Find Academic Supervisors

• Bioinformatics
• Machine Learning
• Deep Learning
• +5 more

Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).

Seth LloydView profile

Professor

• Quantum Information Science
• Quantum Computation
• Quantum Communications
• +5 more

Seth Lloyd is a Professor of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he directs the Center for Extreme Quantum Information Theory (xQIT). His work bridges theoretical physics, quantum information science, and complex systems theory. He has made significant contributions to the foundations of quantum computing and quantum information processing. Lloyd received his education from prestigious institutions: B.A. from Harvard College (1982) M.Phil from Cambridge University (1984) as a Marshall Scholar Ph.D. in Physics from Rockefeller University (1988) Lloyd's research focuses on quantum information science, particularly quantum computation and quantum communications. He has pioneered work in quantum analog computation, quantum error correction, and quantum metrology. His research explores how quantum mechanics can be harnessed for information processing tasks, with applications ranging from quantum computing to understanding biological processes like photosynthesis. Lloyd is also known for his work on complex systems and the relationship between information and physical systems, arguing that the universe itself can be viewed as a quantum computer. His publication record shows a clear progression from foundational quantum computing work to applications in quantum machine learning and quantum biology. The most recent articles reveal a strong focus on quantum algorithms for machine learning, quantum metrology, and the intersection of quantum mechanics with biological systems. His work on the HHL algorithm for solving linear systems has been particularly influential in quantum machine learning, though its practical advantages have been debated following Ewin Tang's classical algorithms. Lloyd has received numerous scientific honors: Lindbergh Fellow (1994) Finmeccanica Professorship (1996) Edgerton Prize (2001) Fellow of the American Physical Society (2007) Quantum Communication Award (2012) International Quantum Communication Award (2012) Throughout his career, Lloyd has mentored numerous students and researchers in quantum information science. He has secured significant research funding for his work in quantum computing and complex systems. His research has been supported by various foundations and government agencies interested in advancing quantum technologies. Lloyd has also been involved in interdisciplinary collaborations, particularly with biologists studying quantum effects in photosynthesis. Lloyd directs the Center for Extreme Quantum Information Theory (xQIT) at MIT, which brings together researchers from physics, computer science, and engineering to tackle fundamental challenges in quantum information processing. His lab has been at the forefront of developing theoretical frameworks for quantum computing and exploring practical implementations of quantum information protocols.

Søren Munch KristiansenView profile

Associate Professor

• Geoarchaeology
• Soil Science
• Groundwater
• +7 more

Søren Munch Kristiansen is an Associate Professor in the Department of Geoscience at Aarhus University, Faculty of Natural Sciences. His interdisciplinary research bridges geoscience and archaeology, focusing on the complex interactions between soil, water, and human societies across time. He is actively engaged in transdisciplinary projects involving geoarchaeology, groundwater, and public health. Research Interests: His work centers on how soil and groundwater have shaped human prehistory and continue to influence modern life. Key areas include safe drinking water from a lifelong perspective, geoarchaeological prospection, Viking Age settlements, and the application of geophysical and geochemical methods in archaeological contexts. He is particularly interested in novel, interdisciplinary methodologies. Recent Research Trends: His recent publications reveal a strong focus on integrating geophysical data (e.g., GPR, borehole databases) with archaeological interpretation, especially in 3D urban modeling and landscape change. Themes include Viking Age sites, interglacial deposits, and anthropogenic soil modifications. His work increasingly employs machine learning and large-scale data synthesis. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students are not listed, he leads and participates in multiple funded research projects, indicating an active supervisory role. His grants span from 2016 to 2024, including projects like TITAN, SAGA, and 'Fingerprinting displaced molecular substances,' highlighting sustained funding and leadership in interdisciplinary geoscience-archaeology research. Labs and Teams: He is involved in collaborative networks such as the Soil Science & Archaeo-geophysics Alliance (SAGA), suggesting leadership in interdisciplinary research teams. His work often involves multi-proxy analyses and international collaborations, particularly in Scandinavian archaeological geophysics.

