جستجوی استاد راهنمای دانشگاهی

Daniel Hlubinkaمشاهده پروفایل

دانشیار

• Statistics
• Theoretical Foundations
• Multivariate Functional Processes
• +۴ مورد دیگر

Daniel Hlubinka is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University. He has been an academic staff member since 1999 and was promoted to associate professor in 2007. His teaching includes courses like Statistics for Financial Mathematicians 2 (NMFM332) and Proseminar in Probability and Mathematical Statistics (NMSA262). He supervises bachelor's, diploma, and doctoral theses, with over 30 bachelor's, 20 diploma, and 5 doctoral theses supervised to date, including former students now working as associate professors. Education: Mathematical Physics (1989-1994) at Charles University; Erasmus stay at Limburgs Universitaire Center (1994-1995); Doctorate (1995-1999) under Professor Josef Štěpán. Research Interests: Statistics, theoretical foundations, multivariate functional processes, nonparametric asymptotics, data depth, optimal transport, and mathematics of chance. Academic Affiliations: Member of the Union of Czech Mathematicians and Physicists, Czech Mathematical Society, Czech Statistical Society, European Mathematical Society, Bernoulli Society, and Institute of Mathematical Statistics. Hlubinka's recent research focuses on functional data analysis, multivariate quantiles, and nonparametric testing. His work applies optimal transport theory, empirical characteristic functionals, and permutation tests to functional statistical problems. He contributes to methodological advancements in depth-based classification, time reversibility testing, and regression models.

Michal Pavelkaمشاهده پروفایل

دانشیار

• Geometric mechanics and thermodynamics (GENERIC)
• Modelling of electrochemical systems (fuel cells, batteries)
• Non-equilibrium continuum thermodynamics and hyperbolic systems
• +۱ مورد دیگر

Michal Pavelka is an Associate Professor at the Division of Mathematical Modeling, Mathematical Institute, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His career spans roles from Postdoc (part time) at the Institute of Chemical Technology to positions at École Polytechnique de Montréal and New Technologies Research Centre. He earned his Ph.D. in 2015 and M.Sc. in 2012 at Charles University under František Maršík. Michal Pavelka's research integrates Non-equilibrium Thermodynamics , Geometric Mechanics , and Machine Learning . His work bridges advanced mathematical frameworks like GENERIC and Extended Irreversible Thermodynamics with practical applications in electrochemical systems (fuel cells, batteries) and quantum fluids . Notably, he has contributed to Smoothed Particle Hydrodynamics and Hamiltonian mechanics in complex systems. His recent publications focus on Multiscale Thermodynamics , Superfluid Modeling , and Machine Learning in Physics . Scientific awards include the Best paper award, Entropy (2021) and Czech Grant Agency President's award (2020). He has secured significant grants, including a €363k Czech Grant Agency award (2023–2025) for geometric multiscale thermodynamics of complex fluids. Scientific Awards: Best paper award, Entropy (2021) Czech Grant Agency President's award (2020) High quality monographs of Charles University competition (1st-3rd place, 2020) Current Projects: He leads research on geometric multiscale thermodynamics and co-supervises projects on zinc-air batteries and solid oxide fuel cells. His lab develops the SmoothedParticles.jl Julia package for fluid dynamics simulations.

Jan Ratajمشاهده پروفایل

استاد

• Integral Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +۲ مورد دیگر

Jan Rataj is a Professor at the Faculty of Mathematics and Physics , Charles University , specializing in Integral Geometry , Geometric Measure Theory , and Stochastic Geometry . He has been affiliated with Charles University since 1991 and has held visiting positions at universities in Jena, Karlsruhe, Ulm, and Aarhus. Research interests focus on geometric probability, convex geometry, and measure theory Teaches courses on Measure and Integration Theory , Geometry , and advanced topics in geometric measure theory Scientific contributions include coauthoring 2 monographs and ~50 research papers. His work explores: Random measurable sets and perimeter analysis Stochastic modeling of microstructures Geometric functionals and invariants Applications to spatial statistics and probability theory He supervises PhD students and leads the Seminar of Stochastic Geometry , which examines topics like: Max-stable random fields Convex body reconstruction Point process analysis

