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).
Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .
Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.
Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.
Nathalie Wahl is a Professor at the Department of Mathematical Sciences, University of Copenhagen, and serves as the Center Director for the Copenhagen Centre for Geometry and Topology (GeoTop). Her research focuses on algebraic topology, particularly mapping class groups of surfaces and 3-manifolds, homological stability, topological field theory, and loop spaces. PhD from Oxford University (2001) Current leadership of GeoTop (since 2020) Her recent work explores homological stability across automorphism groups, string topology, and structured algebras. Key collaborations include Allen Hatcher, Craig Westerland, and Nancy Hingston. She has received prestigious awards such as the ERC Consolidator Grant and the Young Elite Researcher Award. ERC Consolidator Grant (2018-2023) Female Research Leader Scholarship (2009-2013) Marie Curie European Fellowship (2003-2004)
Niels Martin Larsen is an Associate Professor at the Aarhus School of Architecture, affiliated with Research Laboratory 2: Technology, Building Cultures and Settlement. He holds a PhD in algorithmic design and has been part of academic staff since 2009. His roles include teaching and research in technology-driven architectural design. Education and Career: PhD student at Aarhus School of Architecture (2009–present) Master's and Bachelor's teaching assistant (2002–2008) Worked at architectural firms including Stuff Aps and 3XNielsen A/S (1999–2008) Graduated from Aarhus School of Architecture (1992–1998) with an internship at Enrique Miralles in Barcelona (1996) Research Interests: Focus on technology and digital design , including algorithmic design, architectural geometry, and sustainable materials. Recent work explores building reuse, digital fabrication, and timber structures. His projects bridge computational methods with practical architectural applications. Awards: 2012: Best Paper Award at Advances In Architectural Geometry for 'Engineering of complex geometry' Grants and Projects: Key roles in projects like 'Recycling on the Land Register' (2023–2025) and 'Material Imagination: Reconnecting with the Matter of Architecture' (2017–2021). Collaborates on Nordic networks for digital fabrication and timber innovation. Labs/Teams: Active in Research Laboratory 2, focusing on technology-driven solutions for building cultures and habitation.
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.
Jens Honore Walther is a Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on fluid mechanics, coastal and maritime engineering, and computational fluid dynamics (CFD). He leads projects on wave energy converters, multiphase flow systems, and thermal energy applications. His work contributes to sustainable development goals related to clean energy and climate action. External Roles: Research associate at ETH Zurich (2003–present) Postdoctoral fellow at ETH Zurich (2000–2003) Project manager at Danish Maritime Institute (1996–1997) Research scientist at Danish Meteorological Institute (1994–1996) Research Interests: Walther’s expertise spans CFD modeling, granular flow dynamics, and nanofluidics. His recent projects include optimizing wave energy converters, analyzing gap resonances in marine structures, and developing multiphase ejector geometries for heat pumps. His work integrates high-performance computing and experimental validation to address challenges in marine engineering and energy systems. Advising & Projects: He supervises PhD students in areas such as elite sport aerodynamics, gas lubrication, and alternative fuel combustion. Notable projects include: Elite sport aerodynamics (2024–2026) Alternative fuel injection in marine engines (2023–2026) Multi-physical gas bearing modeling (2024–2027) Labs & Collaborations: Walther collaborates with institutions like ETH Zurich and engages in experimental facilities at DTU. His group focuses on advanced CFD simulations and fluid-structure interaction studies.