• Machine Learning
• Pattern Recognition
• Classification
• +6 more

Christian Igel is a Professor at the Department of Computer Science, University of Copenhagen, and serves as director of the SCIENCE AI Centre . He is also a co-lead of the Pioneer Centre for Artificial Intelligence in Denmark. His academic journey includes a Doctoral degree from Bielefeld University (2002) and a Habilitation degree from Ruhr-University Bochum (2010). Igel is a Juniorprofessor (2002–2010) and has held editorial roles at journals like KI - Künstliche Intelligenz and Artificial Intelligence Journal . Doctoral degree: Faculty of Technology, Bielefeld University, Germany (2002) Habilitation degree: Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany (2010) His research spans Machine Learning , focusing on Support Vector Machines , Evolution Strategies , Reinforcement Learning , Deep Neural Networks , and PAC-Bayesian Analysis . He applies these methods to Environmental Monitoring , Medical Diagnostics , and Climate Research . Recent publications highlight work on adversarial machine learning , environmentally sustainable AI , and tree resource mapping using deep learning. His scientific awards include being a ELLIS Fellow . Igel’s software tools like Shark , woody , and Multi-Planar UNet are widely used in research and industry. Notable grants and collaborations involve projects with European Lab for Learning and Intelligent Systems (ELLIS) , SCIENCE AI Centre , and international teams in Denmark , Germany , and France . His lab leadership emphasizes open-source frameworks and reproducible research. Editorial Roles: German Journal on Artificial Intelligence , Evolutionary Computation Journal , Artificial Intelligence Journal Software Projects: Shark , woody , Multi-Planar UNet , U-Time Collaborations: SCIENCE AI Centre , Pioneer Centre for Artificial Intelligence , European Lab for Learning and Intelligent Systems

Ankit KariryaaView profile

Assistant Professor

• Machine Learning
• Remote Sensing
• Geospatial Analysis
• +4 more

Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.

Kim BjergeView profile

Associate Professor

• Computer Vision
• Deep Learning
• Machine Learning
• +4 more

Kim Bjerge serves as Associate Professor and Group Leader in Aarhus University's Department of Electrical and Computer Engineering, specializing in computer vision and machine learning applications for ecological monitoring. His research bridges engineering and environmental science to develop innovative solutions for insect biodiversity assessment and sustainable agriculture. His core research interests include computer vision, deep learning, and edge computing systems for real-world ecological monitoring. Dr. Bjerge develops time-lapse camera pipelines and deep learning models specifically for insect population tracking in natural environments, with emphasis on agricultural applications like black soldier fly farming and biodiversity conservation. His work integrates signal processing techniques with biological data to create field-deployable monitoring systems. Recent publications reveal a strong trend toward practical implementations of computer vision in entomology, particularly focusing on edge processing for camera traps, automated trait prediction in insect farming, and biodiversity monitoring systems. Key research areas include nocturnal insect monitoring, floral environment analysis, and developing specialized datasets like AMI for insect identification in wild settings. He leads multiple significant research projects funded through competitive grants: MAMBO: Modern Approaches to Monitoring Biodiversity (2022-2026) FLYgene: Sustainable Insect Production for Livestock Feed (2022-2026) Automatisk monitering af nataktive insekter: Automatic nocturnal insect monitoring (2024-2029) Pilotprojekt for automatisk registrering af invasive plantearter: Invasive species monitoring (2020-2021) As head of the Signal Processing and Machine Learning research group, Dr. Bjerge directs interdisciplinary teams developing computer vision solutions for biological monitoring systems. His laboratory focuses on creating robust field-deployable technologies including scanner-based arthropod imaging systems, time-lapse camera networks for floral environments, and edge AI processors for real-time insect monitoring in agricultural settings.

• Machine learning methods development
• Bioinformatics
• Biological sequence analysis
• +8 more

Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +3 more

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +7 more

Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.