Ondřej Pejchaمشاهده پروفایل

دانشیار

• Astrophysics
• Binary Stars
• Common Envelope Evolution
• +۷ مورد دیگر

Assoc. Prof. Ondřej Pejcha is an active researcher at the Institute of Theoretical Physics, Faculty of Mathematics and Physics, Charles University in Prague. His work focuses on connecting theoretical modeling, observational astronomy, and data science to understand time-domain phenomena in stellar systems, particularly binary stars and their evolution. Dr. Pejcha's research spans computational astrophysics, with emphasis on (radiation)(magneto)hydrodynamics simulations of binary star systems, common envelope evolution, and stellar mergers. His group develops advanced numerical methods to study transients such as stellar mergers and core-collapse supernovae, persistent variable sources including eclipsing binaries and pulsating stars, and stellar dynamics. They combine supercomputer simulations with semi-analytic models and machine learning techniques applied to astronomical data from surveys like ASAS-SN. The analysis of Dr. Pejcha's recent publications reveals a strong focus on computational approaches to binary star evolution, with increasing integration of machine learning methods in the latest works. His research addresses fundamental questions about common envelope evolution, mass transfer processes, and the connection between theoretical models and observational signatures in time-domain astronomy. ERC Consolidator grant for ROGALLO project (developing new simulation methods for binary stars) ERC Starting grant for Cat-In-hAT project (computational methods for binary star mergers) Czech Science Foundation grant for studying mass transfer in binaries Czech-American collaboration grant for ASAS-SN survey participation Primus award PRIMUS/SCI/17 from Charles University Dr. Pejcha actively mentors PhD students and postdocs, with recent advisees including Jakub Cehula and Milan Pešta. His group has secured substantial funding that supports internationally competitive salaries, dedicated computing resources including a specialized cluster with upcoming GPU enhancements, and travel funds. Alumni from his group have successfully obtained competitive postdoctoral positions and national/international fellowships. The group maintains strong international connections, regularly collaborating with institutions including Princeton University, Brown University, MPA Garching, and Warsaw University Observatory.

• Structural Bioinformatics
• Data Visualization
• Protein Structure
• +۴ مورد دیگر

David Hoksza is an Associate Professor at the Department of Software Engineering, Faculty of Mathematics and Physics, Charles University in Prague. His work bridges bioinformatics and computational biology through innovative algorithm and tool development. University: Charles University School: Faculty of Mathematics and Physics Department: Department of Software Engineering His research focuses on structural bioinformatics and data visualization, particularly in protein and RNA structure analysis. He has also contributed to cheminformatics (ligand-based virtual screening), computational genomics (MinION data analysis), and systems biology (molecular network visualization). David has led major projects including the P2Rank framework for ligand-binding site prediction, R2DT for RNA secondary structure visualization, and the Genomics 2 Proteins portal for linking genetic data to protein structures. His publications span high-impact journals like Nucleic Acids Research , Bioinformatics , and Nature Methods . Recent articles highlight his expertise in RNA structure prediction, protein-ligand interactions, and neurodevelopmental disorder analysis. His work emphasizes template-based modeling, machine learning, and web-based tool development. Scientific Awards David actively teaches courses on data visualization and bioinformatics algorithms, with a focus on practical implementation using technologies like D3.js, Tableau, and Python. He contributes to open-source software and maintains a GitHub repository for his tools.

Marek Omelkaمشاهده پروفایل

دانشیار

• Asymptotic Statistics
• Nonparametric Statistics
• Copula Models
• +۱۰ مورد دیگر

Assoc. Prof. Marek Omelka is a faculty member at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on copula models, dependence modeling, and nonparametric statistical methods with applications in multivariate analysis, permutation tests, and conditional copula structures. Current teaching: NMST424 Mathematical Statistics 3 and JEM019 Statistical Methods for Data Analysis Research interests: Asymptotic statistics, copula estimation, permutation methods, multivariate association, and functional data depth His recent publications analyze copula parameter estimation, covariate effects in conditional models, and multivariate tail coefficients, with implementations in R. The PMSE program he guarantees connects theoretical statistics with real-world applications in data science, finance, and medical studies.

