جست‌وجوی استاد و پژوهشگر

Stefan Thiemannمشاهده پروفایل

پژوهشگر

• Research data management
• Digital humanities
• Open science
• +۴ مورد دیگر

Dr. Stefan Thiemann is a faculty-level researcher at the University of Hamburg, serving since 2017 as head of the Center for Sustainable Research Data Management and since 2016 as the university’s Open-Access officer. He represents Hamburg in the National Research-Data Infrastructure (NFDI) and is a principal investigator in the Cluster of Excellence "Understanding Written Artefacts" (Research Field F: Data Linking). Education: Diplom in Meteorology & Computer Science, University of Hamburg (1990) Dr. rer. nat. in Geosciences, University of Hamburg (1996, dissertation on Arctic boundary-layer convection) Research interests: Thiemann’s work sits at the intersection of digital infrastructure and the humanities. He designs sustainable research-data repositories, federated information systems and on-demand transformer models that allow scholars to re-use humanities data at scale. Current themes include FAIR data implementation, AI-assisted transcription & annotation of manuscripts, and open-science policy development. His recent articles (2023-2025) cluster around two trajectories: building reusable, AI-ready data infrastructures (federated repositories, sustainable viewers, chatbots fine-tuned for humanities queries) and fostering humanities-centred AI workflows (automatic linking of manuscript metadata, large-scale text mining, and user-friendly annotation tools such as the TAMAR platform). Service & awards: Member, University of Hamburg Research Infrastructure Council (2020-) Member, Steering Group on Digitalisation in Research (2020-) Open-Access Officer, University of Hamburg (2016-) Former Faculty Information Officer, Faculty of Humanities (2012-2017) Teaching & grants: He regularly teaches Bachelor seminars on digital research-data management and Internet competence. He leads or co-leads major third-party projects including the DFG-funded SFB 950 “Manuscript Cultures”, the Hamburg Open Science initiative, and infrastructure grants that established the university’s research-data repository and DOI services. Labs & teams: Thiemann heads the Center for Sustainable Research Data Management and is a core member of the Data Linking Research Unit within the UWA Cluster of Excellence. He coordinates interdisciplinary teams developing the UHH research-data repository, FUNDus! collection portal, and manuscript-culture databases hosted at the CSMC.

• Evolutionary Computation
• Machine Learning
• Neurodegenerative Disease Prediction
• +۴ مورد دیگر

Dr. Francesco Fontanella is an Associate Professor at the Department of Electric and Information Engineering (DIEI) at the University of Cassino and Southern Lazio. His research focuses on evolutionary computation, machine learning, and their applications in neurodegenerative disease detection (e.g., Alzheimer’s and Parkinson’s), cultural heritage preservation, and environmental monitoring. He leads the AIDA Lab and has contributed to major initiatives like the HAND project for neuromuscular disease diagnosis and the C4E environmental monitoring system. Fontanella holds editorial roles in journals such as Pattern Recognition Letters , Genetic Programming and Evolvable Machines , and Applied Soft Computing . He has organized international workshops including the XVI International Workshop on Artificial Life and Evolutionary Computation (2022) and chairs the IEEE Computational Intelligence Society’s Task Force on Evolutionary Computer Vision. His teaching responsibilities include courses on computer science fundamentals, operating systems, and cybersecurity at both undergraduate and graduate levels. Notable projects include developing handwriting analysis systems for medical diagnostics and AI-driven tools for medieval manuscript analysis. Fontanella’s work bridges computational techniques with real-world challenges, emphasizing interdisciplinary collaboration between computer science and fields like healthcare, archaeology, and environmental science.

• Medical Image Analysis
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Dr. Efosa Osagie is a Lecturer in the Computer Science and Data Science department at York St John University's London Campus. He holds a PhD in Data Science from the University of Hertfordshire, an MSc in Information Technology from the University of Bolton, and a BEng in Materials and Production Engineering from Ambrose Alli University, Nigeria. His research focuses on applying machine learning and computer vision to medical image analysis, including biomedical image segmentation, Siamese networks, and generative modeling. He teaches master's-level modules in data science, machine learning, and research methods, emphasizing problem-based learning. Dr. Osagie's professional affiliations include membership in the British Computer Society (BCS) and the Cyber Security Experts Association of Nigeria (CSEAN), as well as Fellowship of the Higher Education Academy (FHEA). His work bridges academia and industry, addressing challenges in medical imaging through innovative computational methods.

Tobias Straußمشاهده پروفایل

مدرس

• Mathematics Education
• Historical Document Analysis
• Machine Learning
• +۵ مورد دیگر

Dr. Tobias Strauß is a Lecturer at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, University of Rostock. He teaches courses such as Elementary Algebra and Number Theory and Analytical Geometry, focusing on foundational mathematical concepts and their applications. His research interests span historical document analysis, machine learning, and neural networks, with a particular emphasis on handwritten text recognition (HTR) and computational solutions for cultural heritage digitization. He actively contributes to the Mathematics Society RHO eV, supporting students in mathematics competitions and enrichment programs. Dr. Strauß’s research combines computer vision and machine learning to address challenges in document analysis, including text line detection, cursive script recognition, and keyword search in historical manuscripts. His work also explores semi-supervised learning techniques and neural network architectures tailored for HTR tasks. Through RHO eV, he promotes mathematics education through weekend seminars, district clubs, and game-based learning activities, fostering student engagement and problem-solving skills. His publications highlight advancements in HTR systems, such as the CITlab Recognition & Retrieval Engine, and address topics like regular expression-based decoding and Arabic handwriting recognition. These contributions underscore his expertise in bridging theoretical mathematics with practical applications in digital humanities and education. Dr. Strauß can be contacted via tobias.strauss@uni-rostock.de or through the university’s chat platform. No formal awards or grants are noted in the provided materials, though his involvement in international competitions (e.g., ICFHR, ICDAR) reflects his scholarly engagement.

Andreas Reichمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Augmented Reality
• +۳ مورد دیگر

Andreas Reich is a Researcher at the University of Hohenheim's Department of Communication Science, focusing on media research and media use. He works on the 'DeLLFi' project and holds a Master's degree from Furtwangen University. His research interests include artificial intelligence, machine learning, augmented reality, and network analysis applied to media and communication contexts. He has received notable awards for his master's thesis on automated programming using node trees and an AR smart mirror project in interaction design. Key research contributions include developing benchmark datasets for video-slide alignment, studying AI-driven conversational agents' ethical frameworks, analyzing protest movements' digital organization on Telegram, and exploring onboarding strategies for educational chatbots. His technical work spans program synthesis via reinforcement learning and neural networks, as well as 3D object generation using node-based systems. Awards: Media Prize (2021) - Furtwangen University Master's Prize (2020) - Furtwangen University Expertise Areas: Program synthesis, augmented reality applications, network analysis, educational chatbots, multimodal data alignment, and generative AI. Consultation hours are available by appointment via email.

Edwin Carlinetمشاهده پروفایل

استادیار

• Mathematical Morphology
• Image Processing
• High Performance Computing
• +۱ مورد دیگر

Edwin Carlinet is an Assistant Professor at EPITA Research Laboratory (LRE), specializing in mathematical morphology, hierarchical image representations, and high-performance computing. His research develops efficient algorithms for image processing with applications in document analysis and historical map vectorization. Key projects include the SoDuCo initiative extracting data from 19th-century Parisian trade directories and parallel implementations of morphology algorithms. He leads development of the Pylene image processing library, focusing on generic and efficient implementations. Teaching covers parallel programming, data compression, C++, and GPU programming at EPITA, alongside bioinformatics courses at Sup'Biotech. Research supervision includes PhD candidates working on modern implementations of mathematical morphology algorithms and noise estimation techniques. Recent publications focus on optimizing computational workflows for historical document processing, including noise impact analysis on morphological structures and GPU acceleration of max-tree computation.

• Computer Vision
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Hui Guo is an Adjunct Assistant Professor in the Department of Civil Engineering. Their research intersects computer vision, machine learning, and image processing, with a focus on advanced restoration and super-resolution techniques. Recent publications highlight contributions to image super-resolution challenges (NTIRE 2024/2025), vision transformers (QID), diffusion models for video restoration, and continual learning frameworks (PCL). Work spans OCR-free visual document analysis, adversarial degradation modeling (AND), and parametric image restoration. Contact: guoh15

• Artificial Intelligence
• Data Science
• Computer Science
• +۵ مورد دیگر

Professor Norbert Fischer is affiliated with the University of Würzburg's Faculty of Mathematics and Computer Science, where he works at the Institute of Computer Science as part of the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems. He also contributes to the Center for Artificial Intelligence and Data Science (CAIDAS). Research Focus: Artificial Intelligence, Optical Character Recognition (OCR), and Knowledge Systems Projects: Involved in initiatives like LeLaR (Learning Attitude Control), BuDaNu (Business Data Utilization), KI-Nergy (Intelligent Heating Systems), and others focusing on AI applications in document analysis, medical imaging, and legal consulting. Recent Publications: His work includes AI-driven segmentation of historical prints (2020) and algorithmic visualization techniques (2017). Contact: norbert.fischer@uni-wuerzburg.de | Room 03.011 | Phone: +49 931 / 31-87622

• Computer Science Education
• Data Systems
• Collaborative Learning
• +۳ مورد دیگر

Abdussalam Alhadi M Alawini serves as a Teaching Associate Professor in the Siebel School of Computing and Data Science at the University of Illinois' Grainger College of Engineering. He holds additional roles as an Education Innovation Fellow and Keen Catalyst within the same institution. His work focuses on improving computer science education through innovative pedagogical strategies, particularly in data systems and collaborative learning environments. Key research areas include database education methodologies, student sense of belonging in STEM fields, and the application of data-driven approaches to analyze collaborative learning processes. He has been recognized with the 2024 Campus Excellence in Instruction Award for his contributions to teaching excellence. His recent publications emphasize curriculum design for data systems education, automated feedback systems for database courses, and the psychological impacts of collaborative learning on computing students. Alawini actively contributes to educational initiatives bridging industry needs with academic curricula, and has pioneered visualization tools like GA2Graph to enhance educational analytics.

Adrian ALEXANDRESCUمشاهده پروفایل

دانشیار

• Internet technologies
• Web services
• Distributed systems
• +۵ مورد دیگر

Adrian ALEXANDRESCU is an Associate Professor at the Department of Computer Science and Engineering within the Faculty of Automatic Control and Computer Engineering at “Gheorghe Asachi” Technical University of Iași. He is a member of the Open Infrastructure Research Center and specializes in interdisciplinary research areas such as blockchain technology, distributed systems, artificial intelligence (genetic algorithms, neural networks), and IoT applications. His work bridges theoretical computer science with practical implementations in education, healthcare, and smart technologies. His research interests focus on leveraging blockchain for secure transactions, optimizing distributed systems, and enhancing e-learning through gamification. He has contributed to projects involving sensor networks for health monitoring, real-time driver sobriety tracking, and decentralized identity management systems. His academic contributions span over 20 years, with notable work on genetic algorithms for task mapping in heterogeneous systems and cloud-based solutions for ambient assisted living. Dr. ALEXANDRESCU’s publications emphasize blockchain’s role in trustless systems, IoT-driven healthcare environments, and AI-driven solutions for education and logistics. His work on decentralized article retrieval systems and plagiarism detection frameworks underscores his commitment to ethical and efficient digital ecosystems. Despite no explicitly listed awards, his prolific publication record reflects sustained academic excellence. He advises on projects at the intersection of cloud computing, distributed architectures, and smart technologies. His labs and collaborations focus on developing scalable solutions for real-world challenges such as secure real estate transactions and community-driven academic publishing systems.

Ahmed Sabirمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Ahmed Sabir is a postdoctoral researcher at the Institute of Computer Science , University of Tartu , focusing on AI Ethics (biases, fairness, and explainability in Large Language Models). He earned his Ph.D. in Computer Science from Universitat Politècnica de Catalunya (BarcelonaTech) in 2020 and an MSc from Kanagawa Institute of Technology under the Masuda AI lab. Current Research : Biases in LLMs, Explainable AI, Multimodal Fact-Checking Past Work : Gesture recognition, OCR correction via visual semantics, Image captioning Research Interests span Natural Language Processing , Computer Vision , and their intersection in Vision-Language Models and AI Ethics . His work emphasizes Context-aware multimodal learning Bias measurement and mitigation Efficient post-processing techniques Scientific Contributions include 15+ peer-reviewed publications at venues like ACL, EMNLP, CVPRW, and COLING, with datasets and re-ranking frameworks for vision-language tasks. Notable achievements: Driven advancements in semantic relatedness for OCR correction Developed visual grounding techniques for captioning Created benchmark datasets for text spotting Explored gender bias in genderless languages Technical Expertise includes Measurement electronics for industrial tools LaTeX thesis template development Participated in 48-hour Mobility Hackathon (2017)

• Image Analysis
• Pattern Recognition
• Document Analysis
• +۳ مورد دیگر

Dr Christian Clausner is a Research Fellow at the University of Salford, affiliated with the School of Science, Engineering & Environment and the Informatics research center. He holds a part-time PhD in Computer Science, completed in parallel with his academic role. His research focuses on image analysis, pattern recognition, and document analysis, with a particular emphasis on OCR technology, historical document digitization, and semantic metadata frameworks. Education: Studied Computer Science and Artificial Intelligence in Germany. Professional Experience: Joined academia in 2009 after private sector work, specializing in technical workflows for large-scale document digitization projects. Research Highlights: Developed NAME XML for entity-relation tagging, contributed to OCR evaluation tools, and led initiatives like the 1961 UK Census digitization and crowdsourcing for tabular data extraction. He co-chaired HIP workshops in 2021 and 2023, advancing historical document processing standards. Key Projects: WaterAGRI (2020–2024), Europeana Newspapers OCR workflow, and the Medical Officer of Health Reports scoping study. His work bridges computer science and cultural heritage, addressing challenges in text recognition, layout analysis, and semantic labeling.

Ruwan Tennakoonمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Dr. Ruwan Tennakoon is a Senior Lecturer in Artificial Intelligence at RMIT University's School of Computing Technologies in Melbourne, Australia. He holds a BSc (Hons) in Electrical and Electronics Engineering from the University of Peradeniya and a PhD in Computer Vision from Swinburne University of Technology (2015). Prior to his current role, he held post-doctoral positions at RMIT School of Engineering and IBM Research. His research focuses on robust computer vision, particularly in medical imaging and industrial automation. Key areas include distribution shift challenges, large vision foundation models, and robust algorithms for low-level vision tasks. His work spans applications like medical image analysis, autonomous systems, and defect detection in manufacturing. Ruwan has supervised numerous research projects, including those on threat detection, medical imaging AI, and industrial automation. He teaches courses in computer vision and machine learning, emphasizing practical applications. His research has been published in top-tier venues such as ACM Transactions on Intelligent Systems and Technology and Medical Image Analysis .

Mitch Marcusمشاهده پروفایل

استاد

• Statistical natural language processing
• Cognitively plausible models
• Linguistic structure acquisition

Mitch Marcus is a Professor Emeritus in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He earned his Ph.D. from MIT in 1978 and joined Penn in 1987 after working at AT&T Bell Laboratories. His research focuses on statistical natural language processing and cognitively plausible models for automatic acquisition of linguistic structure. Dr. Marcus created and led the Penn Treebank Project, which revolutionized natural language parsing accuracy. He has served as PI on multiple major projects including DARPA LORELEI and GAILA AIX, focusing on unsupervised morphology acquisition and child language acquisition simulations. Awards: Fellow of American Association for Artificial Intelligence (1992) Founding Fellow of Association for Computational Linguistics (2011) Advising: His former PhD students hold positions at institutions including MIT, Johns Hopkins, Google, and Microsoft Research. Research groups: Directed the Penn Treebank Project and co-led the OntoNotes Project. Currently collaborates on natural language understanding for human-robot interaction.

Aristidis Likasمشاهده پروفایل

استاد

• Neural Networks
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Aristidis Likas is a Professor at the Department of Computer Science and Engineering, University of Ioannina, Greece. His research spans neural networks, machine learning, data mining, and intelligent systems with applications in medicine and biology. He has contributed to image processing, video analysis, and scientific computing, with a focus on algorithm innovation and biomedical applications. His work has been published in journals such as IEEE Transactions on Neural Networks , Pattern Recognition , and Bioinformatics . He has also served as Associate Editor for IEEE Transactions on Neural Networks (2008-2012) and co-chaired conferences including ECML/PKDD 2011 and SETN 2014. Key projects include VIDEOSUM: Video Summarization PRotEin S-Sulfenylation Server (PRESS) Global k-means algorithm Dip-means clustering