Ravi Seshadri is an Associate Professor in the Transport Division at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research focuses on designing equitable, efficient, and sustainable mobility solutions with a focus on fiscal instruments like congestion pricing and tradable permits, as well as emerging mobility modes such as shared and demand-responsive transit. He employs methods from transportation network equilibria, dynamic traffic assignment, and agent-based simulation. His research interests span transportation economics, urban planning, and intelligent transportation systems. Key areas include evaluating the impacts of automated mobility-on-demand systems, optimizing tolling strategies using predictive control and reinforcement learning, and integrating multi-modal transportation networks through game-theoretical frameworks. His work emphasizes real-world applications in urban freight systems, e-commerce logistics, and sustainable urban mobility policies. Recent projects include studying congestion pricing schemes via agent-based microsimulation, analyzing behavioral responses to decarbonization policies, and developing frameworks for tradable credit systems with peer-to-peer trading. He has contributed to both theoretical advancements (e.g., robust traffic assignment models) and applied tools like the SimMobility simulation platform. Ravi's research demonstrates a strong focus on bridging transportation engineering with policy analysis, using cutting-edge computational methods to address complex urban mobility challenges. His work spans academic publications, industry collaborations, and policy consultations to advance sustainable transportation systems.
Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.
Charles Marcus is a Professor at the University of Copenhagen's Niels Bohr Institute, holding the Villum Kann Rasmussen Chair in Quantum Sciences. He directs the Center for Quantum Devices and Microsoft Station Q – Copenhagen, while affiliating with the Niels Bohr International Academy. Education : Stanford University (B.S. 1984), Harvard University (Ph.D. 1990), IBM Postdoctoral Fellow (1990-92) Employment : Faculty at Stanford (1992-2000), Harvard (2000-2011), and UCPH (2012-present) His research focuses on experimental condensed matter physics, particularly quantum coherent electronics in semiconductors/superconductors. Key areas include spin qubits for quantum computing, Majorana modes in nanowires, quantum Hall systems, and superconductor-semiconductor hybrids. Recent work explores topological quantum information schemes and novel magnetic resonance imaging approaches. Scientific publications span quantum devices, Josephson junctions, and topological materials. Awards include the H.C. Ørsted Gold Medal, AAAS Newcomb-Cleveland Prize, and fellowships from AAAS and APS. He serves on advisory boards for quantum technology centers globally. Significant Awards : H.C. Ørsted Gold Medal (2020) Industry Prize, Danish Academy of Natural Sciences (2019) Member, National Academy of Sciences (2018) Award for Research Excellence in Nanotechnology (2014) Professional Roles : Director, Center for Quantum Devices (2012-2019) Lab Director, Microsoft Quantum (2016-2021) Scientific Director, Harvard Center for Nanoscale Systems (2004-2009)
Ole Nørregaard Jensen is a Professor in Biomedical Mass Spectrometry and Systems Biology at the Department of Biochemistry and Molecular Biology, University of Southern Denmark . His research integrates advanced mass spectrometry, proteomics, and bioinformatics to study chromatin biology, post-translational modifications, and cellular signaling networks. He is actively involved in major research initiatives funded by Novo Nordisk Foundation and Lundbeck Foundation. His research interests include Mass Spectrometry, Proteomics, Posttranslational Modification, Histone Biology, Chromatin Biology, Bioinformatics, Systems Biology, Lipidomics, and Protein Chemistry . He employs cutting-edge techniques such as tandem mass spectrometry and ion mobility spectrometry to analyze protein isomers and dynamic modifications. His work has significant implications for understanding gene regulation, DNA replication, and disease mechanisms. His recent publications demonstrate a strong trend in chromatin dynamics, epigenetics, and integrated omics approaches , combining proteomics with transcriptomics and lipidomics to unravel complex biological systems. His research spans from fundamental molecular mechanisms to translational applications in biomedicine and food science. He has been recognized with several prestigious awards: MCP Lectureship Award Juan Pablo Albar Proteomics Pioneer Award 2019 EliteForsk 2009 prize Knight Order of Dannebrog (Ridder af Dannebrogordenen) Jensen is deeply involved in academic service, including peer review for journals like Nature Communications and Molecular and Cellular Proteomics , organizing conferences, and supervising students. He teaches courses such as Biomedical Mass Spectrometry - Principles and Applications and coordinates the Computational Biomedicine international Master’s program. He leads multiple active research projects, including PLATO and INTEGRA, focusing on health data, imaging, and protein networks. He is a key member of a vibrant research environment in biomedical mass spectrometry at SDU, contributing to both national and international scientific collaborations. His lab is at the forefront of developing and applying novel mass spectrometry methodologies for systems biology.
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.
