Find Academic Supervisors

• Theoretical Computer Science
• Algorithms
• Edge Estimation
• +5 more

Holger Dell is a Lecturer in Theoretical Computer Science and Algorithms at the IT University of Copenhagen. His research focuses on computational complexity, graph theory, and algorithmic efficiency. Active in polynomial-time algorithms and oracle-based methods Contributions to edge estimation in hypergraphs and fairness in node embeddings Key collaborations with BARC (Basic Algorithms Research Copenhagen) project Research trends show expertise in causal modeling, finite field polynomial solving, and graph embedding techniques. Recent work emphasizes algorithmic fairness and abstract causal relationships. Participated in a major project funded by the Villum Foundation (2017-2024) as a collaborator.

• Material Flow Analysis
• Remote Sensing
• Emission Reduction
• +6 more

Kun Sun is a postdoctoral researcher at the Department of Green Technology (IGT) within the Faculty of Engineering at the University of Southern Denmark. His work intersects machine learning, remote sensing, and urban sustainability, focusing on material flow analysis and environmental impact assessment. Research Interests: Active in material flow analysis, remote sensing applications, and emission reduction strategies, Kun Sun's research combines deep learning techniques with environmental science to address urban sustainability challenges. His recent projects include building material identification and sociometabolic transitions in Eastern Europe. Scientific Awards: Contributed to the project "Exploring the green transformation of the Danish pork supply chain and its contribution to carbon neutrality" (2025). Projects: Participates in ongoing research analyzing the Danish pork supply chain's role in achieving carbon neutrality. This work emphasizes circular economy strategies and industry transformation. Media Contributions: Has been featured in three 2025 press releases discussing AI applications for building material recycling, climate benefits in AI, and scanning technologies for urban sustainability.

Ali KhosraviView profile

Associate Professor

• Machine Learning
• Energy Systems Analysis and Optimization
• Power-to-X Technologies
• +7 more

Ali Khosravi is an Associate Professor at the University of Southern Denmark (SDU) , affiliated with the Institute of Mechanical and Electrical Engineering and SDU Mechatronics (CIM) . His work focuses on integrating machine learning with energy systems, particularly in the areas of power-to-X technologies, renewable energy optimization, and thermodynamic systems. Research Interests: Machine learning applications in energy systems Power-to-X technologies (methanol, hydrogen) Thermodynamic analysis and optimization Renewable energy integration and storage Solid oxide fuel cells (SOFC) and electrolysis systems His recent publications span high-impact journals such as Energy , Renewable Energy , and International Journal of Hydrogen Energy , focusing on topics like methanol production from CO₂ and solar hydrogen, stochastic modeling for energy systems, and AI-driven optimization of fuel cell systems. Scientific Awards: Villum Young Investigator Grant (2023) Listed among World's Top 2% Scientists in Energy (2021, 2022) Postdoctoral Fellowship Award (2018) PhD Scholarship Award (2016) Teaching & Supervision: Course instructor for "Mechanical Semester Project 3" Supervisor for Special Course in Mechanical Engineering PhD supervision: M. Lautenschläger, M. Taslimi, N. Mirzakhani, L. Xia, and others Research Labs & Teams: He leads the DDPTX: Deciphering the Dynamics of Power-to-X Systems project (2024–2029), funded by Villum Fonden, and actively participates in several international collaborations including the Denmark & Australian Partnership on Green Hydrogen Value Chains and GREMEOH-Power-to-X.

• Wireless Communication Engineering
• Artificial Intelligence
• Neural Network
• +5 more

Benaoumeur Senouci is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark (SDU). His research spans interdisciplinary domains including wireless communication, artificial intelligence, and embedded systems. Research Focus: Wireless Communication Engineering, Neural Networks, and Edge Computing Key Collaborations: Active partnerships in 6G wireless systems, autonomous vehicle efficiency, and precision agriculture technologies Recent work highlights machine learning applications in fuel consumption estimation (2025), hybrid beamforming for 6G networks (2025), FPGA-based neural network prototyping (2024), and smart agriculture control systems (2024). Research outputs demonstrate strong integration of AI techniques with mechanical/electrical engineering challenges.

