Find Academic Supervisors

Malte PedersenView profile

Research Fellow

• Computer Vision
• Underwater Imaging
• Marine Organism Identification
• +5 more

Malte Pedersen is a Postdoctoral Researcher at Aalborg University's Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design. He works in the Section for Media Technology at Campus Aalborg, focusing on visual analysis and perception with applications in marine and maritime research. His work is associated with several research initiatives including "Visual Analysis and Perception," "AI for the People," and "AAU BLUE – Marine & Maritime Research." Dr. Pedersen's research spans the intersection of computer vision and marine science, with a particular emphasis on underwater imaging and analysis. His work addresses challenges in turbid water environments, marine organism identification, and fisheries monitoring. He has developed several important datasets for underwater computer vision research, including BrackishMOT for multi-object tracking of marine species and 3D-ZeF for zebrafish tracking. His recent publications demonstrate a strong focus on applying computer vision techniques to solve practical problems in marine environmental monitoring, fisheries management, and underwater exploration. His research shows a clear trajectory toward developing robust AI systems that can operate effectively in challenging underwater conditions, with applications ranging from seagrass mapping to bycatch identification and automated fisheries monitoring. Computer Vision for underwater environments Marine organism re-identification Benthic habitat mapping Fisheries monitoring and management Object tracking in turbid water Underwater image processing Dr. Pedersen has secured significant research funding as Principal Investigator on multiple projects including FDF (Fully Documented Fisheries, 2024-2026), JAMBO (investigating seabed conditions in Jammerbugt, 2023-2024), AUTOFISK (automated monitoring of cod catch, 2022-2023), and Marine Analytics using Computer Vision (2020-2023). His work has contributed to marine research that has informed policy decisions, including research that "Paves the Way for a Ban on Beam Trawling."

• Computational materials discovery
• 2D materials
• Quasiparticles
• +3 more

Kristian Sommer Thygesen is a Professor and Section Leader at the Department of Physics , Technical University of Denmark (DTU), leading the Computational Atomic-scale Materials Design (CAMD) group. Research Interests: Dr. Thygesen specializes in first-principles methods for materials properties, with ongoing projects in: Quantum emitters in 2D materials Van der Waals heterostructures Machine learning for materials discovery Many-body perturbation theory Supervision: Actively supervises multiple PhD students in projects related to computational materials design and quantum technologies.

Christian Møller DahlView profile

Professor

• Theoretical Statistics
• Time Series Analysis
• Nonparametric Estimation
• +7 more

Christian Møller Dahl is a Professor of Economics at the University of Southern Denmark (SDU), affiliated with the Department of Economics and the Interdisciplinary Centre on Population Dynamics (CPop). His research focuses on theoretical statistics, time series analysis in finance, nonparametric estimation, machine learning, and causal inference. He leads the Big Data Analytics and Digitization (BDAD) research unit, specializing in automated digitization of historical documents like census records and death certificates. His work bridges economics, demography, and computer science, with applications in understanding long-term mortality patterns and economic cycles. Education details are not explicitly stated, but CMD has extensive experience in interdisciplinary projects involving national archives and health institutions. His research agenda includes developing deep learning techniques for transcribing historical data, such as 20 million Danish death certificates, to aid demographers and epidemiologists. He has secured funding from Danish Research Foundations for projects like the Copenhagen Infant Health Nurse Records (CIHNR) cohort, studying early-life health policies and long-term outcomes. Key collaborations include work with national archives on citizen science strategies for digitizing health data and interdisciplinary projects on occupational standardization using machine learning (e.g., OccCANINE). CMD’s methodological contributions include advancements in random fields, neural networks, and GARCH models for financial time series analysis. Grants: Multiple grants from Danish Research Foundations for digitization and health studies. Labs/Teams: BDAD unit (Big Data Analytics and Digitization) and CPop (Interdisciplinary Centre on Population Dynamics). Future Work: Expanding digitization of historical health records and exploring multi-generational health impacts.