Shashi Raj Pandey serves as Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, Denmark. His research is anchored in the Connectivity section and Connectivity Classique-Center for Classical Communication in the Quantum Era, with office location at Fredrik Bajers Vej 7C, C1-111, 9220 Aalborg Øst. His core research spans Network Economics, Game Theory, and Wireless Networks, with specialization in Decentralized Machine Learning and Semantic/Goal-oriented Communications. Current work integrates Digital Twin technologies with 6G systems for industrial automation and earth observation, emphasizing resource-efficient protocols for Internet of Things and edge intelligence applications. Recent publications (2024-2025) reveal a clear trajectory toward AI-6G convergence, featuring semantic communications for satellite imaging, game-theoretic network resource allocation, and digital twin implementations for autonomous systems. Key themes include communication efficiency in distributed learning and physical-digital world integration. Notable recognitions include: Best PhD Thesis Nominee (2021) Excellent Paper at Korea Software Congress, KIISE 2021 Student Best Paper Award at APNOMS 2019 Best Paper at Korea Software Congress, KIISE, 2018 Brain Korea 21st Century Plus Fellowship Academic service includes external PhD examination for EU SNS projects and peer review for premier conferences (AAAI, ICLR, ICML). His lab work within the Connectivity Classique-Center explores classical communication frameworks applicable to quantum-era networks, with focus on semantic information theory and decentralized network architectures.
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.
Petar Popovski is a Professor at the Department of Electronic Systems within the Technical Faculty of IT and Design at Aalborg University, Denmark. His research focuses on next-generation wireless communication systems, with a strong emphasis on ultra-reliable low-latency communication (URLLC), Internet of Things (IoT), multiple access, and 6G technologies. He leads several high-impact research projects, including the Classique - Center for Classical Communication in the Quantum Era funded by the Danish National Research Foundation and WATER (Wireless Architectures for intelligent and Trusted connectivity in the posT-5G ERa) supported by Villum Fonden. His research interests span key areas in modern communication theory and systems, including random access , non-terrestrial networks , satellite communication , and machine learning for reliable communication . He is actively involved in advancing the integration of sensing and communication, digital twin technologies, and quantum-era classical communication frameworks. The recent publications highlight a strong trend toward deterministic and reliable access in wireless networks, integration of sensing and communication for industrial automation, and novel physical-layer techniques using reconfigurable intelligent surfaces. These works are published in top IEEE journals such as IEEE Transactions on Communications , IEEE Transactions on Haptics , and IEEE Transactions on Vehicular Technology . Award highlights include the Best Student Paper Award (2021) , recognizing his mentorship and collaborative research excellence. Prof. Popovski serves as a principal investigator (PI) and supervisor in multiple research projects, securing significant funding from national and international bodies such as the Danish National Research Foundation and the European Space Agency (ESA). He hosts visiting researchers regularly and contributes to scientific leadership through editorial roles and conference participation. He is a key figure in the Connectivity section at Aalborg University and leads cutting-edge research in future wireless systems, contributing to both theoretical foundations and practical implementations in smart infrastructure, space communication, and dependable 6G networks.
Yan Kyaw Tun is a Tenure Track Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, located in Copenhagen, Denmark. His research lies at the intersection of wireless communications, edge computing, and artificial intelligence, with a strong focus on next-generation networks (5G/6G), UAV-assisted systems, and intelligent resource management. His educational background includes a Ph.D. in Computer Engineering from Kyung Hee University, South Korea, where he was awarded the Best Ph.D. Thesis Award in 2021, and a Bachelor of Engineering in Marine Electrical Systems and Electronic Engineering from Myanmar Maritime University. Dr. Tun's research interests span Edge Computing , Multi-Access Edge Computing (MEC) , Resource Allocation , Unmanned Aerial Vehicles (UAVs) , Reinforcement Learning , Energy Efficiency , and Integrated Sensing and Communication (ISAC) . His work leverages AI and optimization techniques to enhance the performance of wireless networks, particularly in space-air-ground integrated systems and satellite-HAP environments. The recent publications highlight a clear trend toward intelligent and sustainable networking: the integration of STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces), Federated Learning for satellite-HAP systems, and AI-driven optimization for UAV trajectories and beamforming. These works are published in high-impact venues such as IEEE Transactions on Mobile Computing and IEEE ICC , showcasing his leadership in cutting-edge communication technologies. His scientific accolades include: IEEE ComSoc Outstanding Young Researcher Award for EMEA Region (2024) Best Ph.D. Thesis Award (2021) Student Best Paper Award at APNOMS 2019 Korea Network Operation and Management Conference Award (2020) Korea Computer Congress 2018 Award Dr. Tun is actively engaged in the academic community as an advisor and grant participant. Though no direct advisees are listed, his involvement in large collaborative projects—evidenced by co-authorship with senior researchers like Prof. Choong Seon Hong—indicates mentorship and team leadership. He has served on the editorial boards of IEEE Internet of Things Journal , IEEE Open Journal of the Communications Society , and IEEE Network , and has secured research support through participation in IEEE-organized workshops and special issues. He is a key organizer of upcoming workshops, including the 'Sustainable AI for Next-Generation Wireless Communications and Networking' at IEEE GLOBECOM 2025 and the 'Digital Twin Networks' workshop at IEEE/CIC International Communications in China 2025, reflecting his role in shaping future research directions in intelligent and green networking.
