جستجوی استاد راهنمای دانشگاهی

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

• Optimization
• Signal Processing
• Machine Learning
• +۱۲ مورد دیگر

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

• Applied Probability
• Insurance Mathematics
• Finance
• +۳ مورد دیگر

Poul G. Hjorth is an Associate Professor in the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark (DTU). He specializes in dynamical systems, mathematical modeling, and industrial applied mathematics, with strong interdisciplinary ties to neuroscience and industrial problem-solving. He has held visiting positions at the University of Southern Denmark and maintains affiliations with the University of Copenhagen as an External Lecturer. Ph.D. in Mathematical Physics, University of California, San Diego (1988) cand.scient in Mathematics, University of Copenhagen (1982) His research focuses on mathematical physics, classical mechanics, dynamical systems, and geometry , particularly in real-world industrial applications. He has been instrumental in organizing the European Study Groups with Industry (ESGI) in Denmark and is Executive Director of the European Consortium for Mathematics in Industry (ECMI). His recent work includes modeling the glymphatic system and cerebrospinal fluid dynamics in the brain, contributing to neuroscience and medical research. The most recent publications reflect a shift toward interdisciplinary applications, especially in neuroscience, fluid dynamics, and environmental modeling , while maintaining a strong foundation in pure and applied mathematics. Themes include brain efflux, phytoplankton dynamics, and philosophical explorations of probability and cosmology. DTU Teaching Award (2010) Hjorth has supervised students and led multiple long-term research projects since the 1990s. He serves as Editor for the Danish Mathematical Society Newsletter Matilde and the Fields Institute journal Mathematics in Industry Case Studies . His collaborative network spans institutions in Denmark, Israel, the UK, and the USA, reflecting his international engagement in both research and academic service.

• Dynamical Systems
• Mathematical Modelling
• Industrial Applied Mathematics
• +۶ مورد دیگر

Michael Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His primary research focuses on statistical inference for stochastic processes, particularly stochastic differential equations and jump processes, with applications in finance, physics (e.g., wind-blown sand dynamics), and biology. He has authored/co-authored influential books such as Exponential Families of Stochastic Processes and edited volumes on empirical process techniques and statistical methods for stochastic differential equations. His work bridges theoretical statistics with applied problems in natural sciences and finance. Research interests include modeling turbulence, sand transport dynamics, and protein structure evolution. Collaborations with earth scientists like Keld Rømer Rasmussen have advanced understanding of aeolian processes. His methodologies emphasize likelihood-based inference and estimating functions, with contributions to high-frequency data analysis and diffusion bridge simulations. A comprehensive CV and full publication list are available on his profile. Key contributions span stochastic modeling in physics (e.g., sand dune dynamics), financial econometrics, and computational statistics. He has pioneered techniques for analyzing multi-modal diffusions and developed frameworks for mixed-effects stochastic differential equations. His work is widely cited in both theoretical and applied statistical literature.

• Statistical Inference for Stochastic Processes
• Stochastic Differential Equations
• Financial Mathematics
• +۳ مورد دیگر

Tue Herlau is an Associate Professor at the Department of Applied Mathematics and Computer Science, Cognitive Systems, within the College of Engineering at Technical University of Denmark. His research focuses on modeling complex networks using Bayesian methods, particularly applied to brain imaging data. He explores multiscale structures in networks (ranging from individual nodes to large-scale temporal dynamics) and causal inference in social and biological systems. Education : Bachelor in Physics and Mathematics (University of Copenhagen), Masters in Informatics (DTU) Recent publications highlight work on generative AI for educational psychology, causal probability trees, Bayesian neural network training, and moral reinforcement learning. Key collaborations span institutions in Denmark and international partners in AI and neuroscience research. He supervises students in probabilistic methods and machine learning applications, including a 2019-2020 Master project on prognostics for carbon/epoxy composites. His network shows strong engagement with computational science, relational databases, and formal concept analysis.

