جستجوی استاد راهنمای دانشگاهی

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.

• Machine learning methods development
• Bioinformatics
• Biological sequence analysis
• +۸ مورد دیگر

Zorana Jovanovic Andersen is an Associate Professor at the University of Copenhagen, Department of Public Health, Section of Environmental Health, specializing in Environmental and Occupational Epidemiology. Her research focuses on health impacts of air pollution , noise exposure , and lifestyle factors across large cohorts including the Danish Nurse Cohort and Diet, Health, and Cancer cohort. Primary research areas: Environmental Epidemiology, Breast Cancer Risk, Air Pollution, Noise Studies, Physical Activity Interactions Key projects: Hyperion (road traffic noise), ELAPSE (low-level air pollution), TAPAS (climate-transportation health risks) Research Themes She investigates long-term exposure to air pollution and noise in relation to breast cancer , diabetes , cardiovascular disease , and brain cancer , while exploring how factors like physical activity modify these risks. Recent work includes dementia and COVID-19 mortality analyses. Scientific Contributions Her 15 most recent publications address: 1) Air pollution disparities in Europe 2) Dementia-air pollution links 3) Stroke-noise interactions 4) Airport ultrafine particle exposure 5) Asthma-COPD overlap 6) Breast cancer-mammographic density relationships. Major Grants Danish Working Environment Research Fund (1.79M DKK, 2017) Danish Heart Association (433K DKK, 2016) Danish Council for Independent Research (1.93M DKK, 2015) Teaching & Leadership Course Leader for Data Processing in Health Care and Public Health Informatics at UCPH since 2011. Organized Danish Health Registries seminars and served on scientific committees for environmental conferences.

• Environmental Epidemiology
• Air Pollution
• Noise Pollution
• +۱۲ مورد دیگر

Rob Gleasure is a Professor in the Department of Digitalization at Copenhagen Business School (CBS), Denmark. His research focuses on the intersection of information systems, digital technologies, and human behavior, with particular expertise in blockchain technology, crowdfunding, AI applications, and technology adaptation. Based at Solbjerg Square 3 in Frederiksberg, he contributes to CBS's mission of advancing knowledge in business and society through digital transformation. Professor Gleasure's research spans several key areas within information systems and digital innovation: Digital finance and blockchain technologies, including cryptocurrency and financial applications Crowdfunding platforms and digital fundraising mechanisms Artificial intelligence applications in various domains including healthcare and banking Human-computer interaction and the psychological aspects of technology adoption Digital collaboration and the affective dimensions of online work Quantum computing infrastructure and emerging technologies His recent publications reveal an evolving research trajectory that increasingly addresses the societal implications of digital technologies. Gleasure has moved from foundational work on crowdfunding and blockchain to more complex examinations of AI ethics, gender bias in technological systems, and the psychological impacts of digital media. His research demonstrates growing attention to sustainable development goals, particularly those related to responsible consumption and production, reduced inequalities, and climate action. The interdisciplinary nature of his work bridges business, technology, and social sciences, often employing both qualitative and experimental methodologies. Professor Gleasure has served as a supervisor for numerous students (21 supervisor tasks mentioned) and has been active in academic service including co-chairing the ACM Collective Intelligence Conference in 2021. His research has attracted media attention, with contributions to discussions on cryptocurrency, carbon offsetting in aviation, and AI applications.

• Information Systems
• Digitalization
• Blockchain Technology
• +۷ مورد دیگر

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

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +۳ مورد دیگر

Bernhard Palsson is the Scientific Director at the Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain), Technical University of Denmark (DTU). His research focuses on systems biology, metabolic engineering, and computational modeling in microorganisms such as Escherichia coli and Yarrowia lipolytica . Key roles : Scientific Director, Researcher, PhD Supervisor SDG contributions : Biosustainability, Antibiotic Discovery, Climate Action Research Interests : Palsson's work spans metabolic pathways , regulatory networks , and genome-scale modeling . He explores chemical stress tolerance mechanisms, pangenome structures, and integrates machine learning with transcriptomic data to advance biosynthetic applications. His projects include antibiotic discovery via computational resources and metabolic model reconstruction for Lactobacillus and Escherichia coli species. Recent Article Trends : His 2025 submissions highlight interdisciplinary approaches combining machine learning and experimental evolution to enhance biochemical production and address antibiotic resistance. Topics include transcriptional network analysis , pangenome decomposition , and metabologenomic modeling , emphasizing scalability and data-driven methodologies. Supervision & Collaboration : Palsson mentors PhD students like Omid Ardalani and collaborates on projects such as iimena (Integration of Informatics and Metabolic Engineering for Novel Antibiotics) and pangenome metabolic model reconstruction. His team includes researchers from DTU and international institutions. Projects : iimena: Novel Antibiotic Discovery (2017–2023) Lactobacillus Pangenome Modeling (2021–2025) Networks : Collaborated with institutions in 12+ countries Co-authorships in Escherichia coli and Yarrowia lipolytica studies

• Biochemistry
• Genetics
• Molecular Biology
• +۴ مورد دیگر

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.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +۷ مورد دیگر

Jakob Eg Larsen is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Applied Mathematics and Computer Science (DTU Compute) within the Cognitive Systems Section. He leads the Mobile Informatics and Personal Data Laboratory (MILAB). His research focuses on Human-Computer Interaction (HCI), Personal Informatics, Quantified Self, and Information Visualization, with applications in wearable technology, mental health, and healthcare. He teaches courses in Digital Media Engineering, including user experience, mobile application prototyping, and personal informatics. Education: PhD (2005) and MSc in Computer Science (1999) from the University of Copenhagen, with additional studies in cognitive psychology. Research emphasizes wearable devices for mental health interventions (e.g., PTSD treatment), physical activity tracking in pregnancy, and personalized hearing aid systems. Over 98 publications and 12 supervised PhD projects highlight his contributions to HCI, mHealth, and data-driven healthcare solutions. Labs/Teams: MILAB focuses on mobile informatics and personal data interaction. Collaborations span interdisciplinary fields like audiology, clinical psychology, and public health.

