جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Remote Sensing
• +۳ مورد دیگر

Fabian Christian Gieseke is an Associate Professor in the Department of Computer Science at the University of Copenhagen, specializing in Machine Learning. His research focuses on developing advanced computational methods for environmental monitoring and large-scale data analysis. Primary research interests include: Application of deep learning to remote sensing and geospatial data Large-scale forest biomass estimation using LiDAR and satellite imagery National-scale ecological monitoring systems Efficient algorithms for processing massive environmental datasets His recent publications demonstrate a strong emphasis on applying machine learning to climate science and ecological conservation. Work frequently involves: Cross-disciplinary collaborations with environmental scientists Development of novel deep learning architectures for 3D data Large-scale mapping of natural resources Solutions for global environmental challenges

Cedomir Stefanovicمشاهده پروفایل

استاد

• Wireless Communication
• 5G/6G Networks
• Internet of Things (IoT)
• +۷ مورد دیگر

Cedomir Stefanovic is a Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design. His research focuses on edge computing, wireless networking, 5G/6G technologies, IoT, smart grids, and bionic limbs. He leads projects like CLIMB (Cloud-connected bionic limbs) and 5G-connected assistive robots, emphasizing assistive robotics and medical technology integration. His work spans random access protocols, latency optimization, and energy-efficient communication systems. With 129 publications (2025: 12; 2024: 8; 2023: 12), his research addresses challenges in industrial IoT, smart grids, and UAV-assisted communication. He has supervised 3 PhD students and actively collaborates internationally. Notable contributions include advancements in deterministic multiple access patterns, AoI-aware scheduling, and bionic limb connectivity. His research is published in IEEE journals and conferences. He hosts guest lecturers and has been featured in media discussions on wireless innovation and smart energy systems. Education: While specific academic credentials are not detailed, his role as a Professor at Aalborg University indicates advanced degrees in electrical engineering or computer science. Research Interests: 5G/6G and IoT networking Edge computing and latency-critical systems Wireless communication protocols (e.g., tree algorithms, NOMA) Smart grid optimization and microgrids Bionic limbs and assistive robotics Age of Information (AoI) minimization strategies Publications: Over 129 papers (2025:12; 2024:8; 2023:12), emphasizing trends in 5G-enabled prosthetics, deterministic access patterns, and industrial IoT optimization. Recent work explores UAV-assisted sensing, D2D communication for smart grids, and deep learning for industrial systems. Funding & Projects: CLIMB (2022–): PI for cloud-connected bionic limbs WindMill (2019–): Machine learning for windmill communication INCOMING (2020–2022): Massive-scale communication innovation Labs/Teams: Edge Computing and Networking group, Classique-Center for Classical Communication in the Quantum Era.

Israel Leyva Mayorgaمشاهده پروفایل

دانشیار

• Satellite Communications
• Non-Terrestrial Networks (NTNs)
• Internet of Things (IoT)
• +۷ مورد دیگر

Israel Leyva Mayorga is an Associate Professor at Aalborg University within the Department of Electronic Systems , part of the Technical Faculty of IT and Design. His research focuses on satellite communications, non-terrestrial networks (NTNs), and IoT connectivity in 5G/6G systems. Key research interests include LEO satellite constellations , random access protocols , energy-efficient wireless systems , and edge computing . He leads or collaborates on projects like SATNEX V WI Y4.6 (ESA) and WITS: Wireless access for heterogeneous IoT Services , addressing challenges in mesh satellite communications and IoT service integration. Awarded the Novo Nordisk Foundation's Prize for Excellence in Technical Science Teaching (2025) and Outstanding PhD thesis (2020) , his work bridges academia and industry, with media coverage on topics like Arctic connectivity and satellite mega-constellations. He serves as an editor for IEEE Wireless Communications Letters and is a member of the one6G consortium. His research outputs span 58 publications , emphasizing satellite routing optimization, IoT traffic management, and energy-autonomous nodes in vehicular networks. Current projects explore semantic image encoding for Earth observation and reconfigurable intelligent surfaces in wireless access.

Anne Marie Svaneمشاهده پروفایل

دانشیار

• Topological Data Analysis
• Probability Theory
• Mathematical Statistics
• +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.

