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نمایش ۱۶-۳۰ از ۱۵۵ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Claus Brøndgaard Madsenمشاهده پروفایل

دانشیار

Aalborg UniversityDenmark

Claus Brøndgaard Madsen is an Associate Professor in the Department of Architecture, Design and Media Technology at Aalborg University, under The Technical Faculty of IT and Design. He is based at Campus Aalborg and is affiliated with the Computer Graphics Group and VR Light research environment. His work bridges computer vision, virtual reality, and architectural design, with a strong focus on real-time visualization and human interaction in digital environments. Research Interests: His primary research areas include Computer Vision , Virtual Reality (VR) , Augmented Reality (AR) , 3D Reconstruction , Structure-from-Motion (SfM) , and Image-Based Lighting . These are applied in industrial VR, urban design evaluation, and sensory augmentation systems. His research fingerprint highlights strong engagement with Virtual Reality (100%), Augmented Reality (71%), Illumination Conditions (41%), and Wind Turbine Engineering (28%). The recent publications (2023–2025) reflect a consistent trend in enhancing user experience in VR/AR environments, with emphasis on spatial cognition, locomotion techniques, exposure control, and machine learning integration for lighting classification and part direction prediction in industrial contexts. These works are published in top-tier conferences such as EG, GRAPP, WACV, and CAADRIA. Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: Madsen has supervised PhD students and served as a supervisor in research projects like AAIVR. He is Principal Investigator (PI) in multiple funded projects, including BUMUS: Sustainable Expansion of Mussel Production (2024–2027), CityVR: Evaluating Parametric City Designs in VR (2020–2022), and V-BRT: Virtual Bus Rapid Transit (2019). These projects reflect substantial grant acquisition and leadership in applied VR research. Labs and Research Groups: He is a key member of the Computer Graphics Group and VR Light at Aalborg University, which focus on real-time visualization, lighting techniques, and immersive technologies for industrial and architectural applications.

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Eigil Kaasمشاهده پروفایل

استاد

University of CopenhagenDenmark

Professor Eigil Kaas is affiliated with the Niels Bohr Institute at the University of Copenhagen . His work spans climate dynamics , numerical weather prediction (NWP) , and atmospheric modeling . As former Section Head of Climate and Computational Geophysics , he leads research on climate-chemistry coupling and sea ice impacts. Education : MSc (1987) and PhD (1993) in Meteorology from University of Copenhagen Research Focus : Climate dynamics and physics Numerical methods in atmospheric models Machine learning for weather prediction Arctic sea ice-climate interactions Thunderstorm electricity and radiation Coupled atmosphere-ocean modeling Article Trends : Recent work combines neural networks with radiative transfer optimization Focus on storm dynamics and gamma-ray flashes Extreme precipitation modeling under climate change Pioneering tidal flow studies in Faroe Island fjords Teaching Legacy : Instructor of Atmospheric Physics and Dynamical Meteorology courses Developed zonally averaged climate model for educational use Mentored 12 PhD/MSc students with DMI/ECMWF collaborations Professional Roles : Chairman of BFI Group 28 (Geosciences & Climate) Scientific Advisory Committee member at ECMWF Project lead in EU ENSEMBLES and PEGASOS initiatives

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Jesper Møllerمشاهده پروفایل

استاد

Aalborg UniversityDenmark

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research focuses on advanced statistical methodologies with applications across various scientific domains. His educational background includes extensive training in mathematical sciences, though specific degree details aren't provided in the current materials. His research interests span: Applied probability theory Markov chain Monte Carlo methods (MCMC) Spatial statistics Stochastic geometry Stochastic simulation Point process modeling Professor Møller's recent publication record shows consistent productivity with 239 research outputs including journal articles, reports, and book chapters. His work demonstrates strong focus on spatial point processes, Bayesian inference methods, and applications of stochastic geometry. The research trends indicate increasing sophistication in modeling complex spatial patterns and developing computational methods for statistical inference. His scientific contributions have been supported by numerous research projects, with 28 projects documented including the current "Peculiar Distribution Functions and Interesting Stochastic Processes" (2022-2026). His work has generated significant scholarly impact with citations across multiple disciplines. Professor Møller has supervised 8 PhD students and maintains active collaborations across international research networks. His current projects suggest continued research activity in developing novel statistical methodologies for complex spatial data analysis with applications in materials science, neuroscience, and environmental statistics.

