Find Academic Supervisors

Hugo-Andrés López-AcostaView profile

Associate Professor

• Business Process Management
• Formal Methods
• Distributed Systems
• +3 more

Hugo-Andrés López-Acosta serves as an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), actively contributing to research in business process management and digital compliance. His work aligns with UN Sustainable Development Goals through innovative computational solutions for regulatory frameworks and process optimization. His core research spans Business Process Management , Formal Methods , Distributed Systems , and Programming Languages , with specialized expertise in process mining, ambiguity resolution in business descriptions, and compliance automation. Recent projects integrate natural language processing with process modeling to address challenges in healthcare guidelines and regulatory adherence, emphasizing tool-driven approaches for real-world implementation. Analysis of his 2025 publications reveals a concentrated focus on enhancing business process compliance through declarative mining techniques and clinical pathway extraction, demonstrating cross-domain applicability in healthcare and regulatory technology. His work consistently bridges theoretical formal methods with practical software engineering solutions. Scientific recognition includes: Otto Mønsteds Fund: Travel grant (2015) As an academic supervisor, he mentors PhD candidate Caballero Villalobos, J. in the Immersive Process Mining project (2024-2027), while leading the DICE Center for Digital Compliance as Principal Investigator. This center drives research in computational compliance frameworks, regulatory technology, and laws-based system design through multi-institutional collaboration. His leadership in the DICE Center establishes a dedicated research ecosystem for digital compliance innovation, fostering interdisciplinary work on regulatory implementation and process verification methodologies.

Nis Sand JacobsenView profile

Researcher

• Fisheries Management
• Marine Ecosystems
• North Sea Ecology
• +7 more

Nis Sand Jacobsen is a Senior Researcher at the Technical University of Denmark (DTU), working within the National Institute of Aquatic Resources (DTU Aqua) in the Section for Ecosystem based Marine Management. Based in Kgs. Lyngby, Denmark, his research focuses on fisheries science, marine ecosystem dynamics, and sustainable management of aquatic resources. With over 50 publications and involvement in numerous research projects, Jacobsen contributes significantly to understanding fish population dynamics, particularly in the North Sea and Baltic Sea regions. Dr. Jacobsen's research interests center on ecosystem-based fisheries management, with particular expertise in North Sea and Baltic Sea ecosystems. His work examines fish stock dynamics, food web interactions, and the impacts of climate change on marine populations. He specializes in herring and sandeel fisheries, investigating spawning patterns, migration behaviors, and the effects of environmental changes on fish distribution. His research integrates ecological, socioeconomic, and management perspectives to develop sustainable approaches for fisheries management. Analysis of Jacobsen's recent publications reveals a strong focus on ecosystem-based approaches to fisheries management. His work increasingly incorporates climate change impacts, spatial management strategies, and multispecies interactions into stock assessment models. A notable trend is the development of integrated modeling frameworks that combine biogeochemical processes with fisheries dynamics, as seen in his work on the FEISTY library. His research also addresses the socioeconomic dimensions of fisheries management, examining how fishing industries adapt to changing fish distributions. Member of International Council for the Exploration of the Sea (ICES) since 2014 Active participant in multiple working groups including Herring Assessment Working Group (HAWG) As a research supervisor, Jacobsen serves as Main Supervisor for PhD student M. V. Strange, whose work focuses on cormorant and seal predation of fish in inner Danish waters. He coordinates multiple research projects including SOS-Fisk, which investigates the influence of cormorants and seals on fish stocks in Danish waters, and TRUST 2, which examines spatial stock structure for North Sea sandeel. His collaborative work extends to international efforts through the International Council for the Exploration of the Sea (ICES), where he has been an active participant since 2014. Dr. Jacobsen's laboratory and team work within the Section for Ecosystem based Marine Management at DTU Aqua focuses on integrating ecological, fisheries, and socioeconomic data to develop comprehensive management strategies. His team collaborates with Danish fisheries organizations, European research institutions, and international bodies to address challenges in sustainable marine resource management, particularly focusing on the impacts of climate change on fish distribution and the resulting management implications.