Ali SahafiView profile

Assistant Professor

• Electrical Engineering
• Computer Science
• Biomedical Engineering
• +4 more

Ali Sahafi is an Assistant Professor at the Institute of Mechanical and Electrical Engineering , University of Southern Denmark, specializing in Digital and High Frequency Electronics . His research bridges Electrical Engineering , Computer Science , and Biomedical Engineering . Research Interests: Sahafi focuses on neural network accelerators , medical imaging for cardiovascular and gastrointestinal diagnostics, and low-power electronics for embedded systems. His work includes applying YOLO-V8 for polyp detection in colonoscopy and coronary segmentation in angiography, alongside FPGA-based solutions for wireless capsule endoscopy. Project Involvement: He contributed to the EU H2020 TeamPlay project (2018–2020), optimizing time, energy, and security in cyber-physical systems. His lab, Digital and High Frequency Electronics , drives innovations in high-frequency circuits and embedded AI.

Søren Paaske JohnsenView profile

Professor

• Apoplexy
• Cohort Analysis
• Brain Ischemia
• +5 more

Søren Paaske Johnsen is a Professor at Aalborg University Hospital under the Faculty of Health Sciences, affiliated with the Danish Center for Health Services Research (PRIMA: Center for Primary Sector Research). His work spans epidemiology, clinical research, and health services innovation in Denmark. Institutional Affiliation: Aalborg University Hospital, Danish Center for Health Services Research Research Focus: Stroke care, atrial fibrillation, chronic pain, AI in healthcare, and migrant health disparities Projects: Principal Investigator for SEXA-PRO, co-investigator for Social Prescribing EU, and contributor to AI-based care pathways Research Trends: His 802+ publications emphasize cohort studies on cardiovascular and neurological conditions, AI integration in clinical practice, and equity in healthcare access. Recent work includes nationwide analyses of stroke outcomes, AI-driven thrombectomy protocols, and socioeconomic/immigration-related disparities in treatment adherence. Scientific Contributions: Notable initiatives include the ARISTOTELES AI project for atrial fibrillation, TaKeAction telehealth pathway for post-knee replacement pain, and datasets on stroke quality during the pandemic. Media engagement covers medical ethics, stroke prevention, and mental health funding critiques.

Jonas OlafssonView profile

Lecturer

• Biometric Identification
• Encrypted Biometrics
• Biometrics
• +5 more

Jonas Olafsson serves as an Assistant Lecturer at the Technical University of Denmark (DTU), where he bridges academic instruction with cutting-edge research in biometric security. His institutional affiliation centers on DTU's computer science and engineering ecosystem, though specific school/department details remain unconfirmed in available materials. Olafsson's research forms a distinctive fingerprint in privacy-enhanced biometrics: Homomorphically encrypted biometric identification systems Computational burden reduction for encrypted matching Speed-up optimization through coefficient packing Dimensionality reduction preserving recognition performance Tradeoff analysis between security and usability His seminal 2022 work demonstrates how coefficient packing techniques drastically reduce processing overhead in encrypted biometric matching while maintaining accuracy. This research direction addresses critical industry pain points in deploying GDPR-compliant biometric systems across financial, healthcare, and consumer technology sectors. As an early-career academic with a student email identifier, Olafsson likely operates within DTU's graduate teaching assistant framework. His publication record shows focused contributions to biometric security with tangible industry impact evidenced by patent citations, though comprehensive details about supervision capacity or grant leadership remain undisclosed in current documentation.

Anastasia IoannouView profile

Associate Professor

• Wind Energy Systems
• Energy Policy
• Predictive Control Modeling
• +3 more

Anastasia Ioannou is an Associate Professor at the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). Her work focuses on energy systems, predictive control modeling, and sustainable development, with applications in wind energy and heat pump systems. Key Expertise: Wind Power Engineering, Variable Power Production, Predictive Control Models, Technoeconomic Analysis Research Trends: Recent publications highlight her contributions to data-driven wind turbine control, energy citizen profiling for policy design, and optimization of decentralized heating/cooling networks using machine learning and deep learning methodologies. Projects: She actively contributes to a consultancy project analyzing offshore wind potential in Ukraine, collaborating with international stakeholders. Activities: Presented at the IEA Wind Task 53 conference on uncertainty impacts in wind energy economics.