Erik Dernerمشاهده پروفایل

پژوهشگر

• Human-Centric Artificial Intelligence
• Large Language Models
• Robotics
• +۶ مورد دیگر

Erik Derner is an ELLIS Postdoctoral Researcher at the ELLIS Unit Alicante, focusing on human-centric AI and ethical LLMs. He collaborates with Prof. Robert Babuška's Machine Learning team at the Czech Institute of Informatics, Robotics, and Cybernetics (CIIRC) and contributes to the VIVES project under the PERTE of New Language Economy. His work addresses biases in LLMs, AI safety, and security, while also exploring symbolic regression for robotics and reinforcement learning. Education: Ph.D. in Robotics and Machine Learning (2022) from Czech Technical University in Prague, with B.Sc. and M.Sc. in Open Informatics. Erik's research spans human-centric AI , large language models , robotics , computer vision , and reinforcement learning . He emphasizes ethical and secure AI development, particularly for underrepresented languages. His work integrates symbolic regression with neural networks to create interpretable and efficient models for robotics. Recent publications highlight his focus on LLM security , gender bias analysis , and symbolic regression for dynamic systems. Notable trends include ethical AI, multimodal red-teaming, and physics-informed modeling. Scientific Awards: Werner von Siemens Award (2023) for his Ph.D. thesis. CTU FEE Dean's Award (2023) for a prestigious dissertation. Erik supervises student projects in robotics, AI ethics, and LLM security, including internships and theses on topics like mental health risks, toxicity evaluation, and real-time assistance for the visually impaired.

Erin Claire Carsonمشاهده پروفایل

استادیار

• Numerical Linear Algebra
• Parallel Computing
• High-Performance Computing
• +۷ مورد دیگر

Erin Claire Carson is an Assistant Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University, Prague. A specialist in numerical linear algebra and high-performance computing, she leads the ERC Starting Grant project InEXASCALE focused on exascale algorithms. Her research explores mixed precision arithmetic, communication-avoiding Krylov subspace methods, and stability analysis in finite precision. Ph.D., University of California, Berkeley (2015) Courant Instructor, New York University (2015-2018) Postdoctoral Researcher and PRIMUS Fellow, Charles University (2018-2022) Dr. Carson's work bridges theoretical analysis with practical implementations for supercomputers. Her recent publications include advancements in low-synchronization orthogonalization, silent error detection, and multilevel sampling techniques. She received the 2025 Wilkinson Prize from SIAM for outstanding contributions to numerical analysis. Current trends in her research involve: Exploiting mixed precision arithmetic for algorithm acceleration Developing stable communication-avoiding Krylov methods Optimizing numerical stability in GPU-based solvers Understanding error propagation in multistage refinements Scientific honors: 2025 SIAM Wilkinson Prize in Numerical Analysis and Scientific Computing 2023 ERC Starting Grant recipient 2019-2022 PRIMUS Research Fellow She supervises PhD and Master’s students while teaching advanced courses in numerical linear algebra and high-performance computing. Her work has been featured in WIRED and Forbes Czech Republic for improving supercomputer algorithms.

• Adaptive Software Architectures
• Smart Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +۵ مورد دیگر

Tomáš Bureš is a Professor at the Department of Distributed and Dependable Systems , Faculty of Mathematics and Physics , Charles University . His work focuses on adaptive software architectures for smart cyber-physical systems (CPS) and IoT, leveraging machine learning (neural networks) for self-adaptation, edge-cloud systems, and model-driven development. Research Groups: Leader of the SmartArch research group. Projects: Involved in ExtremeXP , ESTABLISH , AFarCloud , FitOptiVis , Trust 4.0 , ASCENS , RELATE ITN , Q-ImPrESS . Publications span topics like performance regression testing, ML workflow optimization, confidentiality analysis under uncertainty, and edge-cloud continuum modeling. Key subfields include self-adaptive architectures, adaptation rules, component ensembles, and statistical methods in software engineering. His work bridges theoretical models (e.g., DEECo, SOFA) with practical tools (e.g., ExpEngine, Robin) for CPS and cloud applications. Co-Authors include P. Hnětynka, M. Abdullah, R. Heinrich, and others. Publications appear in venues like IEEE Transactions on Autonomic Computing , ACM/SPEC ICPE , and ECSA Workshops .