• Bayesian Methods
• Complex Networks
• Brain Imaging Data Analysis
• +۳ مورد دیگر

Sara Merino-Aceituno is an Associate Professor at the Faculty of Mathematics, University of Vienna, with prior academic roles at the University of Sussex and Imperial College London. Her research focuses on kinetic theory and mathematical modeling of emergent phenomena in biological, medical, and social systems. PhD in Mathematics, University of Cambridge (2015) MSc in Computer Science and Applied Mathematics, INP Grenoble (2010) BSc in Mathematics, Universitat Politècnica de Catalunya (2009) Her research interests center on understanding how macroscopic patterns arise from microscopic interactions, using tools from partial differential equations, probability, and numerical analysis. She specializes in interacting particle systems, collective dynamics, opinion formation, and cell tissue development. She collaborates closely with experimental biologists to validate and refine her models. The recent publications reflect a consistent focus on kinetic modeling of collective behavior, phase transitions, and multiscale analysis. Her work bridges abstract mathematical theory with concrete applications in biology and social sciences, often involving collaboration with interdisciplinary teams. She develops continuum limits of particle systems and investigates stability, alignment, and pattern formation in complex systems. Sara is actively involved in mentoring and science communication. She has co-authored study guides for students, leads outreach initiatives such as exhibitions at the 'Long Night of Research,' and produces educational videos. She also maintains a blog and YouTube channel to share insights on research, learning, and academic life. Co-authored guide: 'GOOD_STUDY_HABITS.pdf' with Amalio Fernández-Pacheco Created educational video 'Describing Patterns' with filmmaker Sameer Patel Regular contributor to public science events Active blogger on topics including coaching, creativity, and academic mindset She leads a research group called 'The HERD,' which investigates emergence in natural domains, focusing on mathematical models of biological and social systems. Her team includes postdoctoral researchers and students working on kinetic theory, numerical simulations, and interdisciplinary modeling projects.

• Kinetic Theory
• Emergent Phenomena
• Mathematical Biology
• +۱۲ مورد دیگر

Orimar Sauri Arregui is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, Denmark. His research lies at the intersection of mathematical statistics, stochastic processes, and financial modeling. Research Interests: His work focuses on ambit fields , trawl processes , Lévy and infinite divisible random fields , and nonparametric estimation in continuous time. He investigates asymptotic behavior, limit theorems, and statistical inference for complex stochastic models, with applications in financial market microstructure and energy flux modeling. The analysis of his recent publications reveals a strong trend in theoretical statistics and probability, particularly in developing and analyzing models driven by non-Gaussian noise and long-range dependence. His work often involves high-frequency data and contributes to the foundations of spatiotemporal modeling. Scientific Contributions: Developed mathematical frameworks for financial market microstructure. Advanced theory for nonparametric estimation of trawl processes. Derived asymptotic error distributions for numerical schemes in stochastic delay equations. Proved local limit theorems for energy fluxes in random fields. Advising and Research Activity: He has been involved in PhD supervision and maintains an active research output, primarily through preprints on arXiv and SSRN. His collaborations span topics in financial econometrics and statistical physics. Though specific grants are not listed, his consistent publication record suggests ongoing research funding. Laboratory and Teams: While no formal lab is mentioned, his work is part of the broader research network in mathematical statistics and financial mathematics at Aalborg University, with notable collaborations in stochastic modeling and econometrics.

• Mathematical Statistics
• Stochastic Processes
• Nonparametric Estimation
• +۷ مورد دیگر

Konstantin Pavlikov is an Associate Professor at the Department of Business & Management (DBM) under Strategic Organization Design (SOD) at the University of Southern Denmark. His research focuses on Operations Research, Integer Programming, and Stochastic Programming , with particular emphasis on vehicle routing optimization and network flow modeling. Education : PhD in Operations Research (University of Florida, 2014), MSc in Applied Mathematics (Moscow State University, 2007) His work spans combinatorial optimization and network interdiction problems , developing exact and approximate solution algorithms for complex logistics challenges. Recent publications analyze heterogeneous vehicle routing capacity inequalities and two-commodity flow formulations for routing problems. Scientific contributions have been recognized with the Best Reviewer Award (2019). He actively reviews for journals like Computational Management Science and supervises academic works through examination roles.