Anders NielsenView profile

Professor

• Biostatistics
• Stock Assessment
• Fish Population Dynamics
• +5 more

Anders Nielsen is a Professor at the National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, working within the Section for Marine Living Resources. His research focuses on the intersection of biostatistics, fisheries science, and marine ecology, with particular emphasis on developing statistically sound approaches for fish stock assessment and management. His research interests span biostatistics with a focus on modeling dynamical systems and state-space models, fish stock assessment methodologies, fish movement patterns, and statistical software development. Nielsen specializes in improving stock assessment models through better statistical frameworks, optimized sampling designs, and incorporation of spatial structure. His work on automatic differentiation in statistical tools has contributed significantly to computational approaches in fisheries science. Analysis of Nielsen's recent publications reveals strong trends in environmental DNA (eDNA) applications for fisheries monitoring, advanced statistical modeling for stock assessments, and integration of climate variables into fish population dynamics. His work bridges theoretical statistical development with practical fisheries management applications, particularly in North Sea and Baltic Sea ecosystems. The recurring themes include uncertainty quantification, retrospective analysis of assessment models, and development of robust frameworks for sustainable fisheries management. Nielsen actively participates in numerous research projects including MAKE-IT (sustainable mackerel management), FISHMAP (fish distribution and fisheries management), REVIVAL (herring stock assessment), CanMac (genetic approaches to mackerel assessment), and LenSAM (length-based assessment models). He serves as project coordinator for CanMac and LenSAM, demonstrating leadership in developing innovative assessment methodologies. His collaborative network spans multiple institutions and countries, reflecting the international nature of fisheries science. As a member of the International Council for the Exploration of the Sea (ICES), Nielsen contributes to international fisheries science and advice. His work directly supports UN Sustainable Development Goals related to sustainable marine resource management. While specific details about laboratory facilities aren't provided in the available information, his research clearly involves advanced computational modeling and analysis of fisheries data, likely utilizing high-performance computing resources at DTU.

• Tangible Interaction
• Music-making
• Digital Art
• +5 more

Halfdan Hauch Jensen is an academic staff member at the IT University of Copenhagen, affiliated with the Human-Computer Interaction and Design AIR Lab . His research focuses on tangible interaction, digital art, and music-making technologies for non-musicians. He explores how embodied interaction and deep generative models can enhance creative experiences through interdisciplinary design approaches. Active in Human-Computer Interaction and Digital Culture Published in top venues like NIME Conference and CHI Collaborates with researchers like Montesinos Garcia and Løvlie Research trends show emphasis on inclusive design , interactive music systems , and AI-driven creative tools . Current work investigates tangible interfaces for musical expression and generative models for art exploration.

Morten Opprud JakobsenView profile

Associate Professor

• Embedded Systems
• Tiny Machine Learning
• Condition Monitoring
• +2 more

Morten Opprud Jakobsen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University. His research focuses on condition monitoring of rotating machinery , with special emphasis on lubrication condition detection using acoustic signals and implementation of Embedded Machine Learning for predictive maintenance. Primary Research Areas: Embedded Systems, Tiny Machine Learning, Machine Diagnostics Key Techniques: MEMS sensors, Acoustic Emission Analysis, LoRa/Bluetooth Low Energy communication Recent work demonstrates technical expertise in developing low-cost condition monitoring systems that combine vibration and acoustic analysis for early failure detection in mechanical components. Publications in Mechanical Systems and Signal Processing and Tribology International highlight interdisciplinary applications of machine learning in mechanical engineering. Contact: morten@ece.au.dk | +45 21 36 70 53

Preben KidmoseView profile

Professor

• Biomedical Engineering
• Electrophysiology
• Electroencephalography (EEG)
• +6 more

Preben Kidmose is a Professor at Aarhus University, affiliated with the Department of Electrical and Computer Engineering and Biomedical Engineering under AU Engineering. He is a pioneer in ear-EEG and leads the Center for Ear-EEG and the Bioelectrical Instrumentation and Signal Processing research group, which operates within the Neurotechnology Lab. His research focuses on biomedical instrumentation, electrophysiology, and biomedical signal processing, particularly leveraging EEG data. He develops machine learning algorithms for physiological signals and designs wearable biomedical devices and medical systems. His work spans applications in sleep monitoring, auditory attention, hypoglycemia detection, and neurodegenerative disorders. While no explicit scientific awards or student mentees are listed in the provided text, his projects include significant initiatives like the Ear-EEG Sleep Monitor (2017–2021), Sleep after Stroke (2020–2024), and ongoing studies on cognitive impairment and neurodegenerative diseases. His lab explores system engineering solutions for medical devices and applies EEG/signal processing to clinical challenges such as Alzheimer’s and epilepsy research.