• Natural Language Processing
• Computational Linguistics
• Statistical Dialogue Systems
• +۳ مورد دیگر

Assoc. Prof. Vladislav Kuboň, Ph.D., serves at the Institute of Formal and Applied Linguistics (UFAL) within the Faculty of Mathematics and Physics at Charles University in Prague. He holds active roles in faculty management and advisory bodies, with his office located in room S 422 at Malostranské nám. 2/25, Prague 1. His research focuses on computational approaches to language processing, including: Natural Language Processing methodologies Statistical dialogue system architectures Dependency parsing and treebank harmonization Machine learning applications for multilingual NLP Resource development for linguistic frameworks Supervision includes Ahmad Aghaebrahimian's work on the HamleDT (HArmonized Multi-LanguagE Dependency Treebank) project, which establishes standardized linguistic resources across languages. Teaching responsibilities encompass advanced courses such as Statistical Dialogue Systems, reflecting his expertise in human-computer interaction through language technology. Contact details include email vk@ufal.mff.cuni.cz and phone 95155 4255, with a maintained professional homepage at http://ufal.mff.cuni.cz/~kubon/.

• Computational Linguistics
• Morphology
• Grammar
• +۷ مورد دیگر

Jiří Hana is a researcher at the Institute of Formal and Applied Linguistics (UFAL), Faculty of Mathematics and Physics, Charles University in Prague. His work focuses on computational linguistics, particularly resource-light morphological analysis, learner corpora, and grammar modeling for morphologically complex languages like Czech and Russian. Key Research Areas : Computational morphology, learner corpus annotation, grammar models for fusional languages, tagset design, and natural language processing. Grants & Support : Funded by the U.S. NSF (award #0916280) and the Czech Republic Grant Agency (P406/10/P328). Publication Trends : His recent work (2017–2020) emphasizes syntactic annotation of non-native Czech within Universal Dependencies frameworks, leveraging learner corpora for language acquisition research, and developing tools like feat and law for morphological disambiguation. Advising Roles : Supervised 9 students (2011–2024) on projects spanning text classification, error detection, quote attribution, and multilingual learning through audiovisual input. Tools & Contributions : Developed the Russian positional tagset (2010), feat (layered error annotation), and law (manual morphological disambiguation).

• 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.

• Mathematical programming
• Stochastic optimization
• Integer optimization
• +۳ مورد دیگر

Martin Branda is an Associate Professor at Charles University's Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics. He serves as Guarantor of the Master's Program in Financial and Actuarial Mathematics and maintains an active research and teaching profile. Workplace: Department of Probability and Mathematical Statistics Room: K 157 (2030a), 1st floor, Karlín, Sokolovská 49/83, Prague 8 Contact: Phone 95155 3404, Email Martin.Branda@mff.cuni.cz Homepage: https://www2.karlin.mff.cuni.cz/~branda/ Dr. Branda's research focuses on mathematical programming, stochastic optimization, and their applications in financial risk management and actuarial mathematics. His work bridges theoretical optimization methods with practical insurance applications. His recent publications demonstrate expertise in operational research applied to insurance markets, with particular emphasis on risk modeling and calculation methodologies. The research shows strong connections between mathematical optimization techniques and practical insurance applications. Dr. Branda actively supervises student research, with numerous bachelor's and master's theses completed under his guidance. His teaching portfolio includes core optimization courses and specialized finance/insurance applications. He has led multiple research projects funded by the Fund for the Support of Education in the Insurance Industry, particularly in non-life insurance rate calculation (2014/2015), generalized linear models in insurance (2012/2013), and modeling annuities in compulsory insurance (2010/2011).

• Financial Mathematics
• Actuarial Science
• Statistical Analysis
• +۶ مورد دیگر

Assoc. Prof. Jan Hurt, CSc. is an Associate Professor at the Faculty of Mathematics and Physics , Charles University , Prague, Czech Republic. He holds leadership as Head of the Department of Financial and Actuarial Mathematics and is affiliated with the Department of Probability and Mathematical Statistics . With over two decades of teaching experience, Prof. Hurt specializes in computational finance, actuarial modeling, and statistical inference using Mathematica . Department of Financial and Actuarial Mathematics (Head) Department of Probability and Mathematical Statistics Faculty of Mathematics and Physics, Charles University His research focuses on Financial Mathematics , Actuarial Science , and Computational Statistics . He has developed extensive Mathematica -based educational resources for courses like Financial Management, Risk Measures, and Multivariate Analysis. His work spans Portfolio Optimization , Epidemiological Modeling , and Statistical Inference . Prof. Hurt has supervised numerous student defenses and integrated computational tools into actuarial education. He actively contributes to academic conferences, including the Wolfram Technology Conference and Morgan Stanley Quantitative Finance Conference . As a Mathematica license administrator for MFF and CERGE, he supports institutional software infrastructure.