• Operations Research
• Stochastic Programming
• Integer Programming
• +۳ مورد دیگر

Anne Marie Svane is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, specializing in topological data analysis, stochastic geometry, and probability theory. Her research bridges theoretical mathematics with applied statistics, focusing on geometric functionals and computational methods. Education: PhD in Mathematics from Aarhus University. Her recent work explores cobordism obstructions, Gibbs processes, and applications in climate science and medical research. Key contributions include: Advancing kernel persistence methods for topological data analysis. Developing central limit theorems for spatial point processes. Modeling moisture dynamics in building physics with climate data. Investigating the interplay between digital algorithms and geometric structures. She collaborates on interdisciplinary projects like AI-Aalborg Intelligence and contributes to educational initiatives such as Girls' Day in Science.

• Topological Data Analysis
• Probability Theory
• Mathematical Statistics
• +۳ مورد دیگر

Kasper Studsgaard Sørensen is a Research Fellow in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His work bridges pure mathematics and applied engineering through interdisciplinary collaborations. His research spans Probability Theory , Mathematical Physics , and Dynamical Systems , with notable contributions to: Spectral analysis of Dirac-Landau operators Acoustic black hole optimization Distribution theory for stationary random variables Asymptotics of greedy β-expansions Publication trends since 2019 reveal dual focus areas: (1) rigorous mathematical physics (8 Scopus citations) and (2) applied acoustics engineering (4 citations). His fingerprint shows dominant expertise in Integer Mathematics (100%), Cumulative Distribution Functions (66%), and Black Hole theory (66%). Collaborations include H.D. Cornean (repeated co-author), M. Moscolari, and S. Sorokin across international institutions. His ORCID profile (0000-0003-4121-9888) documents 9 peer-reviewed publications including journal articles and preprints.

• Mathematics
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Dr. Yoshiharu Maeno is a Professor at the School of Interdisciplinary Mathematical Sciences, Meiji University, where he conducts interdisciplinary research at the intersection of mathematics, data science, and social systems. His work applies advanced mathematical frameworks to real-world complex phenomena in economics, finance, epidemiology, and social networks. Position: Professor Institution: Meiji University School: School of Interdisciplinary Mathematical Sciences Research Interests Dr. Maeno's research focuses on data science and engineering, complex systems theory, and stochastic process theory applied to economics and social sciences. He specializes in modeling reaction-diffusion processes to understand the dynamics of infectious disease spread, financial crises, and misinformation dissemination on social media. His work bridges theoretical mathematics with practical applications in societal challenges. Publication Trends His recent scholarly output demonstrates a consistent focus on applying reaction-diffusion and stochastic models to complex social and economic systems. The publications span interdisciplinary domains including econophysics, computational social science, and mathematical epidemiology, showing a strong trend toward data-driven modeling of real-world dynamic processes. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants While specific students or grant details are not listed, Dr. Maeno's active research program suggests involvement in mentoring graduate students and securing research funding. His participation in events like the Econophysics Colloquium and CSH Workshop indicates collaboration within the global complexity science community. Labs and Teams Dr. Maeno is associated with research initiatives focused on complex systems and data science, likely involving collaborative teams working on interdisciplinary projects. His involvement in workshops and colloquia suggests membership in international research networks studying complexity in economic and social systems.

• Data Science and Engineering
• Complex Systems Theory
• Stochastic Process Theory
• +۷ مورد دیگر

