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.
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.
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
Yan Cheng is a Guest Researcher in the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. A recently minted PhD graduate (awarded May 2025), Cheng specializes in environmental science and computer science with expertise in deep learning applications for earth observations and terrestrial ecosystem analysis under climate change. PhD in AI for Earth Observation, University of Copenhagen (2021-2025) MSc in Natural Resources Management, University of Twente (2017-2019) BSc in Geographical Information Science, Wuhan University (2013-2017) Cheng's research integrates remote sensing, climate science, and artificial intelligence to address critical environmental challenges. Primary focus areas include tree mortality mapping, climate risk assessment for infrastructure, and phenology analysis of vegetation cycles. Their work leverages high-resolution satellite imagery (PlanetScope, Sentinel-2) and develops novel computer vision techniques for ecosystem monitoring. Cheng maintains active collaborations across multiple continents, with recent visits to ETH Zürich, Wuhan University, Southern University of Science and Technology, and USGS. Notable research outputs demonstrate Cheng's leadership in developing frameworks for multi-hazard climate risk assessment and creating the deadtrees.earth database initiative. Their publications span high-impact journals including Nature Communications, Remote Sensing of Environment, and Communications Earth and Environment, with significant attention from both academic and policy communities. DDSA Travel Grant 2023 IEEE Geoscience and Remote Sensing Society Travel Grant ITC Excellence Scholarship Outstanding PhD Students Award (2024) Cheng has served as teaching assistant for the MSc course 'Satellite Image Processing and Analysis in the Big Data Era' and maintains external positions including Remote Sensing Specialist (2022-2024). Their research has garnered significant attention with multiple news outlets, policy references, and substantial social media engagement across X, Reddit, and Bluesky platforms. Through the deadtrees.earth initiative, Cheng leads a global collaborative effort to build an open-access database for centimeter-scale tree mortality monitoring, demonstrating commitment to open science and collaborative environmental research.
Michael Brun Andersen serves as a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen's Faculty of Health and Medical Sciences. His work is centered at the Radiology division (Blegdamsvej 3, Copenhagen), with active research output extending into 2025. His institutional email is michael.brun.andersen@regionh.dk and professional profile hosted via University of Copenhagen's domain (ikm.ku.dk). His research spans advanced medical imaging applications with emphasis on AI-driven diagnostic enhancement , oncology imaging protocols , and quantitative analysis of CT/MRI artifacts . Key focus areas include photon-counting CT optimization for prostate cancer radiotherapy, motion artifact mitigation in stroke MRI, and pulmonary nodule surveillance systems. Recent work demonstrates integration of reinforcement learning for pancreatic duct identification and validation of handheld ultrasound in emergency settings. Analysis of his 2024-2025 publications reveals strong collaborative networks across European radiology and oncology research groups. His work frequently addresses clinical implementation challenges of AI in diagnostics, spectrum bias in deep learning models, and functional imaging biomarkers for immunotherapy monitoring. Notable contributions include feasibility studies on DCE-CT parameters in lung cancer therapy and investigations into incidental finding management protocols. While no formal awards or student advisement details are documented in the provided materials, his research demonstrates consistent peer-reviewed output in high-impact journals including Acta Oncologica , European Radiology , and Cancer Imaging . His work shows significant attention from clinical communities with multiple publications referenced in news outlets and Mendeley readership.
Birthe P Møller Jespersen is an Associate Professor and External Researcher in the Department of Food Science, specializing in Food Analytics and Biotechnology at the University of Copenhagen. Her research focuses on applying advanced analytical techniques to food science problems, particularly in cereal analysis and functional foods. Dr. Jespersen's research interests include: Food Analytics using hyperspectral imaging and near-infrared spectroscopy Barley and cereal analysis for food and brewing applications Functional foods development Application of machine learning and deep learning in food analysis Protein analysis in plant-based foods Food processing and quality assessment Her recent publications demonstrate a strong trend toward integrating advanced computational methods with traditional food analysis techniques. She has been actively working on applying hyperspectral imaging combined with machine learning algorithms to analyze grain quality, protein content, and food processing parameters. Her research bridges the gap between traditional food science and modern data science approaches. Dr. Jespersen has contributed to the scientific community through numerous publications and media appearances discussing food science topics. Her media contributions include discussions on converting animal feed to human food and the importance of grain fibers in healthy diets. Her professional activities include: Research in food analytics and biotechnology Development of analytical methods for food quality assessment Application of advanced imaging and spectroscopy techniques Integration of machine learning with food analysis
Stefano Cerri serves as a Guest Researcher in the Department of Computer Science at the University of Copenhagen, affiliated with Pioneer AI (P1AI). His work bridges computer science and biomedical applications through advanced imaging techniques. His research focuses on medical image analysis, particularly in neuroimaging and AI-driven diagnostic tools. Cerri specializes in developing computational methods for analyzing brain structures using heterogeneous MRI data, with emphasis on clinical applications and portable imaging systems. His work integrates machine learning with medical diagnostics to address challenges in neurological assessment. Cerri's publication activity demonstrates strong engagement with interdisciplinary biomedical research, particularly in adapting AI techniques for low-field portable MRI systems. His work shows particular relevance for resource-limited clinical settings where traditional high-field MRI is unavailable. Based at Øster Voldgade 3 in Copenhagen, Cerri collaborates with international researchers across multiple institutions as evidenced by his co-authorship network spanning diverse geographical regions.