Yurij Holovatchمشاهده پروفایل

استاد

• Statistical Physics
• Phase Transitions and Critical Phenomena
• Complex Systems
• +۱۳ مورد دیگر

Yurij Holovatch is a Professor and Chief Researcher at the Institute for Condensed Matter Physics (ICMP) of the National Academy of Sciences of Ukraine in Lviv, where he founded the Laboratory for Statistical Physics of Complex Systems. He is a co-founder and co-director of the L4 collaboration and the International Doctoral College in Statistical Physics of Complex Systems, linking ICMP with the Universities of Leipzig (Germany), Coventry (UK), and Lorraine (France). He is also a full member of the National Academy of Sciences of Ukraine. Research Interests: His work focuses on phase transitions and critical phenomena in structurally disordered magnets, scaling of macromolecules and conformational properties of complex polymers, complex networks (ordering, stability, spreading), and increasingly extends into digital humanities and human migration . His research bridges theoretical physics with data-driven modeling of social and urban systems. Publication Trends: His recent publications (2023–2024) reveal a sustained focus on critical behavior in disordered systems using Monte Carlo simulations, exact and asymptotic analysis of models like the Potts and Blume-Capel models, and innovative applications of statistical physics to collective decision-making, transportation networks, and migration. These works reflect a strong interdisciplinary trend, integrating physics-based modeling with data analytics and social science questions. Davydov Prize for studies in theoretical and biological physics (2020) Honorary Ambassador of Lviv (2020) Visiting Professor (Honorary), Coventry University (since 2018) Advising and Grants: As co-director of the International Doctoral College, he plays a central role in training PhD students in statistical physics across Ukraine, Germany, UK, and France. He organizes the annual Ising Lectures workshop in Lviv since 1997, fostering international collaboration. He serves as editor of the book series Order, Disorder and Criticality (World Scientific), with Volume 7 published in 2023. Labs and Teams: He founded and leads the Laboratory for Statistical Physics of Complex Systems at ICMP Lviv. He co-directs the L4 collaboration and the International Doctoral College, which function as cross-institutional research and educational networks integrating theoretical and computational physics across Europe.

Yoshiharu Maenoمشاهده پروفایل

استاد

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

Afsaneh M. Nejadمشاهده پروفایل

استادیار

• Data Science
• Bioinformatics
• Biostatistics
• +۱۲ مورد دیگر

Afsaneh M. Nejad is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, where she conducts research at the intersection of data science, bioinformatics, and biomedical research. Her work leverages large-scale omics data and twin studies to investigate aging, hypertension, cancer, and cardiovascular disease. Assistant Professor, Department of Mathematics and Computer Science Specialization: Data Science, Bioinformatics, Biostatistics Institution: University of Southern Denmark (SDU) Her research focuses on epigenomics, transcriptomics, and proteomics, particularly in longitudinal twin cohorts. She applies advanced statistical and machine learning techniques to identify biomarkers of disease and aging. Key areas include DNA methylation, gene expression, and multi-omics integration in conditions such as hypertension, Alzheimer’s, diabetes, and myocardial infarction. The recent publications highlight a strong trend in using twin-based designs to control for genetic background while studying environmental and lifestyle impacts on health. Her work spans clinical applications, predictive modeling, and mixed-methods public health research, particularly in sports medicine and chronic disease. She serves as a peer reviewer for journals including BMC Medicine , Clinical Epigenetics , Human Genomics , and Scientific Reports . Her teaching includes courses in bioinformatics and biostatistics, indicating active mentorship and curriculum development. DM847: Introduction to Bioinformatics Applied Biostatistics Evidence-based Drug Use and Biostatistics She has contributed to significant collaborative studies such as the DREAM Trial and has presented at conferences on topics like cancer and frailty in aging. Her methodological expertise in longitudinal data analysis, omics integration, and machine learning positions her at the forefront of computational biomedicine.

