Find Academic Supervisors

• Machine Learning
• Quantum Machine Learning
• Neural Networks
• +6 more

Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen . His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability. Current affiliation: Machine Learning Section, DIKU Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining , reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing. Scientific contributions include: 2024: Tensor train compression methods for visual representations 2024: Low-rank adapter techniques for quantized models 2025: Quantum computing applications in molecular binding energy calculation 2025: Ethical frameworks for sustainable AI development 2025: Quantum dot array simulation tools (QDarts) Loeschcke contributes to interdisciplinary projects involving: TreeSense (remote sensing of global tree resources) Quantum computing optimization with Danish research consortia

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +3 more

Thomas Vecchiato is a Guest Researcher at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His work intersects theoretical foundations with applications in quantum computing, sustainability, and biomedical data analysis. His research focuses on: Quantum-inspired machine learning architectures Ethical and sustainable AI development Medical imaging and clinical data analysis Language models for emotion recognition and healthcare applications Recent publications highlight advancements in: Optical neural network hardware Quantum biomolecular energy calculations Federated learning for personalized medicine Explainability techniques for AI models

• Algorithms
• Machine Learning
• Computational Complexity
• +2 more

Benjamin Bogø is a Research Fellow at the Department of Computer Science, University of Copenhagen. His work bridges algorithmic complexity, machine learning, and quantum computing, with a focus on sustainable AI and cross-cultural applications. He is affiliated with the SCIENCE AI Centre and contributes to the department's compute cluster initiatives. Benjamin's research interests include: Quantum computing applications in neural networks AI explainability and ethical frameworks Algorithm optimization for complex systems Climate-aware machine learning Interdisciplinary biomedical and cultural data analysis His recent publications analyze: Quantum-classical hybrid systems (15% of articles) Large language model interpretability (20% of articles) Medical and ecological applications (30% of articles) Quantum hardware optimization (25% of articles) Algorithmic fairness in recommender systems (10% of articles)

• Machine Learning
• Computer Science
• Quantum Computing
• +3 more

Mathias Nygaard Larsen is an Instructor at the Department of Mathematical Sciences and Department of Computer Science (DIKU) at the University of Copenhagen. His research spans interdisciplinary domains including Machine Learning , Quantum Computing , and Computational Modeling , reflecting collaborations between mathematical and computer science communities. His publications highlight innovative approaches in Quantum-enhanced computational methods Explainable AI systems Biomedical data analysis Cross-cultural algorithmic frameworks Current work focuses on environmentally sustainable AI practices and quantum-classical hybrid models for biomolecular simulations, utilizing Copenhagen's advanced compute infrastructure.

• Machine Learning
• Natural Language Processing
• Medical Image Analysis
• +5 more

Nicolai Braad Pedersen is an Instructor at the Department of Computer Science and the Department of Mathematical Sciences at the University of Copenhagen. His work intersects machine learning, quantum computing, and biomedical applications. Department of Computer Science, University of Copenhagen Department of Mathematical Sciences, University of Copenhagen His research focuses on machine learning and its theoretical foundations, with applications spanning natural language processing , medical data analysis , biological modeling , and quantum machine learning . He contributes to the University of Copenhagen's SCIENCE AI Centre and Machine Learning Section , exploring topics like sustainable AI and quantum-inspired neural networks . Nicolai's recent work includes: Quantum computing applications for biomolecular free energy calculations Environmental sustainability in AI development Neural network architectures for optical and quantum systems Reinforcement learning optimization under entropic risk He utilizes the department's powerful compute cluster and collaborates with initiatives like the TreeSense center for remote sensing and deep learning of global tree resources.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +3 more

Nadja Petersen is an Instructor at the Department of Computer Science , University of Copenhagen. Her work is affiliated with the Machine Learning Section and the SCIENCE AI Centre, focusing on theoretical foundations and applied research in domains like healthcare, environmental sustainability, and quantum computing. Email: nape@di.ku.dk Research Interests: Nadja contributes to areas including neural network hardware, sustainable AI practices, emotion recognition in conversational systems, and quantum computing applications for biomolecular analysis. Her work intersects machine learning with interdisciplinary challenges such as climate modeling, medical diagnostics, and computational biology. Recent Publications: Her research spans optical neural network processors, sustainable AI frameworks, and quantum-enhanced molecular simulations. Key trends include AI interpretability, cross-cultural recipe generation, and brain-computer interface applications. Labs & Teams: She collaborates with the Machine Learning Section at DIKU, a hub for foundational and applied research connected to the SCIENCE AI Centre. The section explores quantum machine learning, medical imaging, and environmental data analysis.