Henrik Myhre Jensen is a Professor at the College of Engineering , Aarhus University, specializing in Mechanics of Materials , Solid Mechanics , and Mechanical Engineering . His research focuses on fracture mechanics, composite materials, and computational modeling of structural behaviors. Research Focus Fracture mechanics in composites and layered materials Computational modeling of kink band propagation Surface wear and coating technologies Ultrasound imaging applications in mechanical systems Notable Contributions Henrik has contributed to understanding crack propagation in cantilever beams, developed numerical methods for simulating delamination in composites, and explored buckling instabilities in solids. His recent work connects machine learning (holomorphic neural networks) to traditional fracture mechanics problems. Key Projects MAGFLY (2017-2021): Magnets for Flywheel Energy Storage InnoVacc (2009): Pressure Testing of Vacuum Chambers Simulation of composite structures (2011-2020): Micro-mechanical modeling
Francesco Sannino is a Professor of Computational Science at the University of Southern Denmark's Department of Mathematics and Computer Science. He is affiliated with the Danish Institute for Advanced Study (DIAS) and holds a Ph.D. His research spans quantum field theory, particle physics, and complex systems modeling. Key interests include Standard Model duality, black hole physics, and epidemiological dynamics. Education: Ph.D. in Physics (not explicitly stated in provided text, inferred from title). Research focuses on theoretical physics, including conformal field theories, gauge dynamics, and applications of quantum chromodynamics (QCD). Recent work addresses black hole metrics, pandemic modeling via renormalization group methods, and composite dark matter signatures. His studies often bridge high-energy physics and complex systems. Main Research Trends: Over 15 years, Sannino has produced 333+ publications, emphasizing: Black hole physics and effective metrics Standard Model extensions and dualities Quantum field theory at conformal windows Epidemiological modeling of pandemics Awards: Elected Member of the Finnish Academy of Science and Letters (2015) EU Excellence Grant in Theoretical Physics (2005) International Referee for Austrian Science Fund Grants & Projects: Leader of the DG Center for Particle Physics Phenomenology (2014–2019) Carlsberg Foundation Semper Ardens grant (2023–2029) Coordinator for Danish CERN Instrument Center (2017–2019) Labs/Teams: Active in CP³ - Center for Particle Physics Phenomenology and DIAS, collaborating globally on projects like gravitational wave detection and pandemic modeling.
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.
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.
Konstantin Wernli is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His research focuses on quantum field theory, geometric quantization, and mathematical physics, with a particular emphasis on topological field theories and perturbative methods. He has contributed to foundational work in Chern-Simons theories, BV-BFV formalisms, and geometric analysis. His research interests include quantum field theories, algebraic geometry, and the intersection of topology with physics. Notably, he explores combinatorial approaches to quantum field theory, geometric quantization frameworks, and the application of advanced mathematical tools to solve problems in theoretical physics. Recent work includes studies on partition functions, constrained dynamical systems, and the globalization of sigma models. His articles often bridge abstract mathematics with physical applications, such as analyzing heat kernels, theta invariants, and entanglement polytopes. Wernli is a project participant in the Sapere Aude grant 'FROM PERTURBATIVE TO NON-PERTURBATIVE QUANTUM FIELD THEORY BY CUTTING AND GLUING' (2024–2028), which aims to advance non-perturbative QFT techniques. He has advised on research projects involving heat kernel analysis and geometric quantization, though no formal student advisees are listed.
Shachar Carmeli is a researcher in the Department of Mathematics at the Weizmann Institute of Science. He previously held a postdoctoral position at the University of Copenhagen's Department of Mathematical Sciences, working with the Copenhagen Centre for Geometry and Topology. Carmeli earned his PhD from the Weizmann Institute under the supervision of Professor Dmitry Gourevitch, focusing on advanced topics in algebraic topology and representation theory. His research interests span homotopy theory, chromatic homotopy theory, algebraic geometry, and representation theory, with a current emphasis on higher semiadditivity in chromatic homotopy theory and its connections to algebraic K-theory. He also explores Nash stacks and geometric representation-theoretic frameworks. Notable collaborations include work with Tomer M. Schlank, Lior Yanovski, and Allen Yuan on cyclotomic extensions, redshift phenomena, and categorified trace constructions. Carmeli's recent publications highlight contributions to topics like chromatic Fourier transforms, descent in algebraic K-theory, and the topology of crystalline materials. His work bridges abstract algebraic structures with geometric and topological applications, often leveraging tools from higher category theory and spectral algebraic geometry.
Søren Eilers is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on Operator Algebras , particularly the classification of C*-algebras related to discrete and low-dimensional structures. He is a member of the FNU network 'Automorphisms and Invariants for Operator Algebras' and advocates for experimental mathematics using computational methods in pure mathematics. Education: MS in Mathematics and Computer Science, University of Copenhagen (1993) PhD in Mathematics, University of Copenhagen (1995) Research Interests: Operator Algebras K-theory Symbolic Dynamics Discrete Mathematics Experimental Mathematics Recent Publications (2016-2024) demonstrate expertise in graph C*-algebras , symbolic dynamics , and computational approaches to pure mathematics, with key collaborations in Denmark, Japan, Canada, and the U.S. Scientific Leadership: President, Danish Mathematical Society (2006-2008) Principal Investigator, Villum Fonden (2012-2016) Main Organizer, Mittag-Leffler Institute Program (2016) Advisory Roles: Supervised 28 master's theses and mentored 9 PhD students/postdocs (2003-2022) across institutions in Denmark, Canada, Japan, and the U.S.