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Jeffrey F. Collamoreمشاهده پروفایل

استاد

University of CopenhagenDenmark

Jeffrey F. Collamore is a full Professor at the Department of Mathematical Sciences , University of Copenhagen, specializing in Insurance and Economics (work area: IE). His research focuses on advanced probability theory and its applications in insurance mathematics and quantitative risk management. University of Copenhagen (2002-present) ETH Zurich (2000-2002) EURANDOM (1999-2000) Lund University (1998-1999) University of Illinois (1996-1998) Research Focus : Large deviations and rare event estimation Stochastic fixed point equations and random matrices Harris recurrent Markov chains Multidimensional ruin problems in insurance Stochastic simulation techniques Education : Ph.D. and MA in Mathematics, University of Wisconsin, Madison (Advisor: Peter Ney) BS in Physics and Mathematics, University of California, San Diego

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Irena Vodenskaمشاهده پروفایل

استاد

University of CopenhagenDenmark

Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.

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Julie Marxمشاهده پروفایل

استادیار

Copenhagen Business SchoolDenmark

Dr. Julie Marx is an Assistant Professor in the Department of Finance at Copenhagen Business School (CBS), affiliated with the Center for Big Data in Finance (BIGFI). She holds a PhD from CBS and specializes in real estate economics, behavioral finance, and decision-making processes in housing markets. Her research integrates experimental methods with empirical analysis to investigate topics such as reference dependence, negotiation behavior, and household financial decisions. Her work has been published in top journals like the American Economic Review and Journal of Real Estate Finance and Economics . Notable contributions include studies on gender differences in real estate negotiations and how market downturns influence homeownership decisions. She has also contributed to working papers with institutions like NBER and CEPR. Dr. Marx's research emphasizes bridging theoretical models with real-world market behaviors. She is involved in the research project Risky Decisions: Revealing Economic Behavior and maintains an active academic profile through presentations at conferences like the European Finance Association Annual Meeting.

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Martin Skovgaard Andersenمشاهده پروفایل

دانشیار

Technical University of DenmarkDenmark

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.

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Jonas L. Juulمشاهده پروفایل

استادیار

IT University of CopenhagenDenmark

Jonas L. Juul is an Assistant Professor in the Computer Science Department at the IT University of Copenhagen . With a background in network science and complex systems, he employs statistical methods, mathematical modeling, and computer simulations to study social networks, spreading processes, and human behavior. Focus areas include: information diffusion in social networks Disease spread mitigation in human populations Interdisciplinary collaboration with medical doctors, economists, and computer scientists Recent research highlights include improving statistical models for pandemic forecasting through the InForM project funded by the Novo Nordisk Foundation , and groundbreaking work on contact tracing optimization and information cascade dynamics. Notable recognitions: 2025 H.C. Ørsted Research Talent Prize 2024 Novo Nordisk Foundation Data Science Emerging Investigator Grant 2025 Young Academy membership He has contributed to mathematical modeling efforts during Denmark's COVID-19 reopening in 2020 and maintains active collaborations with institutions including Cornell University , Technical University of Denmark , and Niels Bohr Institute .

