Fabio Pierella is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Wind and Energy Systems Flows, specializing in Wind Turbine Design Division. His research focuses on offshore wind energy systems, fluid dynamics, and structural engineering. He has contributed to projects like OC6 Phase IV and the DeRisk database, validating numerical models for floating offshore wind structures and extreme wave loads. Key research interests include computational fluid dynamics (CFD), hydrodynamic load modeling, and the design of large-scale floating wind turbines. His work spans numerical simulations, experimental validation, and database development for extreme sea states. Pierella has presented at international conferences on topics like wave-structure interaction and turbine control systems. He received the Best Poster Presentation Award (2024) and contributed to datasets such as the DeRisk Database, which provides critical wave data for offshore wind turbine design. His research emphasizes practical applications, including monopile structural integrity under extreme loads and control strategies for floating platforms. Pierella's activities include conference presentations on ultra-large floating turbines (EMULF2 project) and the impact of wave shape on 15MW turbine loads. His interdisciplinary approach integrates computational models with experimental results to address challenges in offshore renewable energy systems.
Ali Akhavan is an Assistant Professor at the Faculty of Engineering and Science , Aalborg University, specializing in Electric Power Systems and Microgrids . His work focuses on grid-connected inverters, microgrid stability, and advanced control algorithms. Research Interests: Control systems for power electronics, stability analysis in asymmetrical grids, passivity-based control, and harmonic compensation. Projects: Participated in CROM (Villum Foundation), SYNCHRONY (private funding), and ASSET (Horizon Europe) to develop high-performance converter systems for renewable energy integration. Scientific Awards: Recipient of the Best Paper Award (May 2021). Email: alak@energy.aau.dk Publications Trend: 15 recent works emphasize grid-forming inverters, harmonic voltage compensation, and stability analysis in renewable energy systems. Key subfields include power quality, passivity enhancement, and dynamic response optimization.
Jun Yang is a Tenure Track Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research spans computational statistics and machine learning, with a focus on high-dimensional inference, time series analysis, and