Ilias Chalkidis is an Assistant Professor specializing in Natural Language Processing at the Department of Computer Science, University of Copenhagen. He is actively affiliated with the Natural Language Processing research section, contributing to both theoretical and applied advancements in the field. His research spans multiple high-impact domains with particular emphasis on: Legal natural language processing and multilingual legal reasoning Large language model applications in political and social contexts Fairness-explainability trade-offs in AI systems Innovative representation learning techniques for textual data Analysis of his recent publications reveals a strong focus on bridging legal informatics with cutting-edge NLP methodologies. His work on multilingual legal corpora (including the 689GB MultiLegalPile dataset) and legal decision influence prediction demonstrates practical applications for judicial systems. Simultaneously, his investigations into LLMs as voting assistants and European political spectrum analysis showcase innovative intersections between computational social science and language technology. His technical contributions to contrastive learning and hyperbolic embeddings provide foundational advances for document representation. Chalkidis actively participates in the research community through workshop organization (Natural Legal Language Processing Workshop 2023-2024) and conference presentations. His research has been published in top-tier venues including ACL, EMNLP, and ECAI, with significant citations reflecting community impact. While specific advising relationships aren't documented in the provided materials, his collaborative work patterns suggest active mentorship within the NLP research ecosystem.
Teresa Anna Steiner serves as an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, specializing in algorithmic research with emphasis on privacy-preserving computational methods and theoretical computer science. Her research centers on differential privacy mechanisms, where she investigates trade-offs between data utility and privacy guarantees through rigorous analysis of noise injection techniques like Laplace and Gaussian distributions. She extends this work to dynamic graph databases requiring real-time privacy protections and develops novel text indexing approaches for regular expression pattern matching, contributing to foundational advancements in algorithm design for sensitive data environments. Recent 2025 publications reveal a cohesive research trajectory focused on practical implementations of differential privacy across diverse data structures, with particular attention to variance optimization in noise mechanisms, edge-level privacy in evolving graphs, and efficient indexing for textual pattern recognition. These works collectively address critical challenges in balancing computational efficiency with robust privacy guarantees in modern data systems. No scientific awards were documented in the available information. Details regarding student advising or research grant funding were not specified in the provided materials.
Lars Dittmann is a Professor at the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), leading the Networks Technology and Service Platforms section. His work bridges advanced networking technologies with real-world applications in healthcare and transportation. Academic Role: Professor, Head of Section University: Technical University of Denmark (DTU) Department: Networks Technology and Service Platforms Research Interests: Professor Dittmann specializes in Software-Defined Networking (SDN) , 5G and IoT technologies , and energy-efficient network design , with a focus on applications in telemedicine and transportation systems . His work integrates machine learning for privacy-preserving traffic analysis and explores optical networks for high-bandwidth scenarios. Scientific Contributions: His recent publications emphasize green cellular networks using SDN/NFV/C-RAN, IoT benchmarking for coverage and mobility, and secure edge architectures for railways. Collaborative projects like the Future Patient telerehabilitation program highlight his interdisciplinary impact. Supervision: He supervises PhD candidates such as Radheshyam Singh, focusing on SDN-based IoT security and 5G network optimization. Labs & Projects: Leads initiatives like EXplorative network PLAnnINg and Broadband Trial Integration , addressing challenges in network reliability , emergency communication , and optical data center scaling .
Athanasios Kolios serves as Professor and Head of the Structural Integrity and Loads Assessment section within the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His work focuses on advancing wind energy technology through structural analysis, materials science, and system optimization for both onshore and offshore applications. His primary research domains include wind turbine structural integrity, offshore wind farm layout optimization, and structural health monitoring systems. He investigates critical challenges in operation and maintenance strategies, techno-economic metrics for wind projects, and materials behavior under dynamic loading conditions. His fingerprint analysis reveals dominant expertise in Wind Turbine Engineering (100%), Offshore Wind Turbines Engineering (45%), and Offshore Wind Farms Engineering (36%). Recent publications demonstrate strong emphasis on data-driven approaches for wind energy systems, including structural optimization algorithms, virtual sensing techniques, and techno-economic assessments of offshore projects. His work consistently bridges theoretical engineering principles with practical industry applications, particularly in Brazilian and European offshore wind contexts. Scientific Awards: No scientific awards were documented in the provided information Professor Kolios actively supervises five PhD candidates across major research initiatives while mentoring Master's students in structural wind energy applications. His research portfolio includes six significant projects addressing critical industry needs: PhD Supervision: Chopard (anomaly interpretation), Piovesan (risk-based technology qualification), Yildirim (floating turbine uncertainty), Rodrigues Faria (autonomous operation), Al-Hagri (offshore structure maintenance) Master's Supervision: Rushil S. Mahajan (monopile buckling analysis) Current Projects: Decision support systems, life cycle cost modeling, floating wind turbine modeling, autonomous operation frameworks, sustainable offshore structure design As section head at DTU Wind and Energy Systems, he leads an internationally collaborative research group focused on structural integrity assessment, load prediction methodologies, and materials innovation for next-generation wind turbines. The team maintains strong industry partnerships and contributes to global wind energy standards development through participation in initiatives like ISSC.
Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.
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.