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Sofie Cairoمشاهده پروفایل

استادیار

Copenhagen Business SchoolDenmark

Sofie Cairo is an Assistant Professor at the Department of Strategy and Innovation at Copenhagen Business School and a Postdoctoral Researcher at the Chair for Entrepreneurship at the University of Zürich since September 2024. She holds a PhD in Economics from the University of Copenhagen (2021), with a dissertation focused on social benefit policies and labor market performance based on field experiments conducted with the Danish Ministry of Employment. Her research primarily investigates gender inequalities in science, innovation and leadership, with particular focus on motherhood penalties in academic careers. Cairo has documented that mothers suffer 25 percentage point penalties on annual scientific publication output compared to fathers in the first seven years after childbirth, with 16% lower research publishing participation. Her methodology combines state-of-the-art micro-econometric and machine learning techniques applied to rich datasets merging bibliometric indicators from Scopus with individual-level administrative registers. Cairo's work has been published in top-field journals including the Journal of Public Economics and Academy of Management Proceedings. She has secured significant funding including a Principal Investigator role on a Lundbeck Foundation project and fellowships from the Carlsberg and SparNord Foundations. Her research has important implications for science policy, academic institutions, and gender equality initiatives. She is frequently invited to present at major conferences including the European Economic Association Annual Meeting and organizes professional development workshops at the Academy of Management and DRUID conferences. Cairo serves as the country coordinator in the REGIS network for young innovation scholars and reviews for journals including Labor Economics, Research Policy, and Nature.

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Knud Henriksenمشاهده پروفایل

مدرس پاره‌وقت

University of CopenhagenDenmark

Knud Henriksen serves as a Part-time Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Image Analysis, Computational Modelling and Geometry research section. His academic profile integrates teaching and research in computer graphics, physics-based animation, and computer vision, contributing to DIKU's expertise in visual computing methodologies. His educational background comprises: Elektroingeniør (Electrical Engineering) Cand. Scient (Master of Science) Ph.D. Henriksen's research focuses on Computer Graphics, Physics-Based Animation, Computer Vision, and Mathematics, with current investigations into inverse kinematics using bone representations and quaternions. His work emphasizes improving computational efficiency and realism in character animation through mathematical modeling and geometric algorithms. Analysis of his 30 publications (2002-2008) reveals consistent innovation in animation algorithms, particularly in inverse kinematics, physics simulation, and spline-based modeling. These contributions bridge theoretical computer graphics with practical applications in gaming and virtual reality, demonstrating robust solutions for 3D interaction and character rigging. No scientific awards are documented in the available information. Henriksen has supervised graduate projects including a 2008 thesis on quaternion-based inverse kinematics. While specific grant details are absent, his research aligns with DIKU's interdisciplinary approach. He operates within the Image Analysis, Computational Modelling and Geometry section, which collaborates extensively with industry on visual computing challenges.

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Andreas Møgelmoseمشاهده پروفایل

دانشیار

Aalborg UniversityDenmark

Andreas Møgelmose is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology under the Technical Faculty of IT and Design. His research focuses on computer vision, artificial intelligence, and their applications in autonomous systems, driver assistance, and industrial vision. He leads projects like AI Color Fashion and Real-world adaption of generative AI for architecture. Møgelmose teaches introductory programming, computer vision, and advanced master's courses, emphasizing practical project-based learning. His work includes developing datasets such as the Multi-view Traffic Intersection Dataset (MTID) and exploring vision-language models for autonomous vehicle safety. He actively engages in media discussions on AI ethics and societal impacts. Research interests span dynamic gesture interpretation for cooperative autonomous vehicles, surgical skill assessment via automated metrics, and multimodal classification of environmental data. He has contributed to over 50 publications, including work on 3D object detection frameworks and generative AI education. Møgelmose collaborates on projects funded by industry partners like COWI and Danish government initiatives. His teaching philosophy centers on blended learning and practical application, fostering innovation in AI and computer vision education. Notable projects include AI:Xpertise Lab (2025–present), which explores AI-driven expertise systems, and collaborations on forest biodiversity analysis using LiDAR and orthophotos. His recent media engagements highlight societal AI challenges, emphasizing responsible implementation in public sectors.

