جستجوی استاد راهنمای دانشگاهی

Abayomi Baiyereمشاهده پروفایل

دانشیار

• Digital Transformation
• Information Systems
• Digital Strategy
• +۶ مورد دیگر

Abayomi Baiyere serves as an Associate Professor in the Department of Digitalization at Copenhagen Business School (CBS), where he conducts cutting-edge research at the intersection of digital technologies and organizational transformation. His work significantly contributes to UN Sustainable Development Goals through digitally-enabled societal impact initiatives and has yielded 69 research outputs including high-impact publications in premier journals like Information Systems Journal and Information Systems Research . His research program focuses on: Digital transformation frameworks (notably the MIND framework for capability assessment) Platform design and governance mechanisms Smart service systems development Workplace transformation through digital subtraction logic Digital strategy implementation challenges Analysis of his 15 most recent publications (2024-2025) reveals a strong theoretical grounding in institutional and practice-based perspectives, with increasing emphasis on ethical dimensions of digital transformation, AI implementation constraints, and methodological innovations in computational research. His work consistently bridges conceptual rigor with practical applicability for organizational leaders. Dr. Baiyere actively shapes academic discourse through editorial roles including co-editing The Routledge Companion to Management Information Systems (2025) and organizing key events like the African IS Paper Development Workshop (2020). His public engagement includes 6 media contributions discussing digital workplace transformation and strategic implementation challenges, demonstrating commitment to translating research into practical insights for broader audiences. Within CBS, he has supervised 8 academic works while contributing to the department's international recognition in digitalization research. His activities reflect deep engagement with both theoretical advancement in information systems and practical solutions for organizational digital maturity.

• Algorithms
• Algorithmics
• Big Data
• +۷ مورد دیگر

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

• Computer Vision
• Image Analysis
• Machine Learning
• +۵ مورد دیگر

Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.

Melih Kandemirمشاهده پروفایل

دانشیار

• Machine Learning
• Reinforcement Learning
• Bayesian Inference
• +۳ مورد دیگر

Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.

Vito Latoraمشاهده پروفایل

استاد

• Complex Systems
• Network Science
• Applied Mathematics
• +۱۳ مورد دیگر

Vito Latora is a Professor of Applied Mathematics and Chair of Complex Systems at the School of Mathematical Sciences, Queen Mary University of London, and also holds the position of Professor of Theoretical Physics at the University of Catania. He leads the Complex Systems and Networks Group, driving cutting-edge research at the intersection of physics, mathematics, and interdisciplinary sciences. His research focuses on complex systems, particularly the structure and dynamics of networks, including multiplex, temporal, and higher-order networks such as simplicial complexes and hypergraphs. He explores applications in social, biological, financial, and cognitive systems, with recent work on creativity, innovation, and success through network analysis. The 15 most recent publications reveal a strong trend in advancing network theory beyond pairwise interactions, with a focus on higher-order structures, memory effects, synchronization, and epidemic spreading. His work combines rigorous mathematical modeling with real-world applications, often published in high-impact journals like Nature Communications , Physical Review Letters , and Science Advances . Dual communities in spatial and biological networks Modeling epidemics with limited detection resources Synchronization via higher-order and directed interactions AI-driven financial risk management Evolutionary games on hypergraphs Interdisciplinary success and funding dynamics Vito Latora has mentored several researchers who appear as co-authors, including Iacopini, Williams, Di Bona, and Lacasa. While specific grants are not listed, his collaborative projects with neuroscientists and anthropologists, along with frequent publications, suggest active funding. He is involved in major scientific events such as NetSci 2023, indicating leadership in the network science community. He leads the Complex Systems and Networks Group at Queen Mary, fostering a collaborative environment for studying complex systems through theoretical, computational, and data-driven approaches.

