جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Robotics
• Machine Learning
• +۶ مورد دیگر

Dr Fateme Dinmohammadi is an Associate Professor and Senior Lecturer in AI & Robotics at the University of West London (UWL) , serving as Course Director for the MSc Software Engineering program within the School of Computing and Engineering . Her research focuses on AI, Machine Learning, Robotics, Predictive Analytics, and IoT-driven systems, with applications in energy efficiency, healthcare, and infrastructure monitoring. She has contributed to over 50 peer-reviewed articles and industry-funded projects through EPSRC, InnovateUK, H2020, and industry collaborations. Previously, she held senior research roles at University College London (UCL) , Cranfield University , and the Edinburgh Robotics Centre . Her work spans AI-driven solutions for smart buildings, battery management in EVs, structural health monitoring of wind turbines, and medical imaging applications like echocardiography. Projects emphasize real-world deployment through digital twins, Bayesian networks, and edge computing. Dr Dinmohammadi teaches across undergraduate and postgraduate programs, including BSc Computer Science, Cyber Security, and AI-focused degrees. Her expertise bridges academic research and industry needs, addressing challenges in Industry 4.0, sustainable energy systems, and autonomous robotics.

• Machine Learning
• Pattern Recognition
• Image Analysis
• +۳ مورد دیگر

Aniket Anand Gurav is a Research Fellow at the Faculty of Information Technology, Engineering and Economics at Østfold University College, Norway. He is based in Halden and can be reached at aniket.a.gurav@hiof.no. His academic work is closely tied to the Machine Learning research group. Research Interests Gurav's research focuses on Machine Learning , particularly in Pattern Recognition and Image Analysis , with applications in computer vision and artificial intelligence. His work addresses challenges in Handwritten Word Image Recognition , leveraging neural networks for Norwegian language processing. Recent Publication In 2023, Gurav co-authored a paper introducing ResPho(SC)Net , a zero-shot learning framework designed for recognizing Norwegian handwritten text. This contribution aligns with advancements in Optical Character Recognition and Document Image Analysis . Labs and Teams He is affiliated with the Machine Learning research group, which drives interdisciplinary projects at the intersection of computer science and engineering.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۲ مورد دیگر

Lazaros T. Tsochatzidis is a researcher with significant contributions to computer vision , machine learning , and their applications in medical imaging and autonomous driving . He has published extensively in top-tier venues like Pattern Recognition , IEEE Access , and Robotics and Autonomous Systems , often collaborating with researchers such as Ioannis Pratikakis and Konstantinos Zagoris . Research Interests Specializes in LiDAR-based 3D object detection for autonomous vehicles Developed domain adaptive object detection techniques using mean teacher models Innovated part-aware graph fusion for skeleton-based action recognition Expert in medical image analysis for breast cancer diagnosis Contributed to historical document transcription and sclera segmentation research Scientific Awards No explicit awards mentioned in available data Notable Collaborations Long-term collaboration with Konstantinos Zagoris (6 co-authored papers) Extensive work with Ioannis Pratikakis (11 co-authored publications) Multidisciplinary collaborations with experts in medical imaging , autonomous systems , and biometric security

• Cultural Heritage
• Document Analysis
• Medical Data Analysis
• +۵ مورد دیگر

Florian Kleber is a senior scientist at the Computer Vision Lab within the Institute of Visual Computing and Human-Centered Technology at Vienna University of Technology (TU Wien), Austria. His research spans two primary domains: document analysis for cultural heritage preservation and medical data analysis. He has been actively involved in lecturing at TU Wien, particularly in Document Analysis courses, and has served as a substitute member of the Curriculum Commission for Informatics. Dr. Kleber's research focuses on the intersection of computer vision and practical applications. His work in cultural heritage includes multi-spectral acquisition and restoration of ancient manuscripts, development of tools for historical document analysis, and work on writer identification and retrieval systems. In medical data analysis, he has specialized in flow cytometry analysis, particularly for minimal residual disease assessment in acute lymphoblastic leukemia. His research demonstrates a consistent pattern of applying advanced computer vision techniques to solve domain-specific challenges in both humanities and medical fields. His recent publications reveal a strong trend toward synthetic data generation for training document analysis systems, self-supervised learning approaches for writer retrieval, and explainable AI techniques for medical data analysis. The publications show increasing sophistication in transformer-based architectures applied to both document analysis and medical imaging domains. His work bridges theoretical computer vision advances with practical applications in cultural heritage institutions and medical settings. Dr. Kleber has supervised multiple diploma theses at TU Wien, including work on automated analysis of herbarium collections, transparency techniques for neural networks, synthetic data for document analysis, and physical layout analysis of newspaper images. He has been involved in numerous research projects including the Vienna City Library poster collection analysis, digitization of museum holdings in Lower Austria, and the AutoFlow project for medical data analysis. His laboratory work centers around the Computer Vision Lab (E193-01) at TU Wien, where he collaborates on projects involving document image analysis, cultural heritage preservation, and medical data processing. His team has developed tools for manuscript analysis, document reconstruction, and flow cytometry data interpretation, often in collaboration with cultural institutions and medical facilities.

• Optimization
• Learning Systems
• Image & Video Processing
• +۳ مورد دیگر

Dr. Ufuk ÖZKAYA is a Lecturer in the Department of Electrical and Electronics Engineering at Süleyman Demirel University's Faculty of Engineering and Natural Sciences. His research focuses on optimization, learning systems, and image/video processing. He holds a PhD in Electronics and Communication Engineering from Yildiz Technical University (2011), following a Master's (2005) and Bachelor's (2003) from Süleyman Demirel University. His work spans interdisciplinary applications, including Ottoman character recognition, biomedical signal analysis, and generative adversarial networks (GANs) for image processing. Notable contributions include GAN-based text segmentation methods and secure digital image scrambling algorithms. With an h-index of 6 and 133 citations, his research bridges theoretical advancements and practical solutions in telecommunications and medical imaging. Education: BSc (2003), MSc (2005) from Süleyman Demirel University; PhD (2011) from Yildiz Technical University Research Interests: Optimization algorithms, neural networks, biomedical signal processing, document analysis Selected Projects: Development of automated calcium imaging analysis systems, Ottoman document digitization His recent work emphasizes applying deep learning to historical document analysis and medical diagnostics, reflecting a commitment to both technological innovation and cultural preservation.

• Information Retrieval
• Contextual Recommendation
• Natural Language Processing
• +۳ مورد دیگر

Anirban Chakraborty is a Lecturer in Computer Science (AI and Data Science) at the University of Wolverhampton's Faculty of Science and Engineering. He leads research initiatives in the Data Science and AI group (DAIREL) at the Digital Innovations and Solution Centre. His academic journey includes a PhD in Computer Science from Trinity College Dublin (2021) and post-doctoral research at the University of Edinburgh's School of Informatics (2022-2024). Current Position: Lecturer (AI/Data Science) at University of Wolverhampton Previous Roles: Post-doctoral Research Associate at University of Edinburgh Education: PhD in Computer Science (Trinity College Dublin, 2021) His research spans personalised information retrieval , contextual recommendation , NLP , and machine learning . Recent work focuses on multi-contextual point-of-interest recommendation and noise handling in OCRed text . Publications demonstrate expertise in relevance modeling, query variants, and co-occurrence analysis. Key contributions include frameworks for contextual recommendation systems and robust retrieval from noisy datasets.

• Software Engineering
• Software Performance Engineering
• Geographic Information Systems (GIS)
• +۴ مورد دیگر

Dr. Eng. Tomasz Babczyński is a researcher at Wrocław University of Science and Technology, affiliated with the Faculty of Computer Science and Telecommunications and the Department of Technical Computer Science. Software Engineering (specializing in Software Performance Engineering) Geographic Information Systems (GIS) software Image processing and pattern recognition (focusing on handwritten document analysis) His research spans interdisciplinary applications of computer science, including: Railway systems dependability analysis (ERTMS/ETCS protocols) Historical landscape modeling using GIS Multiagent system performance evaluation for power systems Advanced techniques for line segmentation in handwritten documents Recent publications (2020-2024) demonstrate expertise in tensor voting algorithms, data heterogeneity challenges, and safety-critical system analysis. His work combines theoretical computer science with practical applications in transportation, energy, and historical information systems. Contact: tomasz.babczynski@pwr.edu.pl

• Semantic Web
• Natural Language Processing
• Information Retrieval
• +۵ مورد دیگر

Dr. Jörg Waitelonis serves as Scientific Co-Worker at FIZ Karlsruhe – Leibniz Institute for Information Infrastructure and Senior Researcher at Karlsruhe Institute of Technology's Institute of Applied Informatics and Formal Description Methods (AIFB), working under Prof. Dr. Harald Sack. His career spans over 15 years in semantic technologies research, beginning at Hasso-Plattner-Institute (2009-2018) and continuing at KIT/FIZ Karlsruhe. Dr. Waitelonis' research focuses on practical implementations of Semantic Web technologies across diverse domains. His work bridges theoretical knowledge representation with real-world applications in cultural heritage informatics, materials science, historical document analysis, and sports science. Key contributions include developing domain-specific ontologies (CourtDocs, PMD Core), advancing FAIR data implementation within Germany's NFDI framework, and creating knowledge graph solutions for cross-disciplinary research data integration. His publication record demonstrates consistent output in top Semantic Web venues (ISWC, SEMANTiCS) with recent work (2023-2025) emphasizing practical ontology engineering for national research data infrastructure projects. Current research directions show increasing focus on materials science ontologies, historical document processing, and BFO-based knowledge representation frameworks. As founder of yovisto GmbH since 2012, Dr. Waitelonis maintains strong industry connections while pursuing academic research. His work demonstrates the practical application of semantic technologies to solve real-world data integration challenges across multiple scientific domains.