جست‌وجوی استاد و پژوهشگر

Siavash Arjomand Bigdeliمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Diffusion Models
• +۷ مورد دیگر

Siavash Arjomand Bigdeli is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he conducts research in Visual Computing. His work spans computer vision, machine learning, and AI applications in medical imaging and food science. He is actively involved in interdisciplinary research and supervises multiple PhD projects. Research Interests: Dr. Bigdeli's research focuses on deep learning for visual data, particularly diffusion models, image synthesis, and privacy-preserving AI. He investigates how AI models can be made more robust, explainable, and fair, especially in sensitive domains like healthcare. His work also extends to texture analysis, scale-space theory, and structural modeling in both biological and food materials. The recent publications reveal a strong trend toward diffusion models , membership inference attacks , and counterfactual generation , indicating expertise in both generative modeling and AI security. These works are applied in domains such as medical imaging and computer vision, showing a balance between theoretical innovation and practical impact. Scientific Supervision and Projects: Supervisor in Human-XAI Collaboration for Improved Fetal Ultrasound Imaging (2025–2028) Supervisor in Bias and Fairness in Medical Imaging (2023–2026) Project Participant in EXCHEQUER: Examining Cheese Quality Elements and Reactions (2021–2025) He collaborates closely with researchers such as Aasa Feragen, Anders B. Dahl, and Jeppe R. Frisvad. His lab is part of DTU Compute’s Visual Computing section, contributing to both fundamental algorithms and real-world applications in AI-driven imaging.

Tommy Sonne Alstrømمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Nanotechnology
• +۳ مورد دیگر

Tommy Sonne Alstrøm is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Danmarks Tekniske Universitet (DTU). His research focuses on machine learning, signal processing, and nanotechnology-based sensor systems. Key research areas include: Explainable AI for time series and spectroscopy Diffusion models for speech enhancement Federated learning optimization Probabilistic modeling of biosignals His recent publications demonstrate expertise in neural network geometry, federated learning privacy, and spectroscopic data analysis. Current work involves self-supervised learning for variable-channel time series and road condition modeling using LiRA-CD dataset. Contact: tsal@dtu.dk

Gozde Unalمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Gozde Unal is a Full Professor in the Department of Computer Engineering at Istanbul Technical University's Faculty of Computer and Informatics Engineering, where she also serves as director of the ITU Vision Lab and founding professor of the AI&Data Engineering Department. She previously held faculty positions at Sabancı University and conducted industry research at Siemens Corporate Research. Her educational background includes a PhD in Electrical and Computer Engineering with a Mathematics minor from North Carolina State University (2002), following postdoctoral work at Georgia Institute of Technology. Professor Unal's research pioneers intersections of Artificial Intelligence and Computer Vision, with significant contributions to medical imaging, 3D reconstruction, and architectural heritage preservation. Her work on diffusion models, adversarial point cloud attacks, and training-free segmentation demonstrates technical innovation while addressing real-world challenges in healthcare diagnostics and cultural conservation. The integration of multi-modal dynamics and evidential deep learning reflects her forward-looking approach to uncertainty quantification in AI systems. Analysis of her 2023-2025 publications reveals dominant trends in point cloud processing for historical reconstruction, medical image segmentation using Bayesian frameworks, and continual learning architectures. These works consistently bridge theoretical advances with applications in architectural heritage documentation and clinical diagnostics, particularly through novel diffusion model adaptations. Her scientific leadership has been recognized through prestigious awards: L’Oreal Turkey’s Female Scientist Award in Life Sciences (2010) Distinguished Young Scientist Award from TUBA (GEBIP) Marie Curie Alumni Association Career Award (2017) As technical program co-chair for MICCAI 2016 and MIDL 2019, she has shaped international discourse in medical image computing. Her lab direction indicates active graduate supervision, though specific student names aren't documented in the source material. The ITU Vision Lab under her leadership drives cutting-edge research in medical image analysis and 3D scene understanding, while her role in founding the ITU-AI Research Center establishes institutional frameworks for interdisciplinary AI innovation across the university.