Alexandra BoltassevaView profile

Professor

• Nanophotonics
• Plasmonics
• Optical Metamaterials
• +3 more

Alexandra Boltasseva is a Professor of Electrical and Computer Engineering and Materials Engineering at Purdue University, affiliated with the Birck Nanotechnology Center and the Purdue Quantum Science and Engineering Institute. A leading expert in nanophotonics and plasmonics, her research focuses on optical metamaterials, nanofabrication, and quantum information technologies. She has held editorial roles at Optical Materials Express and is recognized for advancing machine-learning-assisted photonic design. Moscow Institute of Physics and Technology (B.Sc., M.Sc.) Technical University of Denmark (Ph.D. in nanophotonics) Her research explores the discovery and optimization of nanophotonic structures, including plasmonic materials and metamaterials, for applications in quantum information, on-chip circuitry, and sustainable technologies. Key themes span low-loss photonic devices, tunable optical platforms, and nonlinear optical phenomena. Recent publications emphasize quantum photonics, machine learning for optical design, 2D materials, and time-varying photonic systems. Her work bridges fundamental physics and practical implementation across quantum computing, sensing, and energy conversion. Awarded by multiple societies, including: Fellow of the American Physical Society (2023) Fellow of the Materials Research Society (2021) Fellow of the National Academy of Inventors (2020) Blavatnik National Award Finalist (2018) IEEE Photonics Society Young Investigator (2013) Boltasseva leads the development of platforms for nanophotonic applications, with significant contributions to silicon nitride quantum emitters, plasmonic color printing, and photonic time crystals. Her lab’s work integrates experimental and theoretical approaches for next-generation photonic technologies.

Naomi HallasView profile

Professor

• plasmonics
• biomedical engineering
• optoelectronics
• +9 more

Naomi Hallas is a Professor at Rice University, holding appointments in the Department of Electrical and Computer Engineering, Biomedical Engineering, Chemistry, and Physics. Her pioneering work in plasmonics bridges disciplines, with applications in biomedicine, optoelectronics, chemical sensing, photocatalysis, and solar energy sustainability. Her research spans plasmonic photothermal therapies for cancer (via Nanospectra Biosciences) antenna-reactor photocatalysts for industrial processes solar energy conversion for off-grid water purification and desalination machine learning integration in chemical detection surface-enhanced Raman spectroscopy advancements Key scientific contributions include over 30 years of fellowships in societies like APS, OSA, IEEE, SPIE, MRS, AAAS, NAI , and leadership in translating nanotechnology to clinical and environmental solutions. Recent publications highlight trends in plasmonic-enhanced photocatalysis for methane reforming and pollutant degradation machine learning for SERS and environmental analysis stretchable metamaterials and vacuum ultraviolet light generation biomedical applications of gold nanoshells and nanomatryoshkas Scientific awards and honors include Elected Fellow of American Physical Society (APS) Fellow of Optical Society of America (OSA) Fellow of IEEE, SPIE, MRS, AAAS, and National Academy of Inventors (NAI)

Esben Pilgaard SkovsenView profile

Associate Professor

• Physics
• Optics
• Spectroscopy
• +12 more

Esben Pilgaard Skovsen is an Associate Professor in the Department of Materials and Production at Aalborg University, Faculty of Engineering and Science. His research is centered on terahertz technology, optics, and spectroscopy, with applications in material characterization, remote sensing, and security. He is actively involved in leading research projects and has a strong record of scientific output and collaboration. Education: PhD in Chemistry (Time-Resolved Spectroscopy), Aarhus University (Awarded 20 Dec 2005) M.Sc. in Physics, Aarhus University (Awarded 7 May 2002) Postdoctoral position, Aarhus University (Oct 2005 – Nov 2005) His research interests span terahertz spectroscopy , nonlinear optics , femtosecond laser physics , and material analysis . He has pioneered work in standoff detection of explosives (DETRIS project), continuous-wave terahertz systems , and novel dosimetry using polymers . His recent publications show a trend toward integrating machine learning for spectral classification and improving terahertz source efficiency. His work often bridges fundamental physics with engineering applications. Scientific Awards and Recognition: No formal awards listed, but recognized through media coverage and research funding. Skovsen actively supervises student projects and contributes to academic service through peer review (e.g., for Sensors journal), internal and external examinations, and consultancy (e.g., for the German Research Council). He has led multiple research grants, including industry collaborations and national projects. He is a Principal Investigator on projects such as DETRIS and CITS, focusing on compact terahertz systems and explosive detection. He also contributes to open science through public datasets on Zenodo. His lab work emphasizes experimental terahertz setups, purge systems, and fiber-coupled instrumentation for improved stability and portability.

