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.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence Ethics
• +3 more

Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good

Olga SaukhView profile

Associate Professor

• Efficient Machine Learning
• Deep Learning
• Embedded Systems
• +12 more

Olga Saukh is an Associate Professor at the Institute of Technical Informatics, Graz University of Technology (TU Graz), and a Faculty member at the Complexity Science Hub Vienna (CSH). She leads the Embedded Learning and Sensing Systems research group, which operates across both institutions, focusing on the design and deployment of efficient AI-based systems on edge and mobile platforms. Her work bridges deep learning and embedded systems, with applications in environmental monitoring, precision agriculture, and digital health. Ph.D. in Computer Science, University of Bonn (2009) Habilitation in Embedded Systems, TU Graz (2020) Postdoctoral Training, ETH Zurich (2010–2016) B.Sc. in Applied Mathematics, Taras Shevchenko National University of Kyiv (2002) M.Sc. in Applied Computer Science, University of Freiburg (2004) Her research centers on efficient machine learning, particularly model optimization, neural network pruning, and contrastive learning for resource-constrained devices. She is deeply engaged in solving real-world challenges in IoT, sensor networks, and cyber-physical systems. Her work emphasizes data privacy, sustainability, and practical deployment of AI at the edge. The 15 most recent publications highlight a strong trend in efficient deep learning, including model compression, pruning, and transfer learning, applied to diverse domains such as environmental sensing (air quality, pollution tracking), digital agriculture (cattle farming), and embedded AI (sensor calibration, on-demand sensing). Her work frequently appears in top-tier venues like NeurIPS, ICLR, and IEEE/ACM IPSN, reflecting her leadership at the intersection of machine learning and embedded systems. Scientific awards include: CONET Ph.D. Academic Award (2010) Multiple Best Paper Awards at IEEE PerCom, ACM/IEEE IPSN, IEEE ICPADS, IEEE SECON, and UrbCom Spotlight and Oral presentations at ICML and CoLLAs workshops Ph.D. scholarship from IPVS, University of Stuttgart (2004–2005) Prizes in Ukrainian national mathematics competitions (1996–1998) Olga Saukh actively serves on program committees of leading international conferences in machine learning and embedded systems. She has advised multiple students and leads a collaborative research group spanning TU Graz and CSH Vienna. Her group develops practical AI systems for real-world deployment, with a focus on sustainability and privacy. She co-organizes the public EfficientML reading group and has secured recognition through numerous grants and awards. Her future work continues to explore the theoretical and practical challenges of deploying efficient, trustworthy AI in mobile and embedded environments. Her research group, Embedded Learning and Sensing Systems, operates jointly between TU Graz and CSH Vienna, fostering interdisciplinary collaboration across institutions. The team develops AI solutions for edge computing, sensor networks, and cyber-physical systems, with a strong emphasis on environmental sustainability and data privacy. Members work on joint challenges using advanced collaboration tools, reflecting the distributed nature of modern academic research.

Aage Hill-MadsenView profile

Associate Professor

• Translation Studies
• Medical Communication
• Systemic Functional Grammar
• +5 more

Aage Hill-Madsen is an Associate Professor at the Department of Culture and Learning, Aalborg University (Denmark), affiliated with the Communication, Language, and Discourse (CLD) research unit. His work focuses on translation studies, medical communication, and systemic-functional linguistics. He holds a Cand.mag. et ling.merc. and a PhD in English linguistics. Research Interests: Translation theories (especially intralingual translation) Medical terminology simplification and communication Systemic-functional grammar applications Legitimation Code Theory in educational contexts Genre analysis of specialized texts Semiotic approaches to knowledge transmission Recent Publications Trends: His 2024-2025 work emphasizes medical translation between technical and lay audiences, with studies on EPAR summaries, patient information leaflets, and historical East-West medical terminology exchanges. He combines functionalist translation theory with systemic-functional frameworks to analyze textual transformations. Grants & Projects: Participated in the 2024-ongoing project Udvikling af kommunikative kompetencer gennem frivilligt brobygningsarbejde i NGO’en Social Sundhed , exploring communication skills through NGO activities. Academic Engagement: Active peer reviewer for journals like Globe and Across Languages and Cultures , with conference presentations on intralingual translation strategies and medical text accessibility.