Jesper Lützen is an academic historian of mathematics at the Department of Mathematical Sciences , University of Copenhagen . He is renowned for his expertise in the history of mathematics, particularly focusing on the period 1800–1950, analysis, and mechanics. His current status as a Part-time Lecturer reflects continued engagement despite his emeritus title. Born : October 8, 1951, in Svendborg Education : Student exam (mathematical-physical line), Svendborg State School (1970) B.Sc. in Natural Sciences (mathematics major, physics minor), Aarhus University (1976) Licentiate (Ph.D.) in History of Science, Aarhus University (1980) Doctor of Science, University of Copenhagen (1990) Research Interests : Lützen specializes in the history of mathematical impossibility theorems, ranging from ancient Greek antiquity to modern times. His work spans analysis, mechanics, and the philosophical underpinnings of mathematical concepts. He has extensively explored duality, distributions, and the legacy of figures like Hjelmslev and Juel. Academic Engagement : His 15 most recent publications (2000–2024) reveal a consistent focus on mathematical history, methodology, and foundational debates. Key themes include distribution theory, geometric philosophies, and historical critiques of mathematical rigor. Scientific Awards : Fyns Stiftstidende’s Forskerpris (1982) Børge Jessens Diplom for godt foredrag (1988) Det Naturvidenskabelige Fakultets Formidlingspris (2000) Det Naturvidenskabelige Fakultets Undervisningspris (2003) Editorial and Institutional Roles : Lützen has served on editorial boards for Archive for the History of Exact Sciences and Historia Mathematica , and as a committee member in various academic and outreach initiatives. His outreach efforts include lectures for high school teachers, students, and contributions to the Great Danish Encyclopedia .

• History of Mathematics
• History of Analysis
• History of Mechanics
• +۲ مورد دیگر

Thomas Valentin Mikosch is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on extreme value theory, stochastic processes, and time series analysis with applications in actuarial mathematics and risk management. Extreme value theory for dependent data Heavy-tailed distributions in financial and insurance contexts Statistical inference for stochastic models Recent work includes publications on power-law tails in time series, cluster inference for extremal events, and distance covariance in spatial models. He has served as an editor for journals like Bernoulli and Stochastic Processes and Their Applications and holds memberships in prestigious organizations such as the Royal Danish Academy for Sciences and Letters.

• Extreme Value Theory
• Stochastic Processes
• Time Series Analysis
• +۳ مورد دیگر

Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +۷ مورد دیگر

Ricardo Jose Gabrielli Barreto Campello is a full Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), Faculty of Science and Engineering. He holds a prestigious International Recruit Fellowship from the Novo Nordisk Foundation (2023–2028) and leads advanced research in data mining and machine learning. He has held prior full professorships at the University of Newcastle and James Cook University, and adjunct or visiting roles at the University of Alberta, University of Melbourne, and University of São Paulo. Education: PhD in Electrical and Computer Engineering, University of Campinas (2002) Master in Electrical and Computer Engineering, University of Campinas (1997) Bachelor in Electronics Engineering, State University of São Paulo (Unesp) (1994) His research centers on data mining and machine/statistical learning , with a focus on developing general-purpose algorithms for descriptive and predictive analytics. He emphasizes unsupervised and semi-supervised learning , particularly in clustering, outlier detection, and intrinsic dimensionality estimation . His work bridges conceptual innovation with real-world applications, including gene-expression data analysis under his current NNF-funded project. The recent publications reflect a strong trend in unsupervised and probabilistic methods , particularly in outlier detection, clustering evaluation, and local intrinsic dimensionality. These works appear in high-impact journals and conferences such as Information Systems , Applied Energy , and SIAM SDM, often in collaboration with leading researchers like Arthur Zimek. The research demonstrates a deep integration of statistical rigor and algorithmic innovation. Scientific Awards: Best Research Paper Award, SIAM SDM (2024) 10-year Test of Time Award, PAKDD (2023) International Recruit Fellow, Novo Nordisk Foundation (2023) Runner-Up Best Paper, IEEE Transactions on Big Data (2022) Top 2% Scientist in the World (2020) Ricardo has consistently secured external research funding, including major grants from the Novo Nordisk Foundation and Danish Council for Independent Research. He has successfully advised numerous postgraduate students and led interdisciplinary research teams. His current projects include Advanced Computational Methods for Unsupervised Data Mining and collaborations on Clustering Evaluation Revisited and Reliable Outlier Detection , all based at SDU. He is actively involved in research networks and collaborations across Australia, Canada, and Europe, and maintains affiliations with institutions such as the University of Melbourne and James Cook University. His work continues to shape methodological advances in data science, particularly in foundational areas with broad application potential.

• Data Science
• Data Mining
• Machine Learning
• +۱۳ مورد دیگر