Fabian Cristian Gieseke serves as an Associate Professor in the Department of Computer Science at the University of Copenhagen's Faculty of Science. His research focuses on applying machine learning techniques to environmental monitoring and remote sensing applications. He maintains an active research profile with significant contributions to the field of computational environmental science. Dr. Gieseke's research interests center on Machine Learning, Deep Learning, and their applications in Remote Sensing and Environmental Monitoring. His work particularly emphasizes forest resource monitoring, tree detection, and biomass estimation using advanced AI techniques. He has developed innovative approaches for analyzing satellite and aerial imagery to address critical environmental challenges related to forest management and climate change. His recent publications demonstrate a strong trend toward applying deep learning models to environmental monitoring problems, particularly in forest ecosystems. His research spans from national-scale tree counting and crown segmentation to above-ground biomass estimation using LiDAR data. His work shows increasing interdisciplinary collaboration between computer science and environmental science communities, with publications appearing in high-impact journals across both domains. Dr. Gieseke maintains an active research profile with 76 total research outputs as documented, including 41 article in proceedings, 24 journal articles, and various other publication types. His work has received significant attention with mentions across multiple social media platforms, news outlets, and policy documents, indicating real-world impact of his research. Based at the Department of Computer Science (DIKU) at the University of Copenhagen, Dr. Gieseke's research appears to be part of broader environmental informatics initiatives that bridge computer science with ecological monitoring and climate science applications.
Rikke Munck Petersen is an Associate Professor in Landscape Architecture and Sensory Conception at the University of Copenhagen's Department of Geosciences and Natural Resource Management. She leads research in sensory landscape methodologies and co-organizes the Copenhagen Landscape Lectures series as part of the 'Landscape Architecture and Urbanism' and 'Spatial Change and Planning' research groups. Her educational background includes a PhD (2010) and Cand. Arch. (2000) from the Royal Danish Academy of Fine Arts, School of Architecture, supplemented by studies at the University of Pennsylvania (1999) and Royal Agricultural University of Denmark (1998). PhD, Royal Danish Academy of Fine Arts, School of Architecture (2010) Cand. Arch., Royal Danish Academy of Fine Arts, School of Architecture (2000) Non-degree studies, University of Pennsylvania (1999) Plant use course, Royal Agricultural University of Denmark (1998) Her research pioneers sensory analysis and filmic mediations in landscape transformation, focusing on the body's sensorium, affective experience, and performativity in climate-resilient design. Current projects include multi-sensory river valley analysis (Archive for Becomings, 2025-27) and EU Water4All's INTERLAYER project (2024-27) developing 'slow hydrology' approaches for flood/drought resilience. Her methodology integrates drone footage, film editing, and participatory processes to stimulate sensory awareness in landscape planning. Recent publications reveal strong interdisciplinary convergence between landscape architecture, film studies, and environmental science, with emphasis on affective engagement in climate adaptation. Key trends include drone-mediated sensory co-creation (2020-2024), feminist landscape historiography (2024), and emergent design methodologies for regenerative landscapes (2025). Scientific recognition includes: ICAR-CORA Prize 2011 for PhD thesis 'Landscape transformations' Vola Prize 2000 for master project Frits Schlegel Honors 2003 for early-career architecture She supervises doctoral research including Hongxia Pu's 'Filmic Scroll' (2024) on Chinese desakota landscapes and Kristen Danielle van Haeren's 'Thickness of the Green' (2020) on Danish welfare landscapes. Major grants include Danish Arts Foundation funding (75,000 DKK for Archive for Becomings), EU Water4All (3 million DKK), and Danish Research Council support (6 million DKK for Drone Imaginaries project). As director of Surroundinglab.org and editorial board member for Emotion, Space and Society, she bridges academic research with professional practice. Her studio mupLA (founded 2003) has delivered award-winning landscape projects including Roskilde Festival area planning and Bornholm harbor designs, maintaining active engagement with municipalities and architecture offices.
Hans Jacob Teglbjærg Stephensen serves as a Special Consultant in the Department of Computer Science within the Faculty of Science at the University of Copenhagen. His research focuses on Image Analysis, Computational Modelling and Geometry, with significant contributions spanning mathematical theory and biomedical applications. His institutional email address (hast@di.ku.dk) and physical location at Universitetsparken 1, 2100 København Ø confirm his active affiliation with the university. Stephensen's research interests center on computational geometry, mathematical imaging, and stochastic modeling, with notable applications in neuroscience. His work bridges theoretical mathematics with practical biomedical applications, particularly in cellular and subcellular structure analysis. His publications demonstrate expertise in developing geometric models for complex biological systems and creating novel mathematical frameworks for image analysis. His publication record from 2021-2024 reveals a strong trajectory with high-impact work in both mathematical journals and top-tier biomedical publications. The 2024 Nature Biotechnology paper on glial progenitor cells demonstrates significant translational impact, while his mathematical works in journals like Journal of Mathematical Imaging and Vision establish theoretical foundations. His research shows a clear progression from theoretical geometric models to applications in neuroscience, with increasing collaboration with biomedical researchers. Stephensen completed his PhD thesis titled 'Geometrical Models and Stochastic Geometry of Subcellular Structures' in 2021 through the University of Copenhagen's Department of Computer Science. His work has garnered significant attention, with several publications picked up by multiple news outlets and shared across social media platforms including X (formerly Twitter) and Facebook. The 2024 Nature Biotechnology paper alone was covered by 25 news outlets and shared by 181 X users, indicating substantial scientific impact and public interest in his research.