• Algorithmics
• Online Algorithms
• Graph Algorithms
• +۷ مورد دیگر

Lene Monrad Favrholdt is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), Faculty of Science. Her research is centered on algorithms, particularly online algorithms, graph algorithms, and combinatorial optimization. She is an active contributor to theoretical computer science with a focus on algorithmic performance under uncertainty and with predictions. Her research interests include: Online Algorithms and Competitive Analysis Algorithm Design with Predictions Graph Algorithms (e.g., edge coloring, minimum spanning trees) Combinatorial Optimization (e.g., bin packing, knapsack problems) Resource Allocation and Scheduling The recent trend in her publications shows a strong emphasis on integrating machine learning predictions into classical online algorithms to improve competitive ratios and practical performance. Her work spans theoretical analysis and experimental evaluation, appearing in top venues such as Algorithmica, ICML, and WADS. Her scientific achievements include receiving SDU's teaching award in 2022. She has served as an editor and peer reviewer for major algorithmic conferences, including the European Symposium on Algorithms (ESA) and Scandinavian Symposium on Algorithm Theory (SWAT). SDU's teaching award Lene Monrad Favrholdt has been involved in teaching courses such as Logic and Linear Algebra and has contributed to PhD supervision. She is actively engaged in research collaboration and academic service, including participation in workshops and conferences on new techniques in online algorithms. While specific grants are not mentioned, her extensive publication record and academic activities indicate sustained research funding and collaboration.

Markus Kiderlenمشاهده پروفایل

دانشیار

• Integral Geometry
• Convex Geometry
• Stochastic Geometry
• +۲ مورد دیگر

Markus Kiderlen is an Associate Professor at the Department of Mathematics, Aarhus University, specializing in the intersection of geometry, probability theory, and statistics. His research spans integral geometry, convex geometry, stochastic geometry, geometric tomography, and spatial statistics. Primary affiliation: Department of Mathematics, Aarhus University Academic rank: Associate Professor His work in stochastic geometry involves integral geometric relations, Minkowski tensors for quantifying random particles, and uniqueness/inference problems in geometric tomography (e.g., solving Hammer's X-ray problem). Another key focus is spatial sampling methods, including Wicksell's corpuscle problem and Cavalieri volume estimation improvements. Recent research trends include geometric reconstruction algorithms, stereological techniques for nonconvex particles, and rotational integral formulae for convex bodies. He has also explored polarizations in rearrangement processes and their applications. Markus Kiderlen has presented at international conferences such as MCQMC (2022), Stochastic Geometry Workshops (2019-2022), and the WIASABISS workshop (2018). His publications reflect collaborations with researchers like M. Stehr, R. Eriksen, and D. Hug.

Ioana O. Berceaمشاهده پروفایل

استادیار

• Data Structures
• Randomized Algorithms
• Approximation Algorithms
• +۱ مورد دیگر

Ioana O. Bercea is a tenure-track Assistant Professor in the Computer Science Department at KTH Royal Institute of Technology, affiliated with the Division of Theoretical Computer Science. She holds a PhD in Computer Science from the University of Maryland (advised by Samir Khuller), a Master's from the same institution (advised by Aravind Srinivasan), and a BS in Mathematics and Computer Science from the University of Chicago. Her research focuses on Data Structures (dictionaries, Bloom filters), Randomized Algorithms (hashing, clustering), and Computational Geometry (Traveling Salesman Problem). Her recent publications emphasize scalable data structures, dynamic filters, and algorithmic efficiency, with applications in database systems and high-dimensional optimization. Awards & Honors: Best Artifact of ACM SIGMOD 2023 Future Digileader (Digital Futures, 2023) VR Starting Grant (2024) She advises PhD student Jonas Østergaard Klausen (University of Copenhagen) and collaborates with research groups including BARC (Copenhagen) and Digital Futures. Her VR Starting Grant funds the project "DataTech: Techniques for scalable data storage and analysis."

Christophe Biscioمشاهده پروفایل

دانشیار

• Point Processes
• Statistics
• Mathematics
• +۳ مورد دیگر

Christophe Biscio is an Associate Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research focuses on spatial statistics, point processes, and topological data analysis, with applications in materials science and engineering. He leads interdisciplinary projects like 'Deciphering Nanoporosity of Amorphous Materials using Topological Data Analysis,' exploring medium-range order structures in glasses and metal-organic frameworks. Key research interests include statistical learning for point processes, functional central limit theorems, and adversarial machine learning. His work bridges theoretical mathematics with practical challenges in wireless communication, radar sensing, and urban system modeling. Collaborations span materials scientists, computer scientists, and data analysts. Biscio has advised one PhD student and contributed to datasets on topological summaries for spatial processes. He actively organizes conferences on data science and participates in public outreach events like Earth Day climate change discussions.

Deena Francisمشاهده پروفایل

استادیار

• Digital Twins
• Machine Learning
• Smart Manufacturing
• +۳ مورد دیگر

Deena Francis is an Assistant Professor at the Department of Engineering Technology and Didactics within the Energy Technology and Computer Science school at the Technical University of Denmark (DTU). Her research focuses on digital twins, machine learning applications in smart manufacturing and precision livestock farming, and sensor technology for chemical detection. She leads and participates in high-impact projects such as SEAL (Seabed Environmental Analyzer for Methane Leakage) and Machine Learning for Beehives Monitoring , addressing environmental and agricultural sustainability challenges. Key research areas include reinforcement learning for digital twin systems, dimensionality reduction techniques, and data-driven decision support in agriculture. Her work bridges theoretical advancements in kernel methods and explicit feature maps with practical applications in manufacturing optimization and environmental sensing. Francis has collaborated internationally and contributed to over 15 peer-reviewed publications, with a focus on smart systems and sensor-based solutions. Education: Not explicitly stated in the text. Labs/Teams: Involved in the InnoTech TaskForce , focusing on green transition technologies and data-driven innovation. Her advisory role includes supervising PhD student S. Gopalakrishnan on metal-organic framework arrays for liquid-phase sensing. Francis’s projects often emphasize interdisciplinary collaboration, combining engineering, computer science, and environmental science to address global challenges.

Ju Fengمشاهده پروفایل

پژوهشگر

• Wind Energy Systems
• Floating Offshore Wind Farms
• Wind Farm Optimization
• +۶ مورد دیگر

Ju Feng is a Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), specializing in Wind Energy Systems , Wind Farm Optimization , and Control Strategies . He contributes to research in Floating Offshore Wind Farms , Multi-Disciplinary Optimization , and Noise Propagation . His work includes developing simulation tools, wake models, and control frameworks for wind farms in complex environments. Research Interests: Optimization of wind farm design and operation Advanced wake and flow modeling AI-driven wind energy solutions Noise control and acoustic propagation Multi-fidelity/multi-disciplinary modeling Cost-effective wind farm technologies Publication Trends: Ju Feng's recent work (2022-2024) focuses on floating offshore wind farms , noise constraints , and multi-disciplinary integration . Older articles (2012-2017) emphasize control algorithms , wake modeling , and optimization frameworks . Advising: Supervising PhD Student Wang R. (AI-Augmented Wind Farm Design Optimization, 2025-2028) Co-supervising Nyborg C. M. (Noise Control in Wind Farms, 2019-2023) Collaboration Network: Active in international projects like DecoWind, ModFarm, and FarmConners. Collaborates with institutions such as DTU Wind Energy and researchers including Shen W. Z. , Réthoré P.-E. , and Bredmose H. .

Peter Reinhard Hansenمشاهده پروفایل

پژوهشگر

• Econometrics
• Financial Mathematics
• Quantitative Finance
• +۳ مورد دیگر

Peter Reinhard Hansen is a visiting researcher at the Copenhagen Business School , affiliated with the Department of Finance . His work intersects econometrics, statistical modeling, and financial mathematics, contributing to global sustainable development goals through quantitative research methodologies.

Kim Knudsenمشاهده پروفایل

استاد

• Inverse Problems
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Kim Knudsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His research focuses on inverse problems for partial differential equations, numerical computations, and tomography. He leads projects in hybrid inverse problems and acousto-electric tomography. PhD in Mathematics, Aalborg University (2002) Cand.Scient. in Mathematics and Computer Science, Aalborg University (1999) Postdoc at University of Washington, Seattle (2004-2005) His work spans mathematical modeling, uncertainty quantification, and imaging techniques like electrical impedance tomography. Recent projects address regularization methods, level-set algorithms, and hybrid data tomography. Kim Knudsen supervises PhD students including Asmund Rasmussen, Henrik Schlüter, and Astrid Kirkeby. Collaborations include Tarvainen, Garnier, and Adesokan. Publications highlight Bayesian approaches, spatial regularization, and tomography advancements.