Romualdo Pastor-Satorrasمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Mathematical Physics
• Theoretical Physics
• +۳ مورد دیگر

Romualdo Pastor-Satorras is an Associate Professor of Applied Physics at the Universitat Politècnica de Catalunya (UPC) since 2006. He earned his PhD in Condensed Matter Physics from the Universitat de Barcelona in 1995, followed by postdoctoral research at MIT (1996–1998) and The Abdus Salam International Centre for Theoretical Physics (1998–2000). His extensive international collaborations include visiting positions at Yale University, University of Notre Dame, Kavli Institute for Theoretical Physics, Helsinky University of Technology, Indiana University, and the ISI Foundation. Research Focus His interdisciplinary work spans statistical physics, network theory, and dynamical systems. Primary research areas include: Modeling epidemic spreading in complex networks Random walks and diffusion processes in temporal networks Social and biological applications of network science Glassy dynamics and energy landscapes His research combines mathematical rigor with data-driven approaches to address problems in public health, social dynamics, and complex system behavior. Publications Overview With over 100 peer-reviewed publications, Pastor-Satorras's work demonstrates consistent focus on network dynamics and epidemic modeling. Recent articles explore COVID-19 herd immunity thresholds (2020), echo chambers in political networks (2019), and non-Poissonian temporal networks (2019). His foundational 2015 review on epidemic processes in networks is highly influential. Publications frequently involve interdisciplinary collaborations across physics, data science, and computational biology. Awards and Distinctions Fellow of Universitat Politècnica de Catalunya ICREA Academia Prize (awarded twice by the Government of Catalonia) Research Infrastructure He maintains active collaborations through visiting positions at leading global institutions. His work involves theoretical modeling and computational analysis, though specific laboratory details are unspecified in the provided text.

Kantaro Fujiwaraمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Neural Data Analysis
• Machine Learning
• +۱ مورد دیگر

Kantaro Fujiwara serves as Associate Professor at the Graduate School of Medicine, The University of Tokyo, with concurrent appointments at the International Research Center for Neurointelligence (IRCN) and the Department of Mathematical Informatics, Graduate School of Information Science and Technology. He also manages the Data Science Core infrastructure for IRCN. His academic background includes a Ph.D. in Information Science and Technology from the University of Tokyo (2008), followed by postdoctoral research at the University of Tokyo (JSPS) and University of Cambridge, then assistant professorships at Saitama University and Tokyo University of Science before joining the University of Tokyo faculty. Dr. Fujiwara's research bridges computational neuroscience and neural data analysis through mathematical modeling of neural networks, development of neural data analysis methodologies, and exploration of brain-inspired machine learning. His work extends to biological information processing with specific applications in pancreatic beta cell modeling for diabetes research, establishing connections between theoretical frameworks and experimental neuroscience. His publication record (2017-2023) reveals consistent interdisciplinary contributions applying echo state networks, recurrence analysis, and nonlinear dynamics to neural data classification, physiological signal processing, and disease modeling. These works demonstrate strong integration of computer science, neuroscience, and biomedical engineering methodologies to solve complex neurobiological problems. As Data Science Core Manager at IRCN, he oversees computational infrastructure and software resources that enable advanced neurointelligence research across the University of Tokyo ecosystem, providing critical support for data-intensive neuroscience projects.

Anne Elisabeth Haxthausenمشاهده پروفایل

دانشیار

• Formal methods
• Specification languages
• Semantics
• +۶ مورد دیگر

Anne Elisabeth Haxthausen is an Associate Professor at the Software Systems Engineering section within DTU Compute , Technical University of Denmark . Her work focuses on formal methods, railway control systems, and safety-critical software engineering. Founder and leader of the DTU Railway Verification Group Member of European Technical Working Group on Formal Methods in Railway Control Editorial board member for Springer Formal Aspects of Computing Journal Active in the Overture Language Board Her research emphasizes formal verification of railway interlocking systems, particularly through compositional approaches and automated tools. She has contributed to projects like RobustRailS, Overture, and RAISE, focusing on model-based development and verification. She serves as a tutor for bachelor students and contributes to the advisory committee for DTU's Computer Science and Engineering MSc program. Her recent publications explore challenges in verifying autonomous and AI-driven railway technologies.

Rasmus Paghمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Big Data
• +۷ مورد دیگر

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

Giulio Ciminiمشاهده پروفایل

دانشیار

• Statistical Physics
• Complex Networks
• Economic Networks
• +۱۲ مورد دیگر

Giulio Cimini is Associate Professor of Theoretical Physics in the Department of Physics at the University of Rome Tor Vergata and a Research Associate at the 'Enrico Fermi' Research Center. He is a statistical physicist with a strong interdisciplinary focus on complex networks and their applications in socio-economic systems. His research interests include: Statistical Physics of Complex Networks Reconstruction and Validation of Economic Networks Social Network Interactions and Financial Markets Systemic Risk and Financial Contagion Scientific Success, Fitness, and Complexity Adaptive Social Recommendation Codon Usage Bias and Protein Interaction Networks His recent publications reveal a strong trend in applying statistical physics to real-world networks, particularly in finance and social systems. Key themes include the modeling of systemic risk in supply chains and financial networks, the dynamics of collective action on platforms like Reddit (e.g., the GameStop short squeeze), and the development of network reconstruction methods using maximum entropy and optimal transport frameworks. His work often combines empirical analysis with theoretical modeling. Scientific awards and recognitions include: Associate Editor, Frontiers in Physics – Interdisciplinary Physics Board Member, Network Science Society Member, Council of the Complex Systems Society Steering Committee, CCS/Italy He has advised or collaborated with numerous researchers, particularly in projects related to economic networks and complex systems. His work has been supported by Italian national grants such as PRIN and PNRR. He leads or co-leads research projects including RENet and C2T. His research is conducted within interdisciplinary teams involving physicists, economists, and computer scientists, often in collaboration with institutions like ISC-CNR, IMT Lucca, and the Network Science community.

• Applied Statistics
• Statistical Consultancies
• Functional Data Analysis
• +۵ مورد دیگر

Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.

Kohei Nakajimaمشاهده پروفایل

دانشیار

• Soft Robotics
• Physical Reservoir Computing
• Physical Deep Learning
• +۵ مورد دیگر

Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.

Jesper Linigerمشاهده پروفایل

دانشیار

• Marine Growth Engineering
• Power Fluid Engineering
• Wind Turbine Engineering
• +۶ مورد دیگر

Jesper Liniger is an Associate Professor at AAU Energy within the Faculty of Engineering and Science at Aalborg University. He works in the Esbjerg Energy Section focusing on Offshore Renewable Energy Systems and is affiliated with AAU BLUE – Marine & Maritime Research. His office is located at Niels Bohr Street 8, 6700 Esbjerg, Denmark. Research Interests Marine Growth Engineering and automated cleaning solutions for offshore structures Underwater robotics including Remotely Operated Vehicles (ROVs) and autonomous inspection systems Wind turbine engineering with emphasis on hydraulic pitch systems and fault detection Fluid power engineering applications in marine environments Development of robotic solutions for offshore renewable energy infrastructure Research Trends Dr. Liniger's recent publications demonstrate a strong focus on developing robotic solutions for offshore renewable energy infrastructure. His work bridges theoretical control systems with practical marine applications, particularly addressing marine growth (biofouling) challenges on offshore structures. The research shows increasing interdisciplinary collaboration, combining robotics, fluid mechanics, and wind energy systems to create integrated solutions that improve operational efficiency and reduce maintenance costs in offshore environments. Scientific Awards Innovation Project of the Year (2024) - For underwater robotics development Esbjerg Universitetspris (2018) - University award recognizing research excellence Advising and Research Leadership Dr. Liniger actively supervises PhD students and serves as principal investigator or supervisor on multiple major projects including "NextGen Robotics" for offshore wind farms and "Towards Enhancing Perception and Navigation for Autonomous Underwater Inspection Drone." His research portfolio includes collaborations with industry partners like Vattenfall and Business Center Funen, demonstrating strong industry-academia connections focused on practical applications with economic impact. Research Teams and Facilities Liniger is part of AAU BLUE – Marine & Maritime Research, which provides specialized facilities for marine robotics testing and development. His work involves close collaboration with researchers in control systems, fluid mechanics, and renewable energy. The research group has developed experimental frameworks for testing underwater and surface vehicle operations, with recent media coverage highlighting their innovative approaches to solving marine growth challenges on offshore structures.

• Transportation Engineering
• Urban Planning
• Sustainable Mobility
• +۵ مورد دیگر

Ravi Seshadri is an Associate Professor in the Transport Division at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research focuses on designing equitable, efficient, and sustainable mobility solutions with a focus on fiscal instruments like congestion pricing and tradable permits, as well as emerging mobility modes such as shared and demand-responsive transit. He employs methods from transportation network equilibria, dynamic traffic assignment, and agent-based simulation. His research interests span transportation economics, urban planning, and intelligent transportation systems. Key areas include evaluating the impacts of automated mobility-on-demand systems, optimizing tolling strategies using predictive control and reinforcement learning, and integrating multi-modal transportation networks through game-theoretical frameworks. His work emphasizes real-world applications in urban freight systems, e-commerce logistics, and sustainable urban mobility policies. Recent projects include studying congestion pricing schemes via agent-based microsimulation, analyzing behavioral responses to decarbonization policies, and developing frameworks for tradable credit systems with peer-to-peer trading. He has contributed to both theoretical advancements (e.g., robust traffic assignment models) and applied tools like the SimMobility simulation platform. Ravi's research demonstrates a strong focus on bridging transportation engineering with policy analysis, using cutting-edge computational methods to address complex urban mobility challenges. His work spans academic publications, industry collaborations, and policy consultations to advance sustainable transportation systems.

Márton Karsaiمشاهده پروفایل

دانشیار

• Network Science
• Human Dynamics
• Temporal Networks
• +۱۱ مورد دیگر

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at the Central European University in Vienna, and a Research Professor at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest. He leads the Computational Human Dynamics Lab, focusing on data-driven modeling of social and biological systems. He is also the Editor-in-Chief of the journal Advances in Complex Systems . His research interests lie at the intersection of network science, human dynamics, and socioeconomic systems. He specializes in temporal and spatial networks, modeling contagion processes (both social and biological), and analyzing large-scale human behavioral datasets. His work integrates computational methods with real-world data to understand complex social phenomena such as mobility patterns, migration, segregation, and epidemic spread. He is particularly known for using remote sensing and digital trace data to infer poverty and socioeconomic conditions in urban areas. The recent publications highlight a strong trend in applying network science and machine learning to societal challenges. His work spans high-impact journals in complex systems, data science, and computational social science, with recurring themes in epidemic modeling, urban analytics, socioeconomic inference, and the structure of temporal and spatial networks. The research is highly interdisciplinary, combining physics, computer science, and social science methodologies. He has been invited to speak at major events such as the Conference on Complex Systems, the Lake Como School on Complex Networks, and workshops on data for vulnerability assessment. He served as general co-chair of CCS 2021 in Lyon, demonstrating leadership in the complexity science community. General Co-Chair, Conference on Complex Systems (CCS) 2021, Lyon Invited speaker, 4th Workshop on Data for the Wellbeing of the Most Vulnerable @ ICWSM'23 Invited speaker, Complexity72h Workshop Invited lecturer, Lake Como School on Complex Networks Invited talk, Hungarian Academy of Sciences on COVID-19 modeling While specific grant details are not listed, his coordination of projects on segregation, migration, and poverty inference—often in collaboration with the Complexity Science Hub—suggests active involvement in externally funded interdisciplinary research. He advises students through the Department of Network and Data Science at CEU, though specific advisees are not named. His lab, the Computational Human Dynamics Lab, serves as a hub for data-driven research on social systems.