جستجوی استاد راهنمای دانشگاهی

• Linked Data
• Open Data
• Semantic Web
• +۶ مورد دیگر

Jakub Klímek is an Associate Professor at the Department of Software Engineering, Charles University. He is a leading researcher in Linked Data and Open Data technologies, with a focus on semantic interoperability, XML schema evolution, and data integration frameworks. Education: Habilitation Thesis: Improving data accessibility and interoperability using linked data Doctoral Thesis: XML Formats Evolution and Integration Master Thesis: XML Schema Evolution Bachelor Thesis: Linear Algebra His research spans semantic web technologies, dataset discovery, knowledge graph applications, and model-driven data engineering. Recent publications address technical and legal interoperability of European open data, context-enhanced similarity search, and data specification tools. Scientific Awards: BEST DEMO PAPER AWARD (ISWC 2024) BEST PAPER AWARD (iiWAS 2017) BEST STUDENT PAPER AWARD (APCCM 2013) BEST PAPER AWARD (EGOV 2013) Klímek contributes to practical applications through tools like UnifiedViews, LinkedPipes, and DataSpecer, collaborating with researchers including Petr Škoda, Martin Nečaský, and Jan Helmich. His work emphasizes real-world implementation of semantic data standards in public administration and enterprise systems.

• Artificial Intelligence
• Multi-Agent Path Finding
• Robotics
• +۴ مورد دیگر

Jiří Švancara is a Lecturer at the Department of Theoretical Computer Science and Mathematical Logic (KTIML) within the Faculty of Mathematics and Physics at Charles University in Prague. His academic career focuses on Artificial Intelligence, particularly Multi-Agent Path Finding (MAPF), where he has established himself as a prominent researcher with numerous publications in top-tier conferences. His research interests span across Multi-Agent Path Finding , Robotics , Algorithm Design , and Constraint Satisfaction Problems . Švancara has made significant contributions to the field of MAPF, developing novel approaches for large-scale maps, temporal uncertainty handling, and efficient solving methods. His work bridges theoretical foundations with practical applications in robotics and transportation systems. Analysis of his recent publications reveals a strong focus on improving the scalability and robustness of MAPF algorithms. His research explores graph pruning techniques for large maps, handling temporal uncertainty in path execution, and comparing different objective functions for optimization. He has also investigated applications of MAPF in autonomous intersections and train routing systems, demonstrating the practical relevance of his theoretical work. Best Paper Award for 'Multi-agent Path Finding on Real Robots: First Experience with Ozobots' at IBERAMIA 2018 Švancara actively teaches multiple courses including Introduction to Artificial Intelligence, Propositional and Predicate Logic, and Algorithms and Data Structures. His teaching spans both theoretical foundations and practical implementation, with students engaging in programming assignments that reflect current AI challenges. His research collaborations are extensive, with frequent co-authorship with Roman Barták and other researchers in the field, indicating strong integration within the international AI research community. His laboratory work involves practical implementations of MAPF algorithms, including testing on real robots as evidenced by several publications describing experiments with Ozobots and other robotic platforms. This hands-on approach connects theoretical research with tangible robotic applications, providing valuable validation for his algorithmic contributions.

• Unified Processing of Multi-Model Data
• Influence Maximization in Social Networks
• Efficient Graph Data Processing
• +۳ مورد دیگر

Martin Svoboda is a Lecturer at the Department of Software Engineering , Faculty of Mathematics and Physics, Charles University, Prague. His work focuses on multi-model data management, social network analysis, and XML/linked data processing. University: Charles University School: Faculty of Mathematics and Physics Department: Department of Software Engineering Role: Lecturer Education: Doctoral Thesis: Correction of Invalid Trees with Respect to Regular Tree Grammars (2015) Master's Thesis: Processing of Incorrect XML Data (2010) Bachelor's Thesis: Information System for Small User Group Collaboration (2007) Research Interests encompass: Unified multi-model data processing Influence maximization in social networks Graph data efficiency Big Data and NoSQL systems Querying/indexing linked data XML document correction Publications highlight trends in multi-model data unification, XML correction techniques, and social network analysis, with a strong emphasis on category theory and graph-based approaches. Scientific Recognition: Dean’s Award for Best Master Thesis (2010) Best Poster Award at Reasoning Web Summer School (2012) Projects include grants from the Czech Science Foundation (20-22276S, 2010–2012), Technology Agency of the Czech Republic (TH03010276, 2018–2020), and international collaborations like NoSQL-Net (Germany, 2014).

Jana Strakováمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Deep Learning
• Multilingual Text Processing
• +۳ مورد دیگر

Jana Straková is a researcher at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University in Prague. Her work focuses on advancing natural language processing techniques for multilingual applications, particularly in deep learning architectures for linguistic analysis. Her research interests span Natural Language Processing , Deep Learning , and Multilingual Text Processing with specialization in named entity recognition, dependency parsing, and morphological analysis. She has pioneered neural network approaches for tasks like diacritics restoration and grammar error correction, with particular emphasis on Czech language processing while extending methodologies to Latin and other languages. Analysis of her publication record from 2019-2025 reveals consistent innovation in NLP tool development, particularly the NameTag and UDPipe systems. Her work demonstrates strong integration of contextual embeddings with linguistic resources, showing increasing focus on cross-lingual transfer and historical language processing in recent years. As part of the ÚFAL research group, she collaborates extensively with Milan Straka and Jan Hajič on developing open-source NLP pipelines. Her projects include creating large-scale datasets like CWRCzech for web relevance ranking and advancing ontology engineering through LLM-augmented approaches.

• Computer Science
• Parallel Computing
• Data Stream Processing
• +۳ مورد دیگر

Dr. Jakub Yaghob is a researcher at the Faculty of Mathematics and Physics , Charles University , specializing in computer science and parallel computing. He teaches advanced programming topics including Compiler Principles , Parallel Programming , and Cloud Computing . Research Interests : Parallel data stream processing, virtualization technologies, semantic web infrastructures, and performance optimization Teaching : Advanced C++ programming, virtualization administration, and computer systems architecture Technical Expertise : Design of parallelization frameworks, astrophysical data analysis, and hybrid CPU-GPU systems His publications focus on: Optimizing stream data processing across distributed architectures Developing domain-specific languages like Bobolang Performance evaluation in educational programming contexts Applications of parallel computing in astrophysics

• Theoretical Computer Science
• Computational Complexity
• Data Structures
• +۳ مورد دیگر

Prof. Michal Koucký is a faculty member at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on theoretical computer science, particularly computational complexity, data structures, algorithms, and combinatorics. He leads significant research projects including EPAC (supported by GA CR) and LBCAD (funded by ERC). His educational background includes a Ph.D. from Rutgers University (2003) and a Master's degree from Charles University (1998). Prof. Koucký's research spans multiple areas of theoretical computer science with emphasis on computational complexity , data structures , algorithms , and combinatorics . His work explores fundamental questions about computational efficiency, lower bounds, and algorithmic techniques. He has made significant contributions to edit distance algorithms, string processing, and circuit complexity. His research often bridges theoretical foundations with practical algorithmic applications, particularly in areas like dynamic data structures and approximation algorithms. An analysis of his recent publications (2019-2024) reveals a strong focus on string algorithms, particularly edit distance problems. His work develops novel approximation techniques, sketching methods, and sublinear-time algorithms for pattern matching. There's also significant work on data structure lower bounds and connections to popular complexity conjectures. His research demonstrates a consistent pattern of tackling fundamental problems with innovative approaches that push theoretical boundaries while maintaining practical relevance. Antonin Svoboda award for best dissertation in 2014 (awarded to Jan Bulánek) Best paper award at SAGT 2016 for 'The Big Match in Small Space' Best paper award at FOCS 2018 for 'Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time' He has successfully advised numerous graduate students including Pavel Dvořák, Karel Král, Debarati Das, and Jan Bulánek who have gone on to positions at prestigious institutions like Bristol, Google (Zurich), and Penn State. His research is supported by significant grants including the EPAC project (GA CR) and LBCAD project (ERC), demonstrating the impact and relevance of his work in theoretical computer science. Prof. Koucký is an active member of the theoretical computer science community, regularly presenting at major conferences including STOC, FOCS, ICALP, and SODA. His work often involves collaborations with researchers from institutions worldwide, contributing to a vibrant research environment at Charles University's Institute of Informatics.