• Human Computer Interaction (HCI)
• Personal Informatics
• Quantified Self
• +۳ مورد دیگر

Lars G. Johansen is an Associate Professor at Aarhus University, affiliated with the Department of Electrical and Computer Engineering. His work bridges interdisciplinary domains, with a focus on signal processing and machine learning. Research interests include Audio engineering and acoustic signal analysis Biomedical signal processing Neuroscience applications in Parkinson's disease studies Recent publications highlight trends in audio engineering (e.g., loudspeaker distortion analysis) and biomedical signal processing (e.g., ECG-derived respiration techniques). Collaborative work spans neuroscience, Parkinson's disease treatment evaluation, and noise reduction systems. Contact: Email: lgj@ece.au.dk Phone: +45 41 89 32 74 Labs/Teams: Signal Processing and Machine Learning Laboratory at Aarhus University.

• Signal Processing
• Machine Learning
• Audio Engineering
• +۳ مورد دیگر

Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۷ مورد دیگر

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Maurizio Teli is an Associate Professor in the Department of Sustainability and Planning at Aalborg University, part of The Technical Faculty of IT and Design. His work focuses on participatory design, sustainable development, and computing for social good. He coordinates major projects such as Reworlding (Horizon Europe, 2024–2027) addressing socio-environmental challenges and EDUS (ERASMUS+, 2023–2026) developing training methodologies for sustainable education. Teli is affiliated with the AI for the People Centre, Danish Centre for Health Informatics, and Green Societies, emphasizing interdisciplinary collaboration. His research spans participatory design methodologies, sustainability transitions, and the role of technology in fostering commoning practices. Notable contributions include exploring non-human stakeholders in sustainable development and infrastructuring public history. Teli has authored over 70 publications, including book chapters and peer-reviewed articles in journals like interactions and Radiography . Advising and grants include leadership roles in 7 active projects totaling €10M+ funding. His work bridges academia and practice, with datasets like 'Community Radio Guidelines' (Zenodo, 2019) promoting accessible design practices. Teli’s labs and networks focus on digital sustainability, healthcare informatics, and participatory urban planning.

• Sustainable Development
• Participatory Design
• Computing
• +۳ مورد دیگر

Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.

• Graph Neural Networks
• Blockchain
• Data Management
• +۵ مورد دیگر

Allan Hanbury is a Full Professor for Data Intelligence at the Faculty of Informatics, TU Wien, and a faculty member at the Complexity Science Hub Vienna. He leads the Data Science Research Unit and serves as the Faculty Representative for financial affairs and internationalization. He holds a PhD in Applied Mathematics from Mines ParisTech and a Habilitation in Practical Informatics from TU Wien. PhD in Applied Mathematics, Mines ParisTech, 2002 Habilitation in Practical Informatics, TU Wien, 2008 Bachelor’s and Master’s in Physics and Applied Mathematics, University of Cape Town His research focuses on information retrieval, data mining, natural language processing, and information extraction, with applications in healthcare, legal, and patent domains. He has coordinated major EU projects including Khresmoi, VISCERAL, KConnect, and DoSSIER, the latter training 15 PhD students. He is co-founder of contextflow, a spin-off commercializing radiology search technology. His recent publications (2024–2022) highlight a strong trend in systematic literature review automation, neural re-ranking, large language models, and domain-specific information extraction. Key themes include improving citation screening, patient-trial matching, evaluation metrics, and dataset creation for offensive language and legal text. His work combines technical innovation with real-world impact in medical, legal, and scientific communication contexts. Allan Hanbury has received no explicitly mentioned scientific awards in the provided text. He actively supervises numerous PhD and master’s students and leads large research projects such as DoSSIER, Transparent Automated Content Moderation, and PLFDoc, funded by FWF, WWTF, and EU. His group develops tools for evidence synthesis, clinical data extraction, and legal document analysis. He also contributes to AI and data strategy in Austria and Europe. He leads the Data Science Research Unit at TU Wien and is involved in multiple interdisciplinary projects including BRISE (building regulation analysis), CDL-RecSys, and TACo, focusing on legal and scientific document processing. His work bridges academia and industry through spin-offs like contextflow and collaborations with Deutsche Telekom, Siemens, and FMA.

• Information Retrieval
• Data Mining
• Natural Language Processing
• +۱۳ مورد دیگر

Giovanni Colavizza holds a dual academic appointment as Professor in the Department of Communication at the University of Copenhagen and Associate Professor at the University of Bologna (since November 2023). His research bridges information science, digital humanities, and open science practices. His primary research areas include: Quantitative analysis of open research practices (data/code/preprint sharing) and citation impact Wikipedia's role in scientific knowledge dissemination and news source reliability assessment Digital tools for humanities including knowledge graphs and NLP applications for historical archives Recent publications (2024-2025) reveal strong interdisciplinary trends, combining scientometrics with natural language processing to analyze scientific communication patterns and colonial archival materials. His work appears in leading venues including PLoS ONE, Scientometrics, and IEEE Journal of Biomedical and Health Informatics, demonstrating methodological innovation across information science and humanities domains.

• Open Science
• Scientometrics
• Digital Humanities
• +۴ مورد دیگر

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.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر