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).
Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.
Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.
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.
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.
Edward Baggs is an Assistant Professor in the Department of Culture and Language at the University of Southern Denmark. He is also affiliated with the Centre for Human Interactivity (CHI) and DIAS, reflecting his interdisciplinary focus on cognition, language, and ecological approaches to human interaction. His research centers on ecological and enactive theories of cognition, emphasizing how perception, action, and environment co-constitute cognitive processes. Key interests include affordances, direct social perception, distributed cognition, and the application of ecological psychology to real-world domains such as aviation safety and climate change communication. The analysis of his recent publications reveals a consistent focus on integrating ecological psychology with cognitive science and linguistics. His work challenges representationalist models of mind by advocating for Gibsonian and enactive frameworks that prioritize information in the environment and embodied interaction. Ecological Psychology Cognitive Science Ecolinguistics Enactive Cognition Environmental Communication Philosophy of Mind Edward Baggs has no listed scientific awards in the provided text. He collaborates extensively with researchers such as Sune V. Steffensen, Monica Meyer, and Tom Davies-Barton. Although no formal grants or advising roles are mentioned, his collaborative research outputs suggest active participation in funded or institutional research projects. He has published across journals and books, indicating a strong scholarly presence in ecological and cognitive sciences. He is associated with interdisciplinary research centers such as CHI and DIAS, which support work in human interactivity, digital innovation, and advanced semantics. These affiliations provide a rich environment for exploring cognition as embedded and extended in cultural and technological contexts.
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.
Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.
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.
Shuai Zhao is an Assistant Professor at the AAU Energy Department, Faculty of Engineering and Science, Aalborg University. His research focuses on applying machine learning and artificial intelligence techniques to enhance reliability and condition monitoring in power electronic systems, with specific interests in lifetime estimation, fault diagnosis, and health management of critical components like capacitors and semiconductor devices. Institution: Aalborg University School: Faculty of Engineering and Science Department: AAU Energy Email: szh@energy.aau.dk His research spans multiple domains including: Physics-informed machine learning for power converter systems Remaining useful life prediction with hybrid Bayesian deep learning Thermal transient analysis and stress emulation methods IoT-enabled monitoring schemes for semiconductor devices Neural network applications in lithium-ion battery prognostics Recent publications show a strong trend toward integrating domain-specific physics with machine learning frameworks to address real-world challenges in: Power electronics reliability under operational stress Anomaly detection in multivariate time-series data Robust fault diagnosis for railway traction systems Temperature estimation in electric vehicle motors Imbalanced data handling in diagnostic systems Capacitance degradation modeling under environmental factors Current projects demonstrate collaboration with leading institutions on: AI-assisted long-term maintenance strategies Physics-informed neural network architectures Smart agricultural monitoring systems via IoT platforms Advanced particle filter methods for life prediction
Guangya Yang is an Associate Professor at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), with expertise in power system stability, control, and protection of low-inertia systems, particularly in offshore wind applications. He has worked full-time as an electrical design and simulation specialist at Ørsted (2020-2021) and maintains active roles in IEEE, IEC standardization, and editorial work as lead editor for IEEE Access PES. PhD in Electrical Engineering (University of Queensland, 2008) Senior Member of IEEE Former expert member of European Technology & Innovation Platform for solar PV His research focuses on electromagnetic transient simulation, cyber-physical energy systems, and grid compliance for wind power plants. He leads the Horizon 2020 InnoCyPES project (€12m+ grants), training 15 early-stage researchers in cyber-physical energy systems. Key contributions include validating synchronous condensers for low-inertia systems and developing industry training programs on offshore wind control strategies. Coordinated Hardware-in-the-Loop test bench standards (IEC61400-21-5) Advances in grid-forming converter stability and fault ride-through assessment Developed digital twins for power grid validation Scientific Recognition: World’s Top 2% Scientists (Elsevier/Stanford, 2024) Advising & Grants: Supervises 5 PhD students in projects related to machine learning, digital twins, and predictive maintenance. Coordinates €12m+ in research grants, including Horizon 2020’s InnoCyPES. Labs & Collaborations: Works with Ørsted, industry partners, and international universities on offshore wind validation and digitalization.
Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.
Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.
Davide Mottin is an Associate Professor at the Department of Computer Science, Aarhus University. His primary research focuses on graph theory, machine learning, and data mining, with significant contributions to knowledge graphs, algorithm design, and interdisciplinary applications in drug discovery and material science. He holds a leadership role in large international conferences such as CIKM 2024 as a Program Chair. His research explores scalable graph algorithms (e.g., subgraph matching, alignment), robust knowledge graph cleaning, and leveraging large language models for scientific tasks. Mottin has pioneered work on spectral methods for graph analysis (e.g., NetLSD, VERSE embeddings) and developed frameworks for interactive data exploration (e.g., X2Q, MetaExp systems). Key contributions include FUGAL for graph alignment and Ucode for community detection Active in reproducibility efforts, as seen in retraction notices and algorithmic redesigns Focus on practical applications in drug discovery via evolution-based models (EvolMPNN) He has authored over 60 peer-reviewed publications and holds grants supporting interdisciplinary research at the intersection of computer science and life sciences. Mottin is affiliated with the university's AI and data science initiatives, contributing to both theoretical advancements and real-world system implementations.
