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
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.
Jan Kofroň is an Associate Professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research focuses on program verification, static analysis, and software reliability. His educational background includes a Ph.D., as indicated by his title. He maintains an active research profile with numerous recent publications in top venues. Professor Kofroň's research interests span several key areas in software engineering and formal methods. He specializes in interpolation-based code model checking, static analysis of programs, system behavior models and verification, and programming language semantics. His work bridges theoretical foundations with practical applications in software development. His recent publications demonstrate strong focus on verification techniques for complex software systems, particularly in the areas of Horn clause solving, program comprehension using computational notebooks, and uncertainty-aware self-adaptive cyber-physical systems. His research shows consistent contributions to both theoretical foundations and practical applications of program analysis. Professor Kofroň actively leads and participates in multiple research projects including the current AIDE project (Advanced Analysis and Verification for Advanced Software) and past projects such as SNAPPY, ROBUST, Weverca, Q-ImPrESS, SOFA 2, and ASCENS. He teaches courses related to Java programming, Python programming, program semantics, and mobile devices programming. He maintains an open-door policy for student consultations, requesting students email him to arrange meetings rather than maintaining fixed office hours. His department maintains active GitHub presence, contributing to open-source projects related to software verification and analysis tools.
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.
David Bednárek is an academic affiliated with the Department of Software Engineering at Charles University's Faculty of Mathematics and Physics. He actively teaches programming, compilers, and cloud computing topics, with responsibilities in both winter and summer semesters. His research and teaching focus on software engineering , particularly C++ programming , compiler design , and high-performance computing . He maintains professional presence through platforms like ResearchGate, GitHub, and Google Scholar. David Bednárek can be reached at david.bednarek@matfyz.cuni.cz and is based in room S203, 2nd floor, Lesser Town, Prague 1.
Michal Töpfer is a PhD student and academic tutor at the Department of Distributed and Reliable Systems within the Faculty of Mathematics and Physics at Charles University in Prague. Holding the RNDr. degree (Doctor of Natural Sciences), he actively contributes to both teaching and research at the university. His academic affiliations include: Department of Distributed and Reliable Systems (D3S) Faculty of Mathematics and Physics Charles University Dr. Töpfer's research centers on integrating machine learning with software architecture, particularly for self-adaptive and component-based systems. His work develops systematic approaches to incorporate ML capabilities into software architectures, enabling systems that can self-optimize and adapt to changing conditions. He has made significant contributions to the DEECo component model through his ML-DEECo extension and has developed visualization components for the IVIS framework used in IoT applications. His publication trajectory shows increasing focus on leveraging large language models for software engineering tasks and optimizing component-based architectures. The research spans theoretical foundations of component models to practical implementations addressing real-world challenges in distributed systems, with applications in smart farming and Industry 4.0. As an educator, he teaches practical sessions for undergraduate courses including Introduction to Algorithms and Programming 1 during winter semesters and Programming 2 during summer semesters. While he supervises student work, specific advisees are not documented in available sources. He actively contributes to the academic community through organizing the Mathematical Correspondence Seminar (PraSe), the Young Mathematicians Camp, and the Kasiopea programming competition. His technical expertise spans web development, software architecture, machine learning integration, and data visualization.