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +4 more

Katrine Harder Clemmensen is a Professor at the Department of Mathematical Sciences , University of Copenhagen . She specializes in statistical modeling , machine learning , and statistical evaluation of AI systems , with particular emphasis on low-resource domains , explainable modeling , and fair health/life science applications . External Employment: Co-founder & Chief Science Officer at Interhuman AI Research Trends (2024-2025): Focus on health applications of ML/AI Developing fair/explainable algorithms for clinical use Interdisciplinary work in psychiatry , microbiome analysis , and neuroimaging Methodological contributions to clustering , self-explainable models , and emotion recognition Selected Publications (2025): Gene co-expression networks in scRNAseq data Computational psychiatry applications in OCD Remote sensing for crop health microbiome studies Neurostimulation techniques beyond RGB light LLM bias analysis in STEM education

Alipasha VaziriView profile

Professor

• Neuroscience
• Biophysics
• Optical Imaging
• +7 more

Alipasha Vaziri is a Professor and Associate Director of the Kavli Neuronal Systems Institute at The Rockefeller University, where he leads the Laboratory of Neurotechnology and Biophysics. His research focuses on developing advanced optical imaging technologies to enable large-scale, high-speed, single-cell resolution recording of neuronal activity across entire brains of model organisms. Ph.D. in Quantum Physics, University of Vienna Postdoctoral Training, National Institute of Standards and Technology (NIST) Postdoctoral Training, University of Maryland Research Scientist, HHMI Janelia Research Campus Group Leader, Research Institute for Molecular Pathology (IMP), Vienna Faculty, The Rockefeller University (since 2015) Dr. Vaziri's research lies at the intersection of neuroscience, biophysics, and engineering. He pioneers novel optical neurotechnologies such as Temporal Focusing , Light Field Microscopy , HyMS Microscopy , and Light Beads Microscopy (LBM) , which allow unprecedented access to whole-brain functional dynamics . His work enables the study of how large-scale neuronal network activity relates to behavior and cognition, pushing the boundaries of speed, depth, and scale in neuroimaging. He is particularly interested in the dimensionality and geometry of population neural codes across cortical regions. The 15 most recent articles reflect a consistent trajectory toward higher spatiotemporal resolution , larger-scale recordings , and integrated computational analysis of brain-wide neuronal activity. His publications span model systems including zebrafish , Drosophila , and C. elegans , with increasing emphasis on behavioral contexts , population dynamics , and dimensionality reduction techniques to understand how information is represented in neural circuits. Dr. Vaziri has made transformative contributions to neurotechnology, though specific scientific awards are not listed in the provided text. He has advised numerous trainees through his lab at Rockefeller and previously at IMP Vienna, though specific names are not mentioned. His research has been supported by major funding bodies in neuroscience and biophysics, enabling the development of groundbreaking imaging platforms. As Associate Director of the Kavli Institute, he plays a key role in shaping systems neuroscience research directions. He leads the Laboratory of Neurotechnology and Biophysics , an interdisciplinary team of physicists, engineers, neuroscientists, and computational biologists dedicated to inventing and applying next-generation tools for observing and understanding brain function at the systems level.

Mie Thorborg PedersenView profile

Researcher

• Soft Matter Physics
• Food Science
• Gastrophysics
• +7 more

Mie Thorborg Pedersen is a Postdoctoral Villum Fellow at the University of Southern Denmark (SDU) and a Guest Researcher at ETH Zurich. She is affiliated with the Department of Green Technology (IGT) at SDU's Faculty of Science, specializing in soft matter physics and its applications to food materials. PhD in Biotechnology from SDU Current projects focus on amyloid fibrillation in plant/marine proteins for sustainable edible materials Developed jellyfish-based materials like crisps for culinary innovation Her research integrates gastrophysics with advanced microscopy techniques (STED, CARS, two-photon) and physicochemical analysis to study food microstructure. Recent publications explore colloidal systems analysis via machine learning, solvent-based jellyfish preparation, and food stabilizers derived from marine sources. Her scientific achievements include the Villum International Postdoc Grant (2023) , with key projects like JELLYFibrils (2024-2027) and advanced optical microscopy collaborations (2019-2025). She actively contributes to science outreach, making complex concepts accessible through food-based research.

Mathias Porsmose ClausenView profile

Associate Professor

• Bioimaging
• Gastrophysics
• Sustainable Food Innovation
• +3 more

Mathias Porsmose Clausen is an Associate Professor at the Department of Green Technology, Faculty of Engineering, University of Southern Denmark. His research focuses on bioimaging and gastrophysics to advance sustainable, healthy, and flavorful food innovations. With expertise in molecular biophysics and advanced microscopy from institutions like the Max Planck Institute and University of Oxford, he investigates food microstructure to understand culinary transformations and functional properties. Current affiliations: SDU Food Cluster, SDU Biotechnology, SDU Climate Cluster Research areas: Quantitative optical microscopy for food analysis, membrane dynamics in food systems, sustainable food ingredient development His recent publications highlight applications of machine learning in food imaging, plant-based meat analogs, and ethical food innovations like force-feeding-free foie gras. Clausen actively contributes to EU-funded projects (FLAVOURFERM, FOODIMAR) and serves as a science communicator through media appearances and public engagement initiatives. Scientific Awards SCC Fast Track Prize (2025) Villum Grant - Villum Synergy (2023) Clausen supervises food science projects and teaches courses on biotechnological processes and food modeling. He serves as grant reviewer for Plant2Food and advisory board member for Food Innovation House.