Daniel Haugård OlesenView profile

Associate Professor

• Geodesy
• Earth Observation
• GNSS (Global Navigation Satellite Systems)
• +13 more

Daniel Haugård Olesen is an Associate Professor at DTU Space, Technical University of Denmark, within the Department of Space Research and Technology, specializing in Geodesy and Earth Observation. He leads the GNSS research group, which focuses on advanced navigation technologies, including interference detection, precise positioning, and sensor fusion for drones and robots. Institution: Technical University of Denmark (DTU) School: DTU Space Department: Department of Space Research and Technology Research Division: Geodesy and Earth Observation (GEO) Role: Associate Professor and Head of GNSS Research Group His research interests span GNSS signal integrity, space weather impacts, RTK/PPP positioning, multi-sensor fusion, and applications in environmental monitoring using unmanned systems. He actively contributes to hydrological studies through drone-based river monitoring, leveraging LiDAR, sonar, radar, and GNSS-R techniques. The recent publication trends reflect a strong focus on applying UAS and GNSS technologies to solve real-world environmental challenges, particularly in river hydraulics, surface water dynamics, and soil moisture sensing. His work integrates space-based observation with autonomous platforms, emphasizing reliability, precision, and scalability. Keywords across articles include Earth Observation, Remote Sensing, Hydrology, Navigation, and Signal Processing, with specific subfields such as GNSS interference mitigation, Doppler radar velocimetry, and ionospheric scintillation analysis. Daniel Olesen supervises several PhD projects, indicating an active mentoring role in training next-generation researchers. Notable projects include GNSS-R from UAS, semi-autonomous navigation for river monitoring, and ionospheric effects on GNSS signals. While no specific scientific awards are listed in the provided text, his sustained research output and leadership in key projects suggest recognition within the scientific community. GNSS Reflectometry (GNSS-R) from Unmanned Aerial Systems (2024–2027) Semi-autonomous navigation of UAS for river monitoring (2023–2026) Ionospheric effects on GNSS signals (2020–2024) GNSS Jamming Detection and Localization (2019–2024) Relative positioning and attitude from UAVs (2017–2021) He leads the GNSS research group at DTU Space, which operates multiple state-of-the-art GNSS CORS networks and develops cutting-edge methods for reliable positioning in challenging environments. The team works at the intersection of geodesy, space science, and robotics, fostering interdisciplinary collaboration to advance autonomous navigation and Earth observation capabilities.

Andreas Rønne StokholmView profile

Researcher

• AI
• Earth Observation
• Satellite Data
• +6 more

Andreas Rønne Stokholm is a Postdoctoral Researcher at DTU Space, the National Space Institute of the Technical University of Denmark, specializing in the Department of Space Research and Technology with focus areas in Geodesy and Earth Observation. He previously completed his Ph.D. at the same institution in 2024 and served as a Visiting Researcher at the European Space Agency's (ESA) ϕ-lab, where he contributed to innovative research combining AI with satellite data for Earth monitoring. His research interests center on applying artificial intelligence to Earth observation data, particularly for sea ice mapping in the Arctic. He focuses on developing automated systems using satellite data from Synthetic Aperture Radar (SAR) to replace manual sea ice charting processes. His work has significant implications for maritime navigation safety in polar regions and enabling Northern Trade Routes through improved ice monitoring. He specializes in machine learning applications for cryosphere studies, quantum technology applications in gravimetry, and dynamic sensor systems for atmospheric phenomena. Stokholm actively contributes to multiple research projects including greenhouse gas emission monitoring using satellite remote sensing and multi-approach methane emissions monitoring for the solid waste sector, where he serves as a supervisor for Ph.D. students. His research aligns with UN Sustainable Development Goals related to climate action and life below water. As part of his professional activities, Stokholm collaborates with international research teams across Europe and has contributed to significant datasets including Aurora DVS observations. His work bridges the gap between advanced computing, satellite technology, and practical applications for societal benefit in polar regions and climate monitoring.

Yaman GökselView profile

Researcher

• Surface-Enhanced Raman Spectroscopy
• Therapeutic Drug Monitoring
• Biosensors
• +3 more

Yaman Göksel is a Postdoctoral Researcher at the Department of Health Technology, Drug Delivery and Sensing at the Technical University of Denmark (DTU). His work focuses on Surface-Enhanced Raman Spectroscopy (SERS) for therapeutic drug monitoring of methotrexate in clinical settings. He has contributed to the development of label-free SERS assays and advanced spectral data analysis techniques for accurate drug quantification in patient samples. Research Trends : Recent publications highlight his expertise in SERS miniaturization, chemometric integration, and clinical validation. Key themes include biomedical sensor development , low-power diagnostic tools , and NASA-inspired optical engineering for portable health monitoring systems. Collaborations : Göksel collaborates closely with Professor Anja Boisen's group at DTU, working on microfluidic platforms and SERS-based biosensors. His research intersects analytical chemistry , pharmaceutical science , and biomedical engineering .

Lezhong WangView profile

Researcher

• Computer Science
• Artificial Intelligence
• Machine Learning
• +3 more

Lezhong Wang is a postdoctoral researcher at the Department of Applied Mathematics and Computer Science (DTU Compute), specializing in Visual Computing at the Technical University of Denmark. His work bridges computational imaging, material science, and generative modeling. Education : Ph.D. in Material Appearance and Perception: Estimating and Generating Properties Across Scales (2025, DTU) Research Interests : Dr. Wang focuses on image synthesis , diffusion models , and noninvasive material characterization . His projects include anisotropy estimation in cheese , stereo image generation , and machine learning applications for optical imaging . Publication Trends : His recent work emphasizes training-free generative models , material anisotropy , and cross-scale property estimation . Themes include computer vision , diffusion-based image synthesis , and machine learning for material analysis . Projects : He completed a Ph.D. project titled Computer Vision for Estimating Microstructure Using Optical Models (2022-2025), supervised by Jeppe Frisvad and Anders Dahl.

Thiusius Rajeeth SavarimuthuView profile

Professor

• Computer Vision
• Robotics
• Artificial Intelligence
• +3 more

Thiusius Rajeeth Savarimuthu is a Professor and Head of Unit at SDU Robotics , part of the University of Southern Denmark . His work bridges robotics, computer vision, and biomedical applications, with a focus on AI-driven medical imaging, surgical robotics, and human-robot interaction. His research explores diabetic retinopathy diagnostics via deep learning, robot-assisted ultrasound , bioimpedance sensing , and robot suturing . Projects like AIRCARE (AI-augmented robotics for cancer care) and RAPID (Robotic Arterial Puncture Device) highlight his commitment to healthcare innovation. Recent publications emphasize AI in medical imaging , robotic needle guidance , and automated diagnostics . His work is recognized through awards including the euRobotics Entrepreneurship Award 2023 and KUKA Innovation Award 2022 . Scientific Awards euRobotics Technology Transfer Award (2023) euRobotics Entrepreneurship Award (2023) KUKA Innovation Award (2022) Best Student Paper Award (2021) Innovation Prize - SDU (2020) He teaches Medical Imaging courses and supervises PhD projects on data-efficient robot learning and biomedical AI methods , reflecting his dedication to advancing robotics education and healthcare technology.

Kenichi OhkiView profile

Professor

• Visual Neuroscience
• Functional Brain Mapping
• Neural Circuit Development
• +3 more