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Rikke Gadeمشاهده پروفایل

دانشیار

Aalborg UniversityDenmark

Rikke Gade is an Associate Professor at the Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design at Aalborg University. Her research focuses on Visual Analysis and Perception, AI for the People, and Mobility and Tracking Technologies. She has established herself as a leading researcher in computer vision applications across multiple domains including sports analytics, animal welfare, and human-computer interaction. Dr. Gade earned her PhD in Computer Vision from Aalborg University in 2015 with her dissertation "Taking the Temperature of Sports Arenas - Automatic Analysis of People." Prior to this, she completed her M.Sc. in Informatics with specialization in Vision, Graphics and Interactive Systems in 2011, and her B.Sc. in Electronic and Electrical Engineering in 2009. Her research interests span Computer Vision, Image Processing, Robot Vision, and Thermal Imaging with applications in diverse fields. She has pioneered work in using thermal imaging for sports analytics, developing systems for tracking athletes and analyzing movement patterns in sports arenas. More recently, her research has expanded into AI applications for animal welfare, particularly developing computer vision systems for detecting pain and stress in horses through facial expression analysis. Dr. Gade's publications reveal a clear progression from foundational work in thermal imaging and sports analytics toward more complex applications in healthcare, animal welfare, and smart building systems. Her most recent work shows increasing focus on ethical AI applications that serve societal needs, as evidenced by projects like "AI for the People" and research on equine pain detection. Best Paper Award at Conference CISBAT 2021 (Lausanne, Switzerland) Dr. Gade actively supervises PhD students, including Alves, J.M. on equine affective state assessment. She has secured significant research funding, including a major grant from the Independent Research Fund Denmark for developing AI systems to detect pain in horses. Her collaborative approach is evident in her involvement in multiple interdisciplinary projects spanning computer science, veterinary medicine, and building science. She leads the Visual Analysis and Perception research group at Aalborg University, focusing on practical applications of computer vision technologies. Her team works closely with industry partners on real-world implementations, particularly in sports analytics and animal welfare applications. Current projects include developing AI systems for road damage detection in collaboration with Faxe Kommune and creating advanced tracking systems for sports performance analysis.

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Kiyoshi Kanazawaمشاهده پروفایل

دانشیار

University of CopenhagenDenmark

Kiyoshi Kanazawa is an Associate Professor in the Department of Physics at Kyoto University, where he leads the Interdisciplinary Statphys Lab for Stochastic Process and Econophysics. His research bridges statistical physics with interdisciplinary applications in financial markets, biophysics, and complex systems. He focuses on theoretical and data-driven studies of stochastic processes, particularly non-Markovian dynamics and anomalous diffusion. Research Interests: Kanazawa's work centers on the mathematical modeling of complex systems using stochastic processes. Key areas include non-Markovian processes such as Hawkes processes, econophysics involving financial market microstructure (e.g., Tokyo Stock Exchange), and non-equilibrium statistical mechanics in granular and active matter systems. He develops analytical methods like Markov embedding to transform non-Markovian systems into field-theoretic frameworks for solvability. The recent publications reflect a strong trend in applying statistical physics to real-world data, especially in finance and biological systems. His work combines microscopic modeling with macroscopic prediction , using tools from kinetic theory and agent-based modeling. Articles span topics from Lévy flights in microbial motion to trend-following behavior in traders, showcasing a unifying theme of stochastic dynamics across scales . Scientific Contributions: Pioneered analytical methods for non-Markovian processes using field theory. Provided first-principles derivation of Lévy flights in active matter (published in Nature ). Developed kinetic theory approaches for financial agent-based models (published in PRL ). Established systematic statistical physics framework for non-Gaussian Langevin equations. Academic Guidance: Kanazawa actively mentors graduate students, especially those pursuing PhDs. He emphasizes mastery of stochastic calculus and numerical computation (Python, Julia, C++). Students are encouraged to combine theoretical work with empirical data analysis to strengthen publication potential. His lab promotes open computational practices and journal clubs for collaborative learning. Laboratory: The Interdisciplinary Statphys Lab fosters research in stochastic processes with applications in physics, finance, and biology. It maintains a focus on high-precision financial microdata analysis and theoretical innovation in non-equilibrium systems.