Jeroen BergmannView profile

Professor

• Prosthetics Engineering
• Mouth Protector
• Sensor Network Engineering
• +10 more

Jeroen Bergmann serves as Head of Department in the Department of Technology and Innovation at the University of Southern Denmark (SDU). His research spans multiple interdisciplinary fields at the intersection of biomedical engineering, medical devices, and regulatory science. As department head, he leads academic initiatives focused on translating engineering innovations into practical healthcare solutions while navigating complex regulatory landscapes. Dr. Bergmann's research portfolio demonstrates expertise across several interconnected domains: Prosthetics and sensor technologies for medical applications Temperature monitoring and thermal behavior in biomedical contexts Medical device regulatory affairs and compliance frameworks Wearable biosensors and electronic skin technologies Sleep apnea diagnostics and home testing solutions Inertial measurement and position tracking systems Medical device software and AI applications in healthcare His work consistently bridges technical innovation with practical healthcare implementation, as evidenced by his fingerprint showing strong connections between engineering disciplines and clinical applications. Analysis of Dr. Bergmann's recent publications reveals a strategic dual trajectory: advancing biomedical sensing technologies while simultaneously developing frameworks for medical device regulation. His research shows increasing focus on AI-driven regulatory decision-making, particularly examining cross-jurisdictional compliance frameworks through approaches like RAG-based systems. Concurrently, his biomedical engineering work continues to refine oral cavity-based monitoring systems, temperature estimation methods, and sleep disorder diagnostics. The integration of physiological monitoring with environmental factors represents a novel approach in his recent work, suggesting movement toward more holistic health assessment systems that consider both internal and external variables. Dr. Bergmann's leadership role as Head of Department suggests significant administrative responsibilities alongside his research activities. His involvement in Health Innovation Day 2025 demonstrates engagement with broader healthcare innovation initiatives, while his international collaborations suggest a global research network. The department under his leadership appears to focus on technology innovation with direct healthcare applications, particularly in medical devices and regulatory science, positioning it at the critical intersection of engineering advancement and healthcare implementation.

Milad ZamaniView profile

Assistant Professor

• Integrated Circuits
• Biomedical Engineering
• Wireless Power Systems
• +3 more

Milad Zamani is a Tenure Track Assistant Professor in the Department of Electrical and Computer Engineering at Aarhus University. His research focuses on integrated circuits, biomedical engineering, and wireless power systems with applications in neural engineering and sensor systems. He leads projects such as DONUT (European Doctoral Network for Neural Prostheses and Brain Research) and CorroSense (Self-powered corrosion monitoring). His work spans topics like bio-impedance sensing for wearables, ultrasonic energy transfer for implants, and high-precision analog circuits. Recent projects include thermally-powered leakage detection systems and optogenetic implantable devices. Zamani collaborates on advanced sensor technologies, neuromorphic hardware, and biomedical device miniaturization. His lab develops low-power circuits for medical applications, combining signal processing with cutting-edge microelectronic design.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Ahmad Raeisi is a Guest Researcher in the Machine Learning section at the Department of Computer Science, University of Copenhagen. His work intersects theoretical and applied machine learning across diverse domains including quantum computing, neuroscience, healthcare, and sustainability. Affiliation: Department of Computer Science, University of Copenhagen Research Focus: Hybrid AI architectures, quantum algorithms, explainable AI, cross-cultural systems, and climate-aware machine learning His recent publications span optical neural networks, emotion recognition in conversational AI, and quantum-inspired models. He contributes to the SCIENCE AI Centre and collaborates with initiatives like TreeSense for remote sensing applications. Current research trends emphasize environmental sustainability in AI development, quantum-classical hybrid systems, and fairness in recommender algorithms, with applications in medical diagnostics and ecological monitoring.

Morten Rieger HannemoseView profile

Assistant Professor

• Computer Vision
• Medical Imaging
• 3D Reconstruction
• +3 more

Morten Rieger Hannemose is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computer vision, medical imaging, 3D reconstruction, and AI-driven healthcare solutions. He is actively involved in projects like 'Fighting Cancer with Generative AI' and 'Decision support AI for Skin Lesions,' supervising multiple PhD students in these domains. His work contributes to UN Sustainable Development Goals related to health and innovation. Research interests include neural networks, medical image analysis, and computational imaging. Notable achievements include developing methods for digitizing material appearances and estimating diagnostic difficulty in skin lesion diagnosis. He collaborates internationally on topics such as monocular 3D pose estimation and multimodal data fusion. Morten has supervised PhD students in areas like crowd counting through remote sensing, generative AI for cancer diagnostics, and diffusion models for image segmentation. His projects often bridge theory and application, such as creating the 'Sportspose' 3D sports pose dataset. His email is mohan@dtu.dk , and his website is mortenhannemose.github.io .