Jakob Juul LarsenView profile

Associate Professor

• Signal Processing
• Machine Learning
• Geophysics
• +3 more

Jakob Juul Larsen is an Associate Professor in the Department of Electrical and Computer Engineering at Aarhus University, specializing in Signal Processing and Machine Learning for geophysical applications. His research focuses on developing advanced instrumentation and data processing techniques for groundwater resource characterization. His core research domains include: Signal Processing for geophysical instrumentation Machine Learning applications in geophysics Hydrogeophysics and groundwater mapping Surface Nuclear Magnetic Resonance (SNMR) methods Transient Electromagnetic (TEM) systems Deep learning for electromagnetic data interpretation Dr. Larsen leads multiple significant research initiatives: SuperTEM (2021-2024): Monitoring groundwater resources for sustainable exploitation Surface NMR with long excitation pulses (2021-2022) Flood and Drought – Tracking water in shallow subsurface (2019-2023) GIRem – Guided Injection Remediation (2018-2022) MapField – Field-scale nitrogen management (2018-2021) Abzu – Advanced surface NMR instrumentation (2016-2019) Faster SNMR measurements with new receiver technology (2016-2017) Controlling Sound Zones signal processing (2015-2018)

• Biomedical Engineering
• Medical Imaging
• Nanoparticle Drug Delivery
• +3 more

Thomas Nielsen is an Associate Professor at the Department of Electrical and Computer Engineering, Aarhus University, specializing in Biomedical Engineering. His research bridges medical instrumentation development and clinical oncology applications, with particular expertise in diagnostic imaging technologies. Research Focus: Medical imaging innovation including advanced MRI techniques (hyperpolarized 13 C, 19 F, DCE-MRI) and photoplethysmography for cancer diagnostics Nanoparticle engineering for targeted drug delivery and contrast enhancement Tumor microenvironment analysis focusing on vasculature, hypoxia, and metabolic pathways Development of theranostic platforms combining diagnostics and therapeutics Publication Analysis: His recent work (2013-2022) demonstrates consistent focus on oncology imaging, with 73% of publications involving cancer diagnostics. Key methodological themes include nanoparticle-based contrast agents (33%), MRI technique development (40%), and photoplethysmography applications (13%). Research outputs frequently involve interdisciplinary collaboration across engineering, materials science, and clinical oncology. Projects: Directed three major research initiatives: F127-Folate coated SPION for cancer detection (2014) Chitosan coated PLGA PFOB nanoparticles for cell migration tracking (2013-2014) PLGA-PEG-Folate multimodal nanoparticles for combined MRI/NIR imaging and chemotherapy (2011-2014) Laboratory Affiliations: Conducts research through the Biomedical Engineering laboratories within the Department of Electrical and Computer Engineering, utilizing facilities for nanoparticle synthesis, optical imaging, and magnetic resonance experimentation.

Ioana O. BerceaView profile

Assistant Professor

• Data Structures
• Randomized Algorithms
• Approximation Algorithms
• +1 more

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."

• Programming Languages
• Formal Logic
• Software Verification
• +5 more

Jesper Bengtson is a Lecturer in Theoretical Computer Science specializing in Programming Logic and Semantics at the IT University of Copenhagen. His research primarily focuses on formal methods for programming languages, with particular expertise in session types, separation logic, and proof assistants. Dr. Bengtson's research interests center around programming language theory and formal verification. His work explores how to mathematically verify software correctness through logical frameworks. He specializes in session types for communication protocols, separation logic for reasoning about concurrent programs, and developing tools for interactive theorem proving. His research bridges theoretical computer science with practical applications in software verification. His publication record shows consistent contributions to programming language theory over the past decade, with a clear trend toward applying formal methods to concurrent and distributed systems. Recent work demonstrates increasing focus on mechanized verification of session type systems and integration of session types with separation logic for concurrent program verification. Dr. Bengtson has been involved in several significant research projects including MECHANIST (MECHANIsation of Session typEs, 2021-2025), ToMeSo (Tools and Methods for Scalable Software Verifications, 2009-2013), and a general-purpose framework for software verification (2013-2017). These projects were funded by major Danish research foundations including Danmarks Frie Forskningsfond and Independent Research Foundation of Denmark. His work has been featured in media outlets, including a 2015 article titled 'Danish researcher wants to eradicate software errors' and a 2021 piece about 'Natural or woke? Big shooter introduces controversial soldier,' indicating public engagement with his research.