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Sandra Greinerمشاهده پروفایل

استادیار

University of Southern DenmarkDenmark

Sandra Greiner is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where she conducts research in artificial intelligence, cybersecurity, and programming languages. Her work is centered on improving the reliability and adaptability of control software in cyber-physical production systems through advanced software engineering techniques. Her research interests include software variability modeling , design by contract , and the application of generative AI in automating software development tasks. She focuses on ensuring high reliability in software-intensive systems, particularly in industrial automation contexts. Her work bridges theoretical computer science with practical software engineering challenges. The recent publications highlight a strong trend in leveraging AI for software contract generation, enhancing feature tracing with reliable knowledge, and managing variability in complex production systems. Her research contributes to model-driven engineering, software product lines, and intelligent software assistance, with a consistent emphasis on empirical validation through case studies. Sandra actively collaborates with researchers across Europe, particularly in Austria and Germany, contributing to top-tier conferences such as SPLC, MODELS, and GPCE. Her work has been published in high-impact journals and conference proceedings, reflecting active engagement in the global software engineering community. She is affiliated with the IMADA research center at SDU, as evidenced by her institutional email. No formal awards or fellowships are mentioned in the provided data. Sandra advises no publicly listed students, and there is no indication of lab leadership or team management in the text. However, her collaborative publications suggest participation in research teams focused on software product lines and AI-driven software engineering.

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Inger Lise Gadeمشاهده پروفایل

دانشیار بالینی

Aalborg UniversityDenmark

Clinical Associate Professor Inger Lise Gade is a researcher and physician at Aalborg University Hospital, affiliated with the Faculty of Health Sciences and Department of Blood Diseases. With over a decade of clinical and research experience, she bridges hematology practice with innovative diagnostic research, focusing on non-invasive approaches that benefit fragile patients. Department of Blood Diseases, Aalborg University Hospital Clinical Institute, Faculty of Health Sciences Principal Investigator for multiple research projects Leader of Exhaled Breath Diagnostics and Monitoring research group (ExBreDia) Dr. Gade's research centers on exhaled breath proteomics as a novel diagnostic tool, inspired by clinical challenges in hematology. She has demonstrated that exhaled breath contains endogenous proteins reflecting various biological processes, positioning breath analysis as a valuable biological sample comparable to blood but with significant advantages in non-invasive collection and potential for continuous monitoring. Her work spans hematological diseases, coagulation disorders, pulmonary embolism, and cancer diagnostics, with particular emphasis on translating laboratory findings to clinical applications. Analysis of her recent publications reveals a strong trend toward developing non-invasive diagnostic methods using exhaled breath biomarkers. Her research spans hematology, respiratory medicine, and proteomics with applications in pulmonary embolism, stroke, cancer, and infectious diseases like COVID-19. The publications show methodological development progressing toward clinical implementation, with increasing focus on point-of-care diagnostics and monitoring. Clinical Emerging Investigator grant from Novo Nordisk Foundation (2024) Award for medical talent (2020) Award for medical enthusiast (2020) Dr. Gade actively supervises doctoral students and contributes to medical education, teaching master's level medical students with a focus on lymphoproliferative diseases, anemia, and acute hematological conditions. She has secured significant research funding from the Novo Nordisk Foundation and Danmarks Frie Forskningsfond for her innovative work on exhaled breath diagnostics. In 2024, she co-founded the Network for Clinical Lecturers at Aalborg University and serves as chair of its research committee. Dr. Gade leads the Exhaled Breath Diagnostics and Monitoring research group (ExBreDia), established in 2024 with a Clinical Emerging Investigator grant from the Novo Nordisk Foundation. The group focuses on optimizing methods for exhaled breath collection and analysis, investigating potential applications for rapid diagnostics across various medical conditions. Through collaborations with industrial and academic partners, the research aims to transform point-of-care diagnostics and monitoring through non-invasive precision methods.

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