Søren Ingvor Olsen is an Associate Professor in the Department of Computer Science at the University of Copenhagen's Faculty of Science. His research focuses on Image Analysis, Computational Modelling, and Geometry within the broader field of Computer Science. He maintains an active research profile with numerous publications and collaborations across various application domains. Dr. Olsen received his academic training at the University of Copenhagen, where he earned both his Master's degree in 1984 and his PhD in 1988, both from the Department of Computer Science. His academic career at the same institution began as an Assistant Professor from 1988-1991, followed by his promotion to Associate Professor. He served as Head of the Department from 1996-1999 and has held various significant roles including Visiting Professor at University Blaise-Pascal in France (2006) and involvement with the E-Science Center at the Faculty of Science (2008). Professor Olsen's primary research areas span 3D Computer Vision, Image Analysis, and Pattern Recognition. His work is characterized by its interdisciplinary nature, combining theoretical foundations with practical applications. Recently, he has focused on applying computer vision techniques to real-world problems such as railway sign detection, agricultural weed mapping using drone technology, and automation in food processing industries. His research bridges the gap between fundamental computer vision algorithms and their implementation in diverse industrial settings, demonstrating strong translational research capabilities. Analysis of his recent publication record reveals a strong trend toward applying computer vision and machine learning techniques to environmental monitoring and precision agriculture. His work demonstrates expertise in drone-based imaging systems, deep learning for object detection, and the development of practical solutions for agricultural and transportation infrastructure challenges. The interdisciplinary nature of his research is evident in collaborations spanning environmental science, agricultural engineering, and transportation systems. Throughout his career, Professor Olsen has contributed significantly to academic development, notably being responsible for the creation and accreditation of a cross-faculty bachelor's program in Science and IT in 2009. While specific grant information isn't detailed in the available materials, his extensive publication record and international collaborations suggest successful research funding throughout his career. His work appears to involve substantial student mentorship, though specific advisees aren't named in the provided information. Professor Olsen's research appears to be conducted through the Image Analysis group within the Department of Computer Science at the University of Copenhagen. His work with drone technology and agricultural applications suggests collaborations with agricultural research institutions and potentially industry partners in the railway and food processing sectors. The practical orientation of his recent work indicates strong connections between his academic research and real-world industrial applications.
Sizhuo Li is a Postdoctoral Researcher at the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. Working at the intersection of artificial intelligence and environmental science, Dr. Li applies advanced neural networks to solve critical challenges in forest management and carbon stock estimation using remote sensing technologies. Dr. Li's research interests center on developing and adapting deep learning methodologies for environmental monitoring applications. Their primary focus areas include intelligent forest management systems, tree resource quantification, and carbon stock estimation. Current research involves creating an automatic and scalable tree inventory framework based on UNet architecture that enables individual crown segmentation, precise tree counting, and accurate height estimation at country-wide scales. Dr. Li's publication record demonstrates a clear trajectory from technical deep learning methodologies to practical environmental applications across diverse geographical contexts, from the Mediterranean region to the Sahel and Rwanda. This work effectively bridges computer science and environmental science to address urgent climate change and sustainability challenges through innovative technological solutions. Dr. Li has published in prestigious journals including Nature Food, Nature Ecology and Evolution, Nature Reviews Electrical Engineering, and Frontiers in Remote Sensing, with multiple publications receiving significant citations and media attention. Their research has been highlighted by numerous news outlets and academic platforms, indicating growing influence in the field of AI-powered environmental monitoring.
Johan Peter Bøtker serves as an Associate Professor in the Department of Pharmacy at the University of Copenhagen's Faculty of Health and Medical Sciences, specializing in advanced pharmaceutical technology and solid-state analysis. His research program emphasizes: Solid-state characterization using thermal analysis, Raman spectroscopy, and synchrotron diffraction techniques Development of dissolution testing methodologies with 3D-printed custom apparatus Computer vision applications for pharmaceutical quality control systems Advanced processing techniques including milling, hot-melt extrusion, and additive manufacturing Acoustic data treatment for inhaled drug delivery optimization Analysis of his publication record reveals consistent expertise in solid-state transformations, dissolution mechanisms, and amorphous material stability, with increasing integration of computational modeling and synchrotron-based analytical approaches. His work bridges pharmaceutical sciences with materials engineering and analytical chemistry. While specific awards were not documented, Dr. Bøtker maintains extensive collaborative networks across international research institutions as evidenced by his co-authorship patterns and country-level research mapping.