Veronika CheplyginaView profile

Professor

• Machine Learning
• Medical Image Analysis
• Pattern Recognition
• +4 more

Veronika Cheplygina is a Professor at the IT University of Copenhagen and leads the Data-intensive Systems and Applications research group within the Pattern Recognition Revisited lab. Her work focuses on Machine Learning , Medical Image Analysis , and Pattern Recognition , with particular emphasis on Open Science practices and Bias in Medical AI . Primary Affiliation: Department of Data Science and Artificial Intelligence Research Metrics: 20 publications, 2 projects (2021-2025), 2 datasets Her research addresses critical challenges in medical imaging datasets, including annotation bias , data reuse , and fairness in AI through active collaboration with institutions across 5 countries. She has contributed to major conferences like NeurIPS and MICCAI. Scientific Funding & Recognition: Principal Investigator (PI) for Making Metadata Count (MMC) (Danmarks Frie Forskningsfond, 2022-2025) PI for Choosing a Transfer Source for Medical Image Classification (CATS) (Novo Nordisk Foundation, 2021-2025) She maintains an active role in academic governance as head of an assessment committee for faculty recruitment (2023) and has been featured in media discussions about medical AI practices.

Lars DuggenView profile

Researcher

• 3D Printing
• Piezoelectric Engineering
• Mechatronics
• +3 more

Lars Duggen is a researcher and Head of Unit at SDU Mechatronics Center, University of Southern Denmark, specializing in advanced manufacturing, sensor systems, and mechanical design. His work bridges 3D printing, piezoelectricity, and industrial mechatronics. Institute of Mechanical and Electrical Engineering, University of Southern Denmark Head of Unit, SDU Mechatronics Research Focus: 3D printing of multi-material systems, piezoelectric gas sensors, microcantilever dynamics, and hybrid flexible electronics. Recent projects explore nonlinear sensor behavior, fault diagnosis algorithms, and biomedical applications like non-invasive glucose monitoring. Scientific Contributions: Key articles address machining forces in multi-material printing, functionalization layer modeling, and transfer learning for machinery diagnostics. His work has been cited in fields spanning mechanical engineering, materials science, and sensor technology. Honored with BHJ-priser (2022) Recognized in Sønderjylland research excellence (2022) Teaching & Supervision: Supervises mechatronics projects including fluid behavior modeling and mechanical component design. Collaborates on EU-funded initiatives like EUROFusion BB RH facility and Medical Microtechnology.

• Battery Components
• Lattice-Boltzmann Method
• Nanoscale Engineering
• +4 more

Martin Lautenschläger is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark (SDU Mechatronics), specializing in computational modeling and simulation of advanced engineering systems. His work bridges mechanical engineering and material science through high-performance computing, focusing on battery technologies, fluid dynamics, and nanoscale interactions. Academic Rank: Associate Professor Institution: University of Southern Denmark Research Focus: Battery microstructures, Lattice-Boltzmann methods, nanoscale lubrication, and multiphase flow simulations Research Trends: Recent publications emphasize pore-scale modeling of battery components, electrolyte filling processes, and multiscale approaches to optimize battery performance. His simulations span from molecular dynamics to continuum-level predictions, particularly in lithium-ion and lithium-sulfur systems. Collaborations: Active in interdisciplinary projects with institutions across Europe, focusing on energy storage, computational microstructure analysis, and manufacturing optimization. Teaching: Supervises master's and bachelor's theses in mechanical design, fluid dynamics, and battery technologies, while teaching core courses in mechanical engineering.

Hao LiView profile

Research Fellow

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +2 more

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.

Ivar LundView profile

Associate Professor

• Agricultural Engineering
• Precision Agriculture
• Spray Technology
• +4 more

Ivar Lund is an Associate Professor at the University of Southern Denmark (SDU) in the Department of Industrial and Civil Engineering. His work focuses on agricultural engineering, precision spraying, and thermal energy systems with phase change materials. Education: B.Sc. in Engineering (Agro) from The Engineering College of Horsens (1982) Postgraduate courses at Royal Veterinary and Agricultural University (1999, 2006), University of Aalborg (2003-2004), and Ph.D. from University of Copenhagen (2008) His research spans robotic spraying systems for precision agriculture, fluid dynamics of droplet transport, and thermal energy storage using phase change materials (PCMs). Recent work includes experimentation and numerical modeling of SCR spray droplets, performance analysis of PCM-based building cooling systems, and confidence interval studies for spray measurement data. Key trends in his publications (2014-2023) include: reducing spray drift in pesticide application, optimizing urea-water solution distribution for automotive emissions control, and PCM integration in sustainable building systems. Collaborations highlight partnerships with institutions like UC Davis and University of Aarhus. Lund actively contributes to conferences, including presentations on spray drift modeling (2018), agricultural spray analysis (2018), and micro-jet herbicide application (2018). His work bridges engineering innovation with practical agricultural and energy efficiency solutions.