Frede BlaabjergView profile

Professor

• Power Electronics
• Wind Turbines
• Photovoltaic (PV) Systems
• +6 more

Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)

Dorte HammershøiView profile

Professor

• Acoustics
• Hearing Science
• Audio Engineering
• +5 more

Dorte Hammershøi is a Professor in the Department of Electronic Systems at The Technical Faculty of IT and Design, Aalborg University, Denmark. Her research focuses on acoustics, sound engineering, and hearing science with significant contributions to human hearing, ear canal acoustics, and audio technology applications. Her research interests include: Temporary Threshold Shift and frequency resolution in human hearing Ear canal acoustics and sound pressure level measurement Distortion Product Otoacoustic Emission (DPOAE) analysis Impulse response and acoustic impedance studies Hearing aid technology and rehabilitation methodologies Virtual reality audio interfaces and accessibility applications Professor Hammershøi's recent publications demonstrate a strong clinical-engineering interdisciplinary approach, bridging theoretical acoustics with practical hearing rehabilitation applications. Her work on hearing aid fitting methodologies, occupational noise exposure effects, and virtual reality audio interfaces shows consistent innovation in translating engineering principles to clinical practice. The research shows particular attention to individualized hearing solutions and accessibility technologies. Her scientific contributions have been recognized with: Dansk Lydpris 2020 (awarded November 17, 2021) Ambassadør for Aalborg (awarded September 15, 2004) Professor Hammershøi has supervised 5 PhD students and led numerous research projects including the ongoing "Audio Only VR for Blind Gamers" project (2024-2028) funded by the Independent Research Foundation of Denmark, and the completed "BEAR: Better Hearing Rehabilitation" project (2016-2022). Her research has attracted significant media attention with 110 press/media appearances discussing hearing damage prevention, tinnitus, and public health implications of noise exposure. She maintains active professional engagement through committee memberships (46 documented activities), international collaborations, and contributions to clinical practice guidelines. Her work continues to influence both academic research and practical applications in hearing science and audio engineering.

Mads AlbertsenView profile

Professor

• Environmental Microbiology
• Metagenomics
• Microbial Ecology
• +14 more

Mads Albertsen is a Professor in the Department of Chemistry and Life Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. He leads the Albertsen Lab and is a key member of the Center for Microbial Communities. His research focuses on high-throughput DNA sequencing methods to explore uncultivated microbes and populate the tree of life. He is actively involved in major interdisciplinary projects such as NanoEat , Microflora Danica , and DarkScience , funded by the European Research Council, Villum Foundation, and Poul Due Jensen Foundation. His research interests span metagenomics , long-read sequencing , bioinformatics , microbial ecology , and environmental biotechnology . He develops cutting-edge methods to improve throughput in microbial genome recovery and applies them to diverse areas including wastewater treatment, human microbiome studies, and infectious disease diagnostics. His work has significant implications for public health and sustainability. The recent publications highlight a strong trend in long-read sequencing (Oxford Nanopore), metagenome-assembled genomes (MAGs) , and microbial dark matter . His team has published high-impact papers in Nature , Nature Methods , and Nature Communications , with applications in environmental systems and clinical diagnostics, including SARS-CoV-2 and bloodstream infections. His scientific awards include: The Grundfos Prize (2021) The Fritz Kaufmann Prize (2021) The Rising Star Award by IWA & ISME (2016) Research Result of the Year in Denmark (2015) The Spar Nord Fond Research Prize (2015) Mads Albertsen advises numerous PhD students, leads externally funded research projects, and is involved in technology transfer through his co-founding of DNASense ApS (2014–2020). He also serves on scientific advisory boards and contributes to public policy, including as a member of the Danish SARS-CoV-2 variant risk-assessment group. He teaches courses in Data Science, Bioinformatics, Genomics, and Environmental Microbiology at Aalborg University. His lab, the Albertsen Lab , is part of the Center for Microbial Communities , a leading research center focused on microbial systems biology and environmental applications. The lab collaborates extensively with national and international partners in academia, industry, and public health institutions.

• Multimodal Learning
• Multilingual NLP
• Vision-Language Models
• +4 more

Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.