Panagiotis Karrasمشاهده پروفایل

استاد

• Data Management Systems
• Graph Algorithms
• Data Mining
• +۳ مورد دیگر

Panagiotis Karras is a Professor at the Department of Computer Science , University of Copenhagen , with a faculty affiliate status at Aarhus University . His research focuses on robust and versatile methods for data access, mining, and representation. Education: MSc in Electrical and Computer Engineering from National Technical University of Athens , PhD in Computer Science from University of Hong Kong Research Interests: Spanning data management systems, graph algorithms, and privacy-preserving data publishing, his work addresses challenges in efficient query processing, influence propagation in networks, and semantic-aware data compression. Notable contributions include adaptive indexing techniques, graph alignment frameworks, and algorithms for fair stable marriage problems. Recent Publications (2024-2025): Focus on graph alignment, subgraph matching, and influence maximization, with applications to knowledge graphs, social networks, and time-series data analytics. Scientific Recognition: 2008 Young Scientist Award from the Hong Kong Institute of Science Keynote invitations at ACM SIGKDD and IEEE ICDE Professor Karras actively seeks motivated PhD students to collaborate on these research directions. His lab maintains a powerful compute cluster and modern facilities for data-intensive research.

• Algorithms
• Complexity Theory
• Data Management
• +۲ مورد دیگر

David Rasmussen Lolck is a PhD Fellow at the University of Copenhagen's Department of Computer Science, specializing in the Algorithms and Complexity section. His research focuses on theoretical and applied aspects of computer science, particularly in algorithms, coding theory, and privacy-preserving data analysis. Current affiliation: Department of Computer Science (DIKU) , University of Copenhagen Research areas: Algorithms, complexity theory, differential privacy, and data management Recent publications address noise-robust coding and combinatorial clustering algorithms Contact: dalo@di.ku.dk | Phone: +45 3533 2122

• Programming Languages
• Theory of Computation
• Formal Verification
• +۳ مورد دیگر

Atta Samiullah serves as an Instructor in the Department of Computer Science at the University of Copenhagen, affiliated with the Programming Languages and Theory of Computation (PLTC) section. His role encompasses teaching responsibilities across undergraduate and graduate computer science programs including BSc in Computer Science, Computer Science and Economics, Machine Learning and Data Science, and Communication & IT. His research profile aligns with PLTC's core focus areas: Programming language technology and formal verification methodologies Theory of computation with applications in distributed ledger systems Security and privacy preservation techniques in software systems Financial technology applications and blockchain infrastructure Compiler technology and high-performance computing architectures Quantum programming language design The PLTC section operates through specialized research groups including Decentralized Systems, Security & Privacy, HOT Lab, FUTHARK (Functional Technology for Modern Architectures), and Quantum Programming. These groups maintain active industry collaborations in fintech and blockchain sectors while developing open-source tools like Moscow ML and MLKit compilers. Research output spans probabilistic programming frameworks, Stein variational inference techniques, and applications in bioinformatics and vaccine design. No scientific awards or student supervision activities are documented in current institutional records. His work contributes to the department's mission in advancing programming language theory with practical implementations in secure financial systems and distributed computing environments.

Martin Bøgstedمشاهده پروفایل

استاد

• Clinical Data Science
• Artificial Intelligence
• Applied Probability Theory
• +۱۰ مورد دیگر