Ling DingView profile

Associate Professor

• Biochemistry
• Chemistry
• Molecular Biology
• +7 more

Ling Ding is an Associate Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark, and a key member of the DTU Microbes Initiative and Center for Microbial Secondary Metabolites. Their research focuses on microbial ecology, secondary metabolite biosynthesis, and antifungal compound discovery. Recent work involves genome mining in Streptomyces species, NMR spectroscopy, and studying microbial interactions such as actinobacterial and Pseudomonas fluorescens systems. Projects explore bioactive phosphorus-containing molecules, in situ detection of metabolites, and data-driven biosynthesis mechanisms. Their research contributes to UN Sustainable Development Goals (SDGs) related to health and environmental sustainability, with publications spanning microbial chemistry, drug discovery, and synthetic biology. Supervised PhD projects include omics-driven antifungal metabolite discovery and microbial interaction studies. Key publication trends include Streptomyces-derived bioactive compounds (e.g., lydicamycins, alligamycin), azoxy/polyketide metabolites, and computational approaches for fungal interaction classification. Methodologies emphasize NMR, genome mining, and deep learning models. Advising: PhD students: M. Haahr, A. Kostoglou, D. J. Otto, A. Svetlova, A. Kaltenyte Projects: Active roles in omics-driven antifungal metabolite discovery, phosphorus-containing molecule mining, and secondary metabolite biosynthesis mechanisms.

• Machine Learning
• Remote Sensing
• Geospatial Representation Learning
• +3 more

Stefan Oehmcke is an Assistant Professor at the Machine Learning Section of the Department of Computer Science , University of Copenhagen. His research focuses on applying machine learning techniques to environmental and geospatial analysis, particularly in forest ecology, tree monitoring, and climate impact studies. Research Trends: His recent publications emphasize deep learning for LiDAR data processing, multi-modal geospatial representation, and sustainable AI practices. Key Collaborations: Frequently collaborates with researchers in environmental science, remote sensing, and climate change (e.g., Martin Brandt, Christian Igel). Applications: Develops tools for forest biomass estimation, tree mortality mapping, and urban safety analysis using satellite imagery. While no specific educational background or scientific awards are mentioned in the provided texts, Oehmcke's work demonstrates technical innovation in AI explainability and environmental monitoring, with significant contributions to journals like Remote Sensing of Environment and Nature Communications .

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

Chenjuan GuoView profile

Associate Professor

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +4 more

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Claus Brøndgaard MadsenView profile

Associate Professor

• Computer Vision
• Virtual Reality
• Augmented Reality
• +7 more

Claus Brøndgaard Madsen is an Associate Professor in the Department of Architecture, Design and Media Technology at Aalborg University, under The Technical Faculty of IT and Design. He is based at Campus Aalborg and is affiliated with the Computer Graphics Group and VR Light research environment. His work bridges computer vision, virtual reality, and architectural design, with a strong focus on real-time visualization and human interaction in digital environments. Research Interests: His primary research areas include Computer Vision , Virtual Reality (VR) , Augmented Reality (AR) , 3D Reconstruction , Structure-from-Motion (SfM) , and Image-Based Lighting . These are applied in industrial VR, urban design evaluation, and sensory augmentation systems. His research fingerprint highlights strong engagement with Virtual Reality (100%), Augmented Reality (71%), Illumination Conditions (41%), and Wind Turbine Engineering (28%). The recent publications (2023–2025) reflect a consistent trend in enhancing user experience in VR/AR environments, with emphasis on spatial cognition, locomotion techniques, exposure control, and machine learning integration for lighting classification and part direction prediction in industrial contexts. These works are published in top-tier conferences such as EG, GRAPP, WACV, and CAADRIA. Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: Madsen has supervised PhD students and served as a supervisor in research projects like AAIVR. He is Principal Investigator (PI) in multiple funded projects, including BUMUS: Sustainable Expansion of Mussel Production (2024–2027), CityVR: Evaluating Parametric City Designs in VR (2020–2022), and V-BRT: Virtual Bus Rapid Transit (2019). These projects reflect substantial grant acquisition and leadership in applied VR research. Labs and Research Groups: He is a key member of the Computer Graphics Group and VR Light at Aalborg University, which focus on real-time visualization, lighting techniques, and immersive technologies for industrial and architectural applications.

Katja HoseView profile

Professor

• Query Processing
• Semantic Web
• Linked Data
• +7 more

Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.