جست‌وجوی استاد و پژوهشگر

• Data Protection Law
• Information Technology Law
• Open Data and PSI
• +۷ مورد دیگر

Jakub Míšek is an Assistant Professor at the Institute of Law and Technology, Faculty of Law, Masaryk University in Brno, Czech Republic. His work bridges legal scholarship with information technology, focusing on data protection, open data, and digital regulation. He holds a Ph.D. and multiple academic qualifications, including JUDr. and MgA. His research interests include Data Protection Law , GDPR compliance , Open Data and PSI reuse , proportionality in personal data processing , text and data mining exceptions , and legal aspects of cybersecurity . He explores how legal frameworks adapt to technological innovation, especially in public administration and research contexts. His recent scholarly output shows a strong trend toward interdisciplinary work combining law, computer science, and policy. He frequently publishes on CJEU case law, interoperability of European datasets, and regulatory methods such as accountability and risk-based approaches under GDPR. He is also deeply involved in practical legal guidance and policy recommendations. Co-author of the book Citation Analysis of Case Law (2021) Author of Modern Regulatory Methods of Personal Data Protection (2020) Contributor to Information Technology Law (2018) Co-editor of conference proceedings for JURIX 2024 He has advised on research projects related to law and technology, contributed to national reports on data protection, and organized major academic events such as the Cyberspace and JURIX conferences. His collaborations span across institutions and disciplines, reflecting a robust academic network in legal informatics and digital governance.

Pavel Veselýمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms
• Data Structures
• +۹ مورد دیگر

Pavel Veselý is an Assistant Professor at the Computer Science Institute of the Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in streaming, online, and approximation algorithms, with applications in bioinformatics and data privacy. His research interests include: Streaming algorithms for quantile estimation, geometric problems, and adversarial robustness Online algorithms, especially in scheduling and buffer management Approximation algorithms, including shortest superstrings and their use in genomic data (k-mer sets) Randomized algorithms and algorithmic data privacy His recent work has advanced the state of the art in streaming quantile estimation (KLL sketch), indexed k-mer representations using masked superstrings, and streaming facility location in high dimensions. His publications appear in top venues such as FOCS, STOC, PODS, and SODA. Notable scientific awards include: Best Paper Award at PODS 2021 2022 ACM SIGMOD Research Highlight Award Supervision of award-winning student theses, including Dean’s Award and Czech-Slovak competition wins He actively advises students, including PhD and master’s level researchers, and leads a research group focused on data sketches and streaming algorithms. He has taught courses such as Algorithmic Data Privacy, Randomized Algorithms, and Streaming Algorithms, and previously served as a postdoc at the University of Warwick under Graham Cormode.

• Static Analysis of Programs
• Formal Verification
• Concurrency
• +۱۲ مورد دیگر

Tomáš Vojnar is a Professor at the Department of Intelligent Systems, Faculty of Information Technology, Brno University of Technology (FIT BUT), Czech Republic. He has been a faculty member since 1999, promoted to Associate Professor in 2007 and full Professor in 2012. He leads the VeriFIT research group and has extensive experience in academic leadership, including candidacy for Dean of FIT BUT in 2023 and chairing major conferences such as TACAS'19 and SV-COMP 2018. Research Interests: Static and dynamic analysis of programs, especially those with pointers and dynamic data structures Formal verification of concurrent and infinite-state systems Automata-theoretic and logical methods in verification Binary code analysis and performance analysis His recent publications show a strong trend in developing and applying static analysis tools for verifying memory safety, concurrency, and semantic equivalence in C programs. He has made significant contributions to tools like Predator, 2LS, and DiffKemp, which are widely used in the verification community and have achieved top rankings in international competitions such as SV-COMP. His work bridges theoretical foundations in automata and logic with practical software verification applications. Scientific Awards: Best theory paper award of ETAPS'10 (EATCS) Best paper awards at CADE'19 and CIAA'08 Best tool paper awards at ISSTA'18 and RV'12 Multiple gold, silver, and bronze medals in SV-COMP (Predator, 2LS, Symbiotic) Bronze Humies award for automated design of approximate circuits Gödel prize from FLoC 2014 Olympic Games 1 gold and 3 silver medals at SL-COMP'14 Siemens prize for outstanding PhD thesis (2001) Advising and Grants: He has supervised numerous PhD and Master's students who have contributed to major verification tools and publications. He has been actively involved in national and international research projects, including the Czech Science Foundation panel P103 (2019–2023), and has secured funding for research in formal methods and software verification. His collaborations span institutions in France (LIAFA, CNRS), Austria (TU Vienna), and beyond. Labs and Teams: He leads the VeriFIT research group at FIT BUT, which focuses on formal methods and software verification. The group develops state-of-the-art tools such as Predator (shape analysis), 2LS (heap verification), DiffKemp (semantic diffing), and others used in both academia and industry. The team actively participates in international verification competitions and collaborates with researchers worldwide.