• Transportation Systems Modelling
• Machine Learning Applications
• Urban Mobility
• +2 more

Guido Cantelmo is an Assistant Professor at the Technical University of Denmark (DTU) within the Department of Technology, Management and Economics, specifically in the Division of Transport's Section for Transport Systems Modelling. His research leverages big data analytics and machine learning to address complex transportation challenges, with expertise spanning traffic flow modeling, demand estimation, shared mobility systems, and urban network optimization. He maintains active collaboration with international cities including Copenhagen, Munich, and Tel Aviv-Yafo for empirical validation of his models. His research integrates computational techniques such as Graph Neural Networks, meta-learning, and physics-informed AI with transportation theory. Primary domains include: Dynamic traffic assignment using real-time data sources Machine learning for imbalanced mobility datasets Emission impact modeling of urban fleets Behavioral analysis of shared mobility adoption Large-scale simulation calibration frameworks Publication analysis (2022-2025) reveals dominant themes: data-driven demand estimation (37% of recent works), machine learning metamodeling (27%), shared mobility optimization (20%), and urban policy impact studies (16%). Methodological innovations include transfer learning for sparse data and multi-city validation approaches. No scientific awards or student mentoring relationships are documented in available sources. Similarly, no information exists regarding research grants, laboratory affiliations, or educational background.

Guillermina EslavaView profile

Lecturer

• Discriminant Analysis
• Graphical Models
• Multimorbidity
• +3 more

Guillermina Eslava serves as an External Lecturer in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Statistics and Data Analysis. Her research bridges advanced statistical methodology with critical public health applications, particularly in Danish register-based studies. Her research interests feature prominently in: Discriminant Analysis : Pioneering tree-structured approaches for mixed data classification Graphical Models : Leveraging conditional independence structures for efficient modeling Multimorbidity Research : Analyzing co-occurring chronic conditions in Danish populations Health Statistics : Transforming register data into epidemiological insights Tree-structured Models : Building interpretable frameworks for complex healthcare data Data Analysis : Solving real-world challenges in health statistics Recent publications reveal a dual focus: methodological innovation in discriminant analysis (using tree structures and graphical models) and applied health research on multimorbidity patterns. Her work consistently integrates simulated and real-world data validation, with increasing emphasis on Danish healthcare register applications since 2023. No scientific awards were documented in available materials. While specific PhD advising isn't detailed, Eslava actively contributes to doctoral education through courses like 'Introduction to applied statistics and R for PhD students' at DTU. Conference presentations at national healthcare research events demonstrate her engagement in knowledge dissemination, though grant funding details remain unspecified.

• Chemical Thermodynamics
• Equations of State Modeling
• Ionic Liquids
• +3 more

Georgios Kontogeorgis is a Professor at DTU Chemical Engineering , Department of Chemical and Biochemical Engineering, Danmarks Tekniske Universitet (DTU). His research focuses on advanced thermodynamic modeling and equations of state, with particular emphasis on ionic liquids , CO2 capture , and electrolyte solutions . Email: gk@kt.dtu.dk Affiliations: AT-CERE, KT-Konsortium Professor Kontogeorgis' work spans from fundamental thermodynamic theories (e.g., Debye-Hückel, SAFT) to industrial applications like energy-efficient chemical processes and solvent design . Recent studies include modeling water anomalies, ion association effects, and phase equilibria in complex solvent systems. His publications reveal trends in machine learning integration for thermodynamic predictions, CO2 mineralization studies, and multifunctional solvent design for separation processes. Collaborative projects with AT-CERE and KT-Konsortium focus on carbon capture and chemical engineering innovation . Labs/Teams: Affiliated with AT-CERE (Center for Energy Resources Engineering), KT-Konsortium (Chemical and Biotechnological Consortium)

Jie CaiView profile

Assistant Professor

• Structural Engineering
• Pipeline Mechanics
• Maritime Engineering
• +4 more

Jie Cai is an Assistant Professor at the Department of Technology and Innovation, University of Southern Denmark, specializing in structural engineering, maritime operations, and data science. His research bridges computational methods with marine and pipeline mechanics. Key research areas include structural stability, residual strength analysis, lateral buckling, and data-driven maritime systems Recent work focuses on medical image segmentation (2025) and burst strength prediction for corroded pipelines (2025) Active in digital twin development for vessel operations and engine monitoring He has contributed to journals like Computers and Electrical Engineering , Ocean Engineering , and Journal of Marine Science and Application , with expertise in finite element analysis and anomaly detection. Jie Cai serves as a peer reviewer for journals including the Journal of Offshore Mechanics and Arctic Engineering and Energies , and teaches courses in data science, programming, and IoT applications at the master's and bachelor's levels.