Martin Bøgsted is a Professor at Aalborg University, serving as Center Director at Clinda (Center for Clinical Data Science) within the Clinical Institute of the Faculty of Health Sciences. With over 30 years of experience spanning academia, public sector, and industry, he leads significant research initiatives in clinical data science and artificial intelligence applications in healthcare. His research interests span clinical data science, artificial intelligence, applied probability theory, statistics, and machine learning with applications in medicine, agriculture, and telecommunications. His fingerprint analysis reveals specialized expertise in Diffuse Large B-Cell Lymphoma (100%), B Cell research (74%), Multiple Myeloma (41%), Overall Survival analysis (40%), Malignant Neoplasm studies (34%), and Personalized Medicine (23%). His recent publications demonstrate a strong focus on AI applications in healthcare, privacy-preserving synthetic data generation, clinical trial participation, and time-to-event prediction models. His work shows a clear trend toward integrating AI for personalized risk assessment and clinical interventions, with significant emphasis on ethical development of health data technologies. Martin Bøgsted actively supervises master's and PhD students, with records indicating 14 PhD students supervised. He leads major research projects including SE3D (Synthetic health data: ethical development and deployment via deep learning approaches) funded by the Novo Nordisk Foundation (2024-2028) and ARISTOTELES (Integrating AI for Personalized Risk Assessment) (2023-2028). He directs the Center for Clinical Data Science (CLINDA) at Aalborg University, which focuses on developing innovative infrastructure, artificial intelligence, and digital tools for multimodal data fusion and predictive modeling in the healthcare sector. His leadership extends to advisory roles including the Working Group for the National Genome Database and the Advisory Board for Research and Infrastructure at the Danish National Genome Center.

Xiufeng Liuمشاهده پروفایل

پژوهشگر

• Energy Systems
• Data Analytics
• Renewable Energy
• +۵ مورد دیگر

Xiufeng Liu is a Senior Researcher at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on smart meter data analytics, big data, and energy systems, contributing to UN Sustainable Development Goals related to affordable and clean energy. He holds a PhD from Aalborg University (2012) and has held positions at IBM Canada, the University of Waterloo, and Åbo Akademi University. Dr. Liu’s expertise spans energy economics, climate policy modeling, and data-driven methodologies. His work integrates machine learning with energy systems, addressing challenges such as wind power forecasting, solar cell optimization, and heat load prediction. Key projects include OPTIX (optimizing positive-energy districts) and ANSWER (wind-solar energy prediction models). His research outputs emphasize sustainable energy solutions, including federated learning frameworks for privacy-preserving data sharing and anomaly detection in energy grids. Collaborations span global institutions, reflecting his commitment to interdisciplinary energy solutions. Dr. Liu has supervised numerous projects and contributed to 148 publications. His work bridges technical innovation with policy implications, aiming to advance climate-compatible energy strategies globally.

Ivan Bjerre Damgårdمشاهده پروفایل

استاد

• Cryptography
• Secure Communication
• Multi-Party Computation (MPC)
• +۳ مورد دیگر

Professor Ivan Bjerre Damgård is affiliated with the Department of Computer Science at Aarhus University, where he holds a full academic rank of Professor. His primary research focus lies in cryptography, secure communication, and privacy-preserving technologies. Key areas include multi-party computation (MPC), homomorphic encryption, and differential privacy. He has contributed to foundational work on secure protocols for dynamic networks, threshold cryptosystems, and resilient distributed systems. His recent publications (2023–2024) emphasize advancements in MPC protocols with optimized communication and round complexity, secure computation in unstable networks, and privacy-preserving techniques. Notable work includes 'Phoenix' (2023) addressing network instability and 'Broadcast-Optimal MPC' frameworks minimizing setup requirements. Professional activities include organizing conferences like TCC 2009 and ICALP 2008, as well as delivering lectures on cryptography and IT security. No scientific awards are explicitly listed in the provided information. His research collaborates extensively with global institutions, reflected in co-authored papers with researchers like Agarwal, Braun, and Ciampi. Ongoing work appears to prioritize practical implementations of theoretical cryptographic constructs in real-world distributed environments.