Remus TeodorescuView profile

Professor

• Battery (Electrochemical Energy Engineering)
• Modular Multilevel Converter
• Lithium-Ion Batteries
• +6 more

Remus Teodorescu is a Professor at AAU Energy , Aalborg University , specializing in Power Electronics System Integration and Materials . His work bridges Lithium-Ion Batteries , Modular Multilevel Converters , and Smart Battery Systems . Education : Not explicitly mentioned in the text. Research Interests focus on Battery Management Systems , AI-Driven Energy Optimization , and Power Electronics for renewable energy integration. Key projects include Digital Twin for Lithium-Ion Batteries and BMS-DC for Data Centers . Recent Publications (2025) emphasize Finite Set MPC , Gradient Descent Optimization , and AI in Battery Parameter Estimation . His 2024 work explores Physics-Informed Neural Networks and Fault-Tolerant Converters . Scientific Awards : Villum Foundation Grant (313 million kroner, 2021) Named world's best in electrical engineering (2023) Advising includes supervising PhD projects on AI-Accelerated Battery Twins and Data-Driven SOH Estimation . Collaborations span Energy Cluster Denmark and Villum Fonden .

Francisco Camara PereiraView profile

Professor

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
• +3 more

Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.

Mads Leth JakobsenView profile

Associate Professor

• Public Leadership
• Leadership Development
• Bureaucracy and Debureaucratization
• +4 more

Mads Leth Jakobsen is an Associate Professor at the Department of Political Science , Aarhus University, specializing in Public Leadership , Leadership Development , and Bureaucracy and Debureaucratization . He serves as a member of the King Frederik Center for Public Leadership management team, focusing on leadership for public value creation and handling complex social challenges like the climate crisis. Academic Rank: Associate Professor Primary Research: Public Leadership, Distributed Leadership, Climate Governance Teaching: Public Leadership and Governance on political science programs Key Projects: Public Value Leadership (2022), Distributed Leadership (2012-2016) His research explores paradox leadership , organizational responses to climate challenges, and leadership credibility. Recent publications examine municipal climate strategies, bureaucratic dynamics, and leadership development frameworks. He emphasizes the interplay between leadership practices and employee outcomes in public sector organizations. Scientific awards and student supervision details are not listed in the available information. His articles highlight a strong focus on reconciling performance regimes with organizational learning, leadership credibility, and innovative governance models in public sector contexts.

• Natural Language Processing
• Machine Learning
• AI Ethics
• +5 more

Arnav Arora is a PhD Fellow at the Department of Computer Science , University of Copenhagen (DIKU), specializing in Natural Language Processing . His work focuses on ethical AI, bias detection, and societal impacts of language models. Email: aar@di.ku.dk Location: Universitetsparken 1, 2100 København Ø Arnav's research explores fine-grained value alignment in language models, harmful content detection , and cross-cultural differences in AI responses. His work bridges technical NLP advancements with social responsibility, including dual use ethical frameworks and community value analysis . Key publication trends include: 2025: Bias mitigation through BiasGym framework 2024: Factcheck-Bench benchmark development 2023: Thorny Roses dual use analysis 2022: Cross-cultural value probing methods 2020: Multi-hop fact checking systems Arnav contributes to the Software, Data, People & Society (SDPS) section, collaborating with interdisciplinary teams on projects involving language model evaluation and societal impact mitigation . His work often addresses real-world AI deployment challenges through academic-industry partnerships.

Sneha DasView profile

Assistant Professor

• Speech and Language Technology
• Machine Learning
• Data Privacy
• +5 more

Sneha Das is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Speech and Language Technology, Machine Learning, and Privacy-Preserving AI. Her research bridges technical innovation with applications in mental health and physiological signal analysis. Her work focuses on Speech Emotion Recognition , Distributed Speech Processing , and Explainable AI , with recent publications exploring model interpretability, speaker anonymization, and physiological data analysis for emotion detection. She actively supervises PhD students in projects involving AI for mental health and hydroacoustic modeling of fish behavior. Key Research Areas: Speech Emotion Recognition (SER) Privacy and Fairness in Speech Processing Transfer Learning with Physiological Time Series AI Applications in Health and Aquaculture Notable achievements include earning a DSc (Tech) degree for her thesis on robust distributed speech processing. She also contributes to educational activities, including teaching applied statistics and R programming to PhD students.

• 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.