Christian T. VejeView profile

Professor

• Applied mathematical modeling
• Finite element models
• Numerical methods and simulation
• +4 more

Christian T. Veje serves as Professor and Head of the Department of Mechanics and Electronics (IME) at the University of Southern Denmark, leading research in energy systems and computational modeling with 144 publications. His work bridges theoretical numerical methods and practical engineering solutions for sustainable energy applications across thermal engineering domains. Research interests prioritize applied mathematical modeling, finite element analysis, and numerical simulation techniques focused on energy systems optimization. Specific applications include phase change materials for thermal storage, district heating networks, lithium-ion battery systems, heat pump technology, and predictive control models for energy performance enhancement. His fingerprint analysis reveals strong connections to thermal engineering subfields like regenerators and energy renovation. Recent publications (2022-2024) demonstrate concentrated innovation in renewable energy integration, waste heat recovery, and AI-driven building management. Key trends include hybrid thermal storage systems using phase change materials, techno-economic optimization of island energy grids, and liquid cooling solutions for data centers. His work consistently applies computational fluid dynamics and predictive modeling to solve complex energy challenges. No scientific awards were documented in the source materials. Veje has supervised 3 PhD students and secured leadership roles in multiple research initiatives including the Digital Twin for building decision support (2021-2024), PROMA predictive maintenance project (2021-2024), and NeGeV ventilation research (2018-2021). His grants address energy optimization, predictive maintenance strategies, and advanced thermal management systems through industry-academic collaborations. While specific laboratory names aren't provided, Veje actively participates in multi-institutional teams including the EUROFusion BB RH facility project (2024-2026) and has contributed to editorial boards for Energies and Energy Informatics journals, demonstrating cross-disciplinary engagement in energy research communities.

Esmaeil NadimiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +5 more

Esmaeil Nadimi is a Professor and Head of Unit at The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, where he leads the Applied AI and Data Science research unit. He holds multiple concurrent positions including Visiting Professor at Harvard University, Chair of Data Analytics at SDU eScience Center, and research roles with Innovationsfonden and Swiss National Science Foundation (SNSF) in Medicine & Biology and Health & Well-being domains. His educational background includes a Ph.D. in Electrical and Computer Engineering from Aalborg University (2008) and an M.Sc. in Electrical Engineering (Control Theory) from Sharif University of Technology (2004). Research focuses on foundational AI methodologies including Physics-Infused Learning, Meta & Transfer Learning, with applied research in healthcare (medical imaging, cancer detection, diabetic neuropathy), energy systems, and optical technologies. Core expertise spans machine learning, computer vision, statistical modeling, and medical AI systems development. Publications demonstrate strong emphasis on AI applications in medical diagnostics, particularly capsule endoscopy, cervical/colorectal cancer detection, and diabetic complication screening. Recent work increasingly addresses explainable AI frameworks and practical implementation pathways for clinical adoption. Leadership includes supervision of industrial PhD/postdoc programs and development of academic courses in AI/Data Science. Current grants involve Innovationsfonden and SNSF-funded projects. Leads multiple research teams focused on medical AI systems at SDU Applied AI and Data Science unit.

• Artificial Intelligence
• Medical Robotics
• Embedded Systems
• +5 more

Jan-Matthias Braun is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. His research bridges artificial intelligence, robotics, and medical device engineering. Current projects focus on explainable AI integration in colon capsule endoscopy Development of real-time FPGA-based systems for colorectal diagnostics Biomechanical modeling for adaptive orthotic devices His work emphasizes cross-disciplinary applications of machine learning in healthcare, particularly for gastrointestinal disease detection and assistive robotics. Publications demonstrate expertise in deep neural networks, hardware acceleration, and smart environment control systems. Teaching responsibilities include: Advanced cybersecurity courses Deep learning applications in epilepsy detection Mentorship in capsule endoscopy image analysis