Iveta Hnetynkovaمشاهده پروفایل

دانشیار

• Numerical Mathematics
• Linear Algebra
• Inverse Problems
• +۵ مورد دیگر

Iveta Hnetynkova is an Associate Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. She specializes in numerical linear algebra, inverse problems, and regularization methods, with applications in image processing and scientific computing. Education: Doctor of Natural Sciences (RNDr.) from Charles University (2003) Ph.D. in Scientific Computations from Charles University (2006) Habilitation thesis on error-contaminated linear approximation (2019) Research Interests: Krylov subspace methods Total Least Squares (TLS) formulations Noise revealing in discrete inverse problems Tensor generalizations and structured matrices Applications in image processing and jewelry defect analysis Scientific Awards: Visegrad Group Young Researcher Award (2014) J. Jirsa Prize for textbook excellence (2013) I. Babuska Prize (2nd place) (2007) SVOČ Prize in mathematics (2003)

• Interconnection Networks
• Extremal Combinatorics
• Graph Theory
• +۳ مورد دیگر

Assoc. Prof. Petr Gregor is a faculty member at the Department of Theoretical Computer Science and Mathematical Logic , Faculty of Mathematics and Physics , Charles University in Prague . His research focuses on algorithmic and structural problems in interconnection networks, leveraging tools from extremal combinatorics, graph theory, and coding theory. Research Interests : Interconnection networks, Gray codes, hypercube structures, symmetric graph decomposition, fault-tolerant combinatorial algorithms. Teaching : Offers courses in propositional/predicate logic, computational complexity, hypercube structures, automata theory, and data structures. Awards : Best paper award at MFCS 2022 . Contact : Petr.Gregor@mff.cuni.cz | gregor@ktiml.mff.cuni.cz | Personal Website

• Natural Language Processing
• Computational Linguistics
• Dependency Syntax
• +۵ مورد دیگر

Prof. Zdeněk Žabokrtský is a Professor at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University in Prague. He serves as the head of the PhD study program in Computational Linguistics and teaches several courses including Language Data Resources, Variability of Languages in Time and Space, Natural Language Processing, and Introduction to Language Technologies. His office is located in room S 409 on the 4th floor in the Lesser Town area of Prague. Prof. Žabokrtský's research spans multiple areas of Natural Language Processing and Computational Linguistics. His primary interests include building multilingual morphological resources, developing NLP applications that utilize parallel corpora, studying dependency syntax and valency frameworks, coreference resolution, theoretical studies on formal representations of natural languages, and applying Machine Learning techniques to linguistic problems. His work bridges theoretical linguistics with practical applications in language technology. Analysis of Prof. Žabokrtský's recent publications reveals a strong focus on morphological analysis, coreference resolution across multiple languages, and cross-lingual NLP. His research spans diverse languages including Czech, Turkish, Russian, and various Indic languages. He has made significant contributions to morphological resources, word-formation networks, and multilingual coreference systems, often creating and utilizing linguistic resources while developing novel computational approaches to linguistic phenomena. As an academic leader, Prof. Žabokrtský has supervised numerous PhD students and contributed to major research projects at the Institute of Formal and Applied Linguistics. His work has been supported by various grants that have enabled the development of important linguistic resources and NLP tools. He has been instrumental in establishing the PhD study program in Computational Linguistics at Charles University. Prof. Žabokrtský is a key member of the Institute of Formal and Applied Linguistics research teams, contributing to projects focused on developing language technologies, creating linguistic resources, and advancing theoretical understanding of natural language processing. His work is closely integrated with the Prague Dependency Treebank project and other major linguistic resources developed at Charles University.

Lubomír Bulejمشاهده پروفایل

دانشیار

• Dynamic Program Analysis
• Software Performance Evaluation
• Software Performance Modeling
• +۵ مورد دیگر

Lubomír Bulej serves as an Associate Professor at the Department of Distributed and Dependable Systems within the Faculty of Mathematics and Physics at Charles University, Prague. His office is located in room S 205 in the historic Lesser Town district, with contact details including email bulej@d3s.mff.cuni.cz and phone +420 951 554 189. He maintains an active research profile through platforms like Google Scholar, DBLP, and GitHub. His research centers on dynamic program analysis and software performance evaluation , with specific focus on analysis composition, program instrumentation, profiling accuracy, and observability in managed platforms. He develops methods for automatic performance evaluation during development, performance change detection, and testable documentation of performance assumptions. Additional interests span object-oriented programming, programming languages, operating systems, and computer architectures, reflected in his teaching of courses like Computer Architecture and Operating Systems. Analysis of his 2021-2025 publications reveals a consistent trajectory in performance-aware software development , with increasing emphasis on compiler technologies (particularly GraalVM), self-adaptive systems for cloud/edge environments, and cost-optimized performance testing methodologies. His work bridges theoretical program analysis with practical industrial applications, especially in avionics and distributed systems. Dr. Bulej actively contributes to major research projects including GraalVM compiler evaluation, FitOptiVis (avionics systems), ASHLEY (aircraft demonstrators), Ferdinand (model-driven design evaluation), Q-ImPrESS (service-oriented systems), and CoCoME (component modeling). He maintains critical software infrastructure such as the Renaissance Benchmark Suite, DiSL instrumentation framework, and Java Performance Measurement Framework.

• Multi-model data management
• Schema inference and evolution
• XML and semi-structured data
• +۳ مورد دیگر

Irena Holubová, Ph.D. , is an Associate Professor at the Department of Software Engineering , Faculty of Mathematics and Physics , Charles University in Prague, Czech Republic. Her work focuses on multi-model data management, schema evolution, XML and graph data processing, and inferred social networks. She has led or co-led numerous national and international research projects, supervised over half a dozen Ph.D. students, and contributed extensively to the academic community. Education: Ph.D. in Computer Science (exact university and year not specified in text) Research Interests: Her research spans multi-model data management , including modeling, schema inference, and evolution. She explores inferred social networks through graph analytics and real-world data. Previously, her work included Linked Data , XML schema inference , XML benchmarking , and management of XML data using relational databases . Grants and Projects: Principal researcher, GAČR 23-07781S: Self-Adapting Management of Multi-Model Databases (2023–2025) Principal researcher, GAČR 20-22276S: Unified Management of Multi-Model Data (2020–2022) Co-researcher, TAČR TH03010276: System for advanced analytics of large connected data (2018–2020) Supervisor, GAUK 292323: Self-adaptive management of multi-model databases (2023–2025) Supervisor, GAUK 1416213: Evolution and adaptability of complex applications (2013–2015) Co-researcher, GAČR P202/10/0573: Handling XML Data in Heterogeneous and Dynamic Environments (2010–2012) Principal researcher, GAČR 201/09/P364: Management of XML data in (object-)relational databases (2009–2011) Member, GAČR 201/06/0756: Development of a Native Storage for XML Data (2006–2008) Member, SemWeb 1ET100300419: Intelligent Models for the Semantic Web (2005–2008) Conference Organization: Co-organizer: FiCloud ’17, MobiWis ’17, Innovate-Data ’17, NoSQL-Net ’15/’14, BenchmarX ’10/’09, X-Schemas ’09, MoViX ’09 Editorial and Reviewing Roles: PC member: MEDI, IDEAS, ACM SAC, ICIST, CoMoNoS, RCIS, VLDB Journal, Big Data Research Journal, and many more Reviewer: VLDB Journal, The Computer Journal, LNCS Transactions, special issues, and books Independent expert: EU FP7, Horizon 2020 Ph.D. Students Supervised: Jáchym Bártík (2022–) Pavel Koupil (2019–2022, finalist Antonín Svoboda Award) Marek Polák (2012–2017) Martin Svoboda (2010–2015, Dean's Award for Best Master Thesis) Jakub Stárka (2010–2013) Tomáš Knap (2008–2013, finalist IT Master Thesis 2009) Labs and Teams: She leads the Data Management Research Group at the Department of Software Engineering, focusing on multi-model databases, schema evolution, and XML technologies. The group has produced tools like MM-cat , Socneto , STOCK , BDgen , DaemonX , jInfer , and Analyzer .

Martin Krulišمشاهده پروفایل

دانشیار

• High-performance computing
• GPU programming and CUDA optimization
• Parallel algorithms and data structures
• +۴ مورد دیگر

Martin Kruliš is an associate professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University , Prague, Czech Republic. His primary roles include research, teaching, advising, and leading projects that bridge high-performance computing, GPU programming, and self-adaptive systems. Education: While exact details of his own degrees are not provided, Dr. Kruliš’s extensive publication record and faculty position at Charles University indicate advanced training in computer science with specialization in parallel and distributed computing. Research Interests: High-Performance GPU Computing: Deep investigation into CUDA kernel optimization, memory bandwidth utilization, and workload dispatching for massively parallel accelerators. Self-Adaptive & Self-Optimizing Systems: Designing ensemble-based abstractions that integrate machine-learning estimators to enable runtime adaptation in component architectures. Parallel Algorithms & Data Structures: Development of cache-friendly, SIMD-aware, and GPU-accelerated algorithms for clustering, dimensionality reduction, and similarity search. Software Engineering for Parallelism: Creation of C++ libraries, DSLs (e.g., Bobolang), and educational tools (ReCodEx) that simplify parallel programming and automated evaluation. Publication Trends: Across 2011–2025, Kruliš’s articles reveal a clear trajectory from foundational GPU-accelerated indexing and multimedia retrieval toward sophisticated self-adaptive systems that leverage machine learning. Recent works (2023–2025) increasingly focus on integrating LLMs and neural networks into compiler and runtime optimization loops, reflecting a convergence of AI and systems research. Scientific Awards & Recognition: No specific awards or fellowships are mentioned in the provided text; however, sustained publication in top-tier venues (JPDC, IPDPS, Euro-Par, SEAMS) and active involvement in program committees and tool development indicate strong peer recognition. Teaching & Student Supervision: Teaches Programming in Parallel Environment (NPRG042) , Advanced Programming in Parallel Environment (NPRG058) , Computer Systems (NSWI170) , and Software Projects . Supervises numerous bachelor’s and master’s theses; exact student names are not listed in the text. Labs, Projects & Tools: ReCodEx: A widely used platform for semi-automated evaluation of programming assignments at Charles University. Simdex: A modular simulator of the ReCodEx backend that enables realistic experimentation with self-adaptive job dispatching and machine-learning controllers. Active contributor to open-source repositories on GitHub, focusing on GPU kernels, benchmarking frameworks, and educational tooling.

• Natural Language Processing
• Artificial Intelligence
• Information Extraction
• +۴ مورد دیگر

Assoc. Prof. Pavel Pecina, Ph.D., is a faculty member at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His primary academic role as an Associate Professor focuses on Natural Language Processing , Artificial Intelligence , and related areas. Research Interests: Information extraction, information retrieval, machine translation, multimodal data interpretation, optical music recognition Teaching: Courses since 2012-2022 including Natural Language Processing , Information Retrieval , and Statistical Methods in NLP Key Research Projects include GI-Insight (AI for healthcare), RES-Q+ (global stroke care registry), and MEMORISE (heritage digitization). His recent publications (2023-2025) demonstrate expertise across clinical NLP , historical document analysis , and music technology domains. Advising: Supervised 10+ graduate students in areas ranging from semantic search to optical music recognition. Collaborates with international institutions on multilingual translation and medical informatics challenges through initiatives like IWSLT and CLEF eHealth.

• interpretation of deep neural networks
• machine learning
• dependency syntax
• +۳ مورد دیگر

David Mareček is a researcher at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University. His work focuses on neural network interpretation, machine learning, dependency syntax analysis, and machine translation, with a particular emphasis on unsupervised and semi-supervised parsing techniques. Current projects: GenderBias (2023–present), EduPo (2024–present) Long-term contributions in dependency treebanks (HamleDT, 2012–2016) and neural machine translation frameworks (Treex, TectoMT) His research explores linguistic structure in neural architectures, including BERT and Transformer models, through methods like structural probing and attention analysis. Recent publications examine gender bias mitigation, automated poetry analysis, and theatre script generation. Key collaborations include Rudolf Rosa, Tomáš Musil, and Tomasz Limisiewicz. He supervises students in projects spanning multilingual representations, language model probing, and bias detection in AI systems. Technical leadership in developing tools like THEaiTRobot and DEPFIX demonstrates his commitment to applied computational linguistics and robust NLP systems.

Milan Strakaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Neural Networks
• Deep Learning
• +۱۲ مورد دیگر

Milan Straka is a professor at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University, Prague. His research focuses on machine learning, artificial neural networks, deep learning, and structured prediction with applications to natural language processing (NLP) tasks including POS tagging, dependency parsing, named entity recognition, and optical music recognition. University: Charles University School: Faculty of Mathematics and Physics Department: Institute of Formal and Applied Linguistics Straka has developed several NLP tools like UDPipe and NameTag, contributing to multilingual coreference resolution, Czech grammar error correction, and historical language processing pipelines. His recent work involves contextualized embeddings, semantic parsing, and large-scale dataset creation for Czech NLP tasks. He collaborates on European Language Grid initiatives and has participated in multiple shared tasks (CRAC, MRP, W-NUT). His publications span topics from algorithm design (functional data structures) to modern transformer-based language models (RoBERTa) with emphasis on Czech language resources.

• Word-formation
• Derivational morphology
• Morphological segmentation
• +۵ مورد دیگر

Associate Professor Magda Ševčíková is a prominent linguist at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University in Prague. She specializes in morphological research with a focus on Czech language, particularly in word-formation, derivational morphology, and morphological segmentation. Her work bridges theoretical linguistics with computational approaches, making significant contributions to language resource development. Dr. Ševčíková's educational background includes a Ph.D. in Mathematical Linguistics (2009) and an Mgr. in Czech and German Philology (2004), both from Charles University. Her habilitation in Linguistics was completed in 2025 with a thesis on conversion between noun and verb in Czech. Her research interests center on word-formation phenomena, particularly conversion processes, derivational morphology of Czech, morphological segmentation, dependency syntax, and named entity recognition. Her publication record reveals a strong focus on data-driven morphological research, with recent work emphasizing corpus-based analysis of noun-verb conversion, derivational networks (particularly DeriNet), and cross-linguistic comparisons of morphological structures. She has pioneered methods for modeling morphographemic alternations and semi-automatic construction of word-formation networks. Dr. Ševčíková has served in significant academic roles including chair of the Czech Science Foundation's evaluation panel for Linguistics and Literature (2023-2025), member of the discipline committee for Social Sciences and Humanities, and Scientific Committee member for the Language Resources and Evaluation Conference. She has led multiple Czech Science Foundation projects on Czech word-formation and morphology. She actively contributes to major linguistic projects including DeriNet (a lexical network of Czech derived words), Universal Derivations collection, Universal Segmentations project, and the Czech Named Entity Corpus. Her work has established important language resources that support both theoretical linguistic research and practical NLP applications.

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

Lenka Weingartová is a researcher at the Institute of Formal and Applied Linguistics (UFAL), part of Charles University's Faculty of Mathematics and Physics in Prague. Her work focuses on advancing linguistic theory through computational methodologies within one of Central Europe's oldest and most prestigious academic institutions. Her research spans critical areas in modern linguistics: Computational Linguistics Natural Language Processing Formal Language Theory Applied Linguistics Machine Translation Speech Recognition As a core member of UFAL, she contributes to the institute's internationally recognized work in developing language technologies and theoretical frameworks. The institute maintains strong collaborations with European research networks while training next-generation linguists in both theoretical and applied domains.

Alexander Wilkieمشاهده پروفایل

استاد

• Computer Graphics
• Predictive Rendering
• Spectral Rendering
• +۷ مورد دیگر

Alexander Wilkie is a Full Professor at Charles University, Faculty of Mathematics and Physics (MFF), Department of Software and Computer Science Education. He has been affiliated with Charles University since 2008 and leads the computer graphics branch of the CGG research group. Position: Full Professor, Head of Computer Graphics Branch (CGG) Location: Mala Strana Room 425, Prague 1 Research interests include Predictive Rendering , Spectral Rendering , Fluorescence Effects , Polarization Modeling , and 3D Printing Optimization . His work focuses on creating physically accurate rendering techniques that predict real-world material appearances, particularly for fluorescent and polarizing surfaces. Recent publications highlight advancements in constrained spectral uplifting , fluorescence handling , and sky/atmosphere modeling . These works are published in venues like Computer Graphics Forum , SIGGRAPH , and Optics Express . Professional roles include membership in ACM SIGGRAPH and IEEE , reviewer for journals/conferences like Computer Graphics Forum (CGF) , and local organizer of EGSR 2011 . Labs and teams: Wilkie is part of the CGG research group , where members present ongoing work at seminars and collaborate on projects like the ART: Advanced Rendering Toolkit and DISTRO (Horizon 2020 grant).