (LICS, ICALP) and healthcare informatics journals (ICHI, CBM).

Minhaj Alamمشاهده پروفایل

استادیار

• AI in healthcare
• Medical Imaging
• Computer-aided diagnostics
• +۵ مورد دیگر

Dr. Minhaj Alam is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of North Carolina at Charlotte. His research focuses on AI-driven medical imaging, particularly in ophthalmology, with expertise in federated learning, computer-aided diagnostics, and imaging biomarkers. He holds a Ph.D. from the University of Illinois at Chicago, a Postdoctoral Fellowship at Stanford University, and prior degrees from the Islamic University of Technology in Bangladesh. Education: Ph.D., University of Illinois at Chicago, 2020 Postdoctoral Fellowship, Stanford University, 2022 M.Sc. and B.Sc., Islamic University of Technology, 2015 and 2013 His research interests include developing AI models for retinal disease diagnosis (e.g., diabetic retinopathy, age-related macular degeneration) using optical coherence tomography angiography (OCTA), federated learning for multi-institutional data, and automated risk stratification systems. Notable contributions include the AV-Net framework for artery-vein classification and the EYE-Llama large language model for ophthalmology. Dr. Alam’s publications span over 30 peer-reviewed articles in journals like Frontiers in Medicine , Scientific Reports , and Radiology AI . His work emphasizes quantitative biomarker analysis and translational AI for clinical impact. He collaborates with institutions like Stanford University and investigates cutting-edge techniques like self-supervised learning and domain adaptation. No scientific awards or grants are explicitly listed in the provided material. His lab focuses on advancing AI tools for precision medicine and non-invasive diagnostics, with a vision to bridge gaps in healthcare accessibility through innovative imaging technologies.

Myeong Leeمشاهده پروفایل

استادیار

• Community Informatics
• Civic Technology
• Information Inequality
• +۳ مورد دیگر

Myeong Lee is an Assistant Professor of Information Science and Director of the Community Informatics Lab at George Mason University (GMU), School of Computing. His research focuses on local communities, technology-enabled groups, and information inequality, with a goal to enhance civic engagement and public policy through mixed-method approaches. He holds a PhD in Information Studies from the University of Maryland, College Park, and master’s degrees in Information Management and Software Engineering, alongside a BS in Electrical Engineering from Seoul National University. Affiliations: Institute for Digital Innovation (IDIA), Center for Advancing Human-Machine Partnership (CAHMP), Urban Research Hub, and Humanities-Centered Design (HCD) group at GMU. Awards: NSF #1816763 grant, Best Paper Awards, and fellowships from institutions like the Center for Open Data Enterprise. Key Projects: Developing the Social Safety in Virginia (SAFETI) system, mapping disability service information ecology in Virginia, and advancing AI-in-Gov initiatives. His research integrates computational methods with socio-technical theories, addressing issues like news deserts, data bias in civic technologies, and information accessibility in marginalized communities. Lee collaborates with municipalities, NGOs, and academic partners to bridge theory and practice, recently leading the Open Data Impact Map project and advising the SDG National Reporting Initiative. Recent Events: Invited talks at international conferences (CSCW 2024, ASIST 2025), AI policy forums, and academic collaborations in Korea. He serves on organizing committees for major conferences like CHI and CSCW. Grants: NSF IIS #1816763, Virginia Board for People with Disabilities (VBPD), and DOLI Innovation Lab funding. His work emphasizes participatory design and boundary-spanning approaches to solve complex socio-technical challenges.