Kenichi Ohki is a Professor in the Department of Physiology at the Graduate School of Medicine, The University of Tokyo, and serves as Deputy Director and Principal Investigator at the International Research Center for Neurointelligence (IRCN). His research focuses on visual neuroscience and functional brain mapping, with particular emphasis on understanding how the visual cortex processes information at cellular resolution. Education: 1990-1996: Medical degree (MD) from Faculty of Medicine, The University of Tokyo 1996-2000: PhD in Medicine from Department of Physiology, The University of Tokyo 1996: Visiting scholar at Department of Brain and Cognitive Sciences, MIT Dr. Ohki is renowned for developing single-cell resolution functional mapping with two-photon calcium imaging in 2005, which revolutionized the understanding of visual cortex architecture. His work has revealed fundamental principles of how orientation selectivity emerges in visual neurons and how developmental programs interact with neural activity to shape cortical function. His laboratory continues to investigate the interplay between innate developmental circuits and neuronal activity in determining cortical function using advanced imaging and computational techniques. Analysis of Dr. Ohki's publications shows a consistent progression from foundational work on visual cortex organization to understanding developmental mechanisms and more recently, integrating neuroscience with artificial intelligence approaches. His recent work demonstrates increasing sophistication in analyzing neural circuits, with growing emphasis on computational modeling and cross-disciplinary approaches that bridge neuroscience and AI. Scientific Recognition: Multiple high-impact publications in Nature, Nature Neuroscience, and other top journals Development of innovative techniques for functional brain mapping Leadership as Deputy Director of IRCN, a major international research center Selection for Beyond AI Institute's mid- to long-term research project Regular recognition of laboratory members with research awards Dr. Ohki actively mentors a diverse research team including project assistant professors, postdoctoral researchers, and graduate students. His laboratory has received significant institutional support for research on visual neuroscience and neural circuit development. Several of his students have received prestigious awards at major neuroscience conferences in Japan, demonstrating his effective mentorship and the quality of research conducted in his laboratory. The Ohki Laboratory operates within the Department of Physiology at the University of Tokyo and is affiliated with the International Research Center for Neurointelligence (IRCN). The lab utilizes advanced techniques including in vivo two-photon calcium imaging, optogenetics, and computational modeling to investigate visual information processing in the mammalian brain. The research team consists of scientists with diverse expertise in neuroscience, imaging technology, and computational analysis, creating a highly collaborative and interdisciplinary research environment focused on understanding the fundamental principles of visual processing in the brain.

• Mathematical Image Analysis
• Variational Methods
• Partial Differential Equations
• +6 more

Francois Bernard Lauze is an Associate Professor at the Department of Computer Science, University of Copenhagen, specializing in the Image Analysis, Computational Modelling and Geometry section. His research bridges mathematical image analysis and geometric methods for shape analysis in medical imaging. His primary research interests include variational methods, partial differential equations, and Riemannian geometry applied to image inpainting, motion estimation, segmentation, and medical imaging. Recent work focuses on physics-integrated deformable registration, SE(3) group equivariant neural networks, and multi-view 3D reconstruction techniques. The trends in his publications highlight interdisciplinary approaches combining geometry, computer vision, and deep learning. Notable examples include locally orderless networks, SE(3) group convolutions for DWI segmentation, and RNb-NeuS for 3D reconstruction. He is affiliated with the Pioneer AI section at DIKU, which anchors foundational research in artificial intelligence, machine learning, and medical imaging. The section hosts the Applied Geometry lab and participates in major international conferences like MICCAI and CVPR.

• Natural Language Processing
• Multimodal Understanding
• Machine Translation
• +3 more

Israfel Salazar is a Research Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Natural Language Processing research group . His work focuses on multimodal understanding and representation, bridging natural language processing, machine translation, computer vision, genomics, and historical data analysis. Email : israfel.salazar@di.ku.dk Phone : +4535337068 Research interests include: Culturally aware multimodal machine translation Massively multilingual vision evaluation Genomic offset statistics Temporal data extraction from historical documents His recent publications highlight interdisciplinary approaches combining language and vision systems, statistical modeling for biological data, and historical record analysis. He contributes to benchmarking frameworks and evaluation methodologies for modern AI applications. Israfel's work spans both foundational and applied research domains, with a focus on cross-modal learning, computational methods in bioinformatics, and automated analysis of historical archives. His projects often require hybrid techniques integrating linguistic and visual modalities. He operates within the Natural Language Processing section at DIKU, which also hosts research into probabilistic programming applications in bioinformatics, computer vision integration with NLP, and energy-efficient computational frameworks. Collaborations with other DIKU groups (e.g., FUTHARK for GPU acceleration, quantum programming) may inform his technical implementations.

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +4 more

Adrián Avelino Sousa-Poza is a Researcher at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its applications across diverse domains including Artificial Intelligence , Medical Data Analysis , and Quantum Computing . His research intersects with the SCIENCE AI Centre , focusing on both theoretical and applied aspects of machine learning. Recent work explores environmentally sustainable AI , quantum-enhanced models , and cross-cultural adaptation systems , reflecting his interdisciplinary approach. Key themes in his publications include large language models , quantum computing applications , and healthcare informatics , with a particular emphasis on interpretability , fairness , and hardware optimization in AI systems.