Paul Hünermundمشاهده پروفایل

استاد مدعو

• Causal Inference
• Econometrics
• Innovation Economics
• +۳ مورد دیگر

Paul Hünermund is a Tenure Track Adjunct Professor at the Department of Strategy and Innovation, Copenhagen Business School. His research focuses on causal inference, econometrics, and innovation economics, with notable contributions to strategic management methodologies and AI-driven innovation systems. Current research trends in his publications include: (1) causal inference techniques in management science; (2) impact of AI on knowledge diffusion and innovation ecosystems; (3) control variable selection in empirical analysis; and (4) post-conflict innovation dynamics. His recent work appears in journals like Econometrics Journal and Organizational Research Methods . He has contributed to debates on innovation policy, AI applications for SMEs, and research productivity challenges, with media appearances in Danish and international outlets. His work aligns with UN Sustainable Development Goals related to innovation and sustainable economic growth. Supervised work includes 6 projects, though specific advisees aren't named in the provided texts. The ORCID identifier 0000-0001-9163-038X is associated with his academic profile.

• Formal Methods
• Privacy Engineering
• Probabilistic Programming
• +۳ مورد دیگر

Raúl Pardo Jimenez is an Associate Professor in the Department of Software Engineering at the IT University of Copenhagen, affiliated with the Software Quality Research (SQUARE) group. His research focuses on formal methods and privacy engineering, with particular emphasis on probabilistic programming and privacy risk quantification in data analytics. His research spans three interconnected domains: Formal verification of privacy compliance systems like GDPR implementation Probabilistic modeling for quantitative privacy risk assessment Open source sustainability impacts on software quality metrics Recent publications demonstrate a consistent focus on developing rigorous computational methods for privacy preservation, with works appearing in Springer's Lecture Notes in Computer Science and Empirical Software Engineering journals. He leads the REIDENT project (2018-2022), funded by Villum Fonden, which developed Bayesian probabilistic programming approaches for reidentification risk assessment in sensitive datasets.

Peter Schneider-Kampمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.

Carsten Keßlerمشاهده پروفایل

استاد

• Geographic Information Systems
• Population Distribution
• Migration Studies
• +۷ مورد دیگر

Carsten Keßler is a Professor at the Department of Sustainability and Planning within The Technical Faculty of IT and Design at Aalborg University. He holds adjunct professorships at Bochum University of Applied Sciences (since 2021) and the Ph.D. Program in Earth and Environmental Sciences at the City University of New York (2017–2021). His research focuses on geoinformatics, population dynamics, migration patterns, and spatial data infrastructure. Education: Not explicitly listed in provided texts, but his academic rank implies doctoral qualification. Key research areas include: - Geographic Information Systems (GIS) for urban and environmental analysis. - Migration studies , including historical migration patterns and future migration scenarios for Europe. - Spatial data infrastructure development and open educational resources for GIScience. - Machine learning applications in population modeling and environmental projections. He leads/co-leads projects such as FUME: Future Migration Scenarios for Europe (2019–2023) and EO4SDG: Earth Observation and Deep Learning for Sustainable Development . His work has produced over 75 publications, with recent focus on climate resilience, urban heatwaves, and geoprivacy. Notable awards include a prize for High-resolution spatialized population projections (2017). He actively contributes to academic peer review, conferences, and educational initiatives in geoinformatics.

• Data Management
• Location-Based Services
• Trajectory Data
• +۷ مورد دیگر

Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.

• Computability Theory
• Rewriting
• Lambda Calculus
• +۳ مورد دیگر

Jakob Grue Simonsen is a Professor and Department Chair at the Department of Computer Science (DIKU), University of Copenhagen. He holds a Dr. Scient., PhD, and MBA. His research focuses on the mathematics of computation, including computability theory, term rewriting, lambda calculus, complexity hierarchies, and symbolic dynamics, with applications in information retrieval and human-computer interaction. His primary research explores infinite computational processes and their finite representations, alongside practical work in neural hashing, fact-checking systems, and quantum language models. Previously, he contributed to constructive mathematics and biocomputing. An analysis of his 15 most recent publications (2015–2022) reveals strong emphasis on: Theoretical computability and game-theoretic models Neural networks for NLP (BERT, semantic hashing) Fact verification and explainable AI methodologies Efficient retrieval algorithms and recommendation systems Awards: RTA 2004 Best Paper Award CHI '13 Honorable Mention CHI '14 Honorable Mention