Papaioannou Georgiosمشاهده پروفایل

استادیار

• Real-time Computer Graphics
• Virtual Reality
• Photorealistic Imaging
• +۳ مورد دیگر

Georgios Papaioannou Georgios Papaioannou is an Assistant Professor at the Department of Informatics, Athens University of Economics and Business (AUEB). He holds a BSc and PhD in Informatics/Computer Science from the National and Kapodistrian University of Athens. His research focuses on real-time computer graphics, virtual reality, photorealistic rendering, and 3D pattern recognition with applications in cultural heritage digitization and interactive environments. Education BSc in Informatics, National and Kapodistrian University of Athens PhD in Computer Science, National and Kapodistrian University of Athens Research Interests His work spans real-time rendering techniques, virtual reality systems, 3D object reconstruction, and cultural heritage preservation. Notable projects include the PRESIOUS EU project for predictive digitization of cultural objects and the Virtual Archaeologist system for fractured artifact reassembly. Key Contributions Dr. Papaioannou has authored influential papers on global illumination algorithms, volume-based ambient occlusion, and real-time graphics optimization. His work integrates computer vision and machine learning to solve challenges in interactive visualization and archaeological reconstruction. Professional Activities A member of Eurographics, ACM, and SIGGRAPH, he actively reviews for top computer graphics conferences. His lab focuses on advancing real-time rendering technologies for immersive applications, including VR systems for cultural heritage education and historical reenactment.

• Internet of Things (IoT)
• Environmental Monitoring Systems
• Autonomous Vehicles
• +۶ مورد دیگر

Professor Tomasz Zientarski is a faculty member at Lublin University of Technology's Faculty of Electrical Engineering and Computer Science, Department of Computer Science. His research spans interdisciplinary areas including IoT applications, environmental monitoring systems, autonomous vehicle design, molecular dynamics simulations, and computer science education challenges. He actively contributes to scientific expeditions such as the 10th Central Asia expedition using 3D digital technology and participates in events like LubGame Conference 2025. His academic projects include the Integrated Development Programme of the university, Universal Design initiatives, and the T1D Coach project. He advises students on thesis topics involving IoT systems, environmental data visualization, and autonomous vehicle monitoring. He has been honored with the Medal of Merit for the city of Lublin as part of the Department's recognition. His work integrates computational physics (e.g., Lennard-Jones potential modeling) with practical applications like diabetes patient simulation and noise-resistant audio detection. Tools he uses include Moodle, Microsoft Azure, and Visual Paradigm. Research trends in his publications show dual focus: applied CS innovations (IoT, OCR, medical simulations) and fundamental material science studies (thin film stress, molecular dynamics). His 2024 articles highlight advancements in deepfake detection and NLP, while earlier work addresses barriers in educating international students and optimizing wireless communication models. His lab activities include maintaining a traffic acquisition station and preserving cultural heritage through 3D technology.

Éva Hajnalمشاهده پروفایل

دانشیار

• Environmental Science
• Computer Science
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Éva Hajnal is an Associate Professor and Vice Dean for Research at the Alba Regia Technical Faculty of Óbuda University. Her work focuses on interdisciplinary research spanning environmental monitoring technologies, artificial intelligence applications in agriculture, and sensor development. She leads efforts in livestock health monitoring using rumen bolus sensors and has contributed to ecological assessment methodologies using diatoms. Her email is hajnal.eva@amk.uni-obuda.hu , and she is located at Room 25 in Building K on Budai út 45. Her research integrates computer science with ecological challenges, addressing issues like groundwater quality and wind energy modeling. Research interests include: Biosensor development for livestock health AI-driven environmental data analysis Ecological status assessment using diatoms Virtual reality and motion tracking systems Online mapserver technologies Recent work emphasizes long-term rumen temperature analysis, low-computational algorithms for cattle heart rate estimation, and multilingual handwritten character datasets. Her research trends show strong focus on applying computational methods to ecological and agricultural challenges. She has contributed to projects like wastewater management control systems and biodiversity indices for freshwater ecosystems. No specific scientific awards are listed. Her administrative role as Vice Dean for Research involves overseeing research strategy and collaboration at the faculty level. No advising or grant details are provided in available texts. Her work is associated with the university's technical faculty and environmental engineering teams, contributing to both academic and applied research initiatives.

Ioan Clejuمشاهده پروفایل

مدرس

• Coding Theory
• Digital Systems Design
• Computer-Aided Error-Correcting Code Design
• +۷ مورد دیگر

Ioan Cleju is a Lecturer at the Technical University of Iasi , affiliated with the Faculty of Electronics and Telecommunications and the Department of Logic Circuits . His research spans coding theory, digital systems design, and error-correcting codes with computer-aided methods, while later works focus on AI-driven signal processing for ECG biometrics and smart traffic systems. Email: icleju@etc.tuiasi.ro Research Interests include: Computer-Aided Design of Error-Correcting Codes Multi-Distance and Cyclic Code Design PLD Circuit Applications in Coding Theory AI-Driven Sparse Signal Recovery ECG Biometric Authentication Smart Urban Mobility Systems Recent Publications highlight trends in: Machine learning for cybersecurity and traffic management ECG signal analysis using convolutional networks Dictionary learning for anomaly detection Historical document digitization with symbolic AI Multi-lane vehicle flocking algorithms

Ralf Möllerمشاهده پروفایل

استاد

• Artificial Intelligence
• Humanities-Centered AI
• Causal and Probabilistic Models
• +۵ مورد دیگر

Prof. Ralf Möller is a Professor of Artificial Intelligence in Humanities at the University of Hamburg's Faculty of Humanities, Department of Philosophy. He leads the Institute of Humanities-Centered Artificial Intelligence (CHAI) and serves as spokesperson for the 'Data Linking' research field within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA, 2019–2025). His research focuses on intellectics, causal and probabilistic-relational models, and AI applications in humanities, emphasizing sustainable data management and multimodal foundation models. He heads the Data Linking Lab and the CHAI Institute, overseeing projects like the TAMAR initiative for manuscript research. His work bridges technical AI advancements with humanities needs, addressing challenges in data curation, federated information systems, and ethical AI integration. He contributes to interdisciplinary collaborations, including ethics committees and cultural heritage preservation initiatives. Key research themes include lifted inference in probabilistic graphical models, temporal data prediction, and synergistic OCR-LLM systems for damaged documents. His projects emphasize scalability, sustainability, and human-centered design principles. Supervising doctoral candidates in AI and humanities intersections, he advocates for AI systems grounded in cultural and ethical considerations. Labs/Teams: CHAI Institute, Data Linking Lab. Current Projects: UWA Cluster (2019–2025), Data Linking Infrastructure development, Humanities-Centered AI applications. Grants include leadership roles in institutional and collaborative research funding.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +۴ مورد دیگر

Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework

David Mercierمشاهده پروفایل

استاد

• Information Fusion
• Belief Functions
• Uncertainty Management
• +۳ مورد دیگر

David Mercier is a Professor at the University of Artois, France, affiliated with the LGI2A laboratory and the IUT de Béthune's Department of Networks and Telecommunications. He holds a HDR (Habilitation à Diriger des Recherches) from 2015 and a PhD from the University of Technology of Compiègne (UTC), France (2006). His research focuses on information fusion, belief functions, and uncertainty management, with applications in electrical engineering, transportation systems, and machine learning. Education DEA (M.S. Equivalent) in Artificial Intelligence, University Paul Sabatier, Toulouse (2003) PhD in Information Fusion for Postal Address Recognition, UTC (2006) HDR (Research Habilitation), University of Artois (2015) Research & Awards Notable contributions include work on contextual belief function corrections, vehicular network information fusion, and multi-target tracking. He received a co-best student paper award at BELIEF 2016. His research spans electrical machines diagnosis, stochastic simulations, and deep learning integration with Dempster-Shafer theory. Students & Involvement Supervised PhD theses include studies on evidential deep learning and motor fault diagnosis. Active in the Belief Functions and Applications Society (board member since 2010). Co-edited special issues in International Journal of Approximate Reasoning and authored book chapters on belief function applications. Labs & Teams Primary affiliation with LGI2A laboratory, collaborating with institutions like LSEE and industrial partners such as Solystic and SNCF. Research integrates theoretical advances with practical applications in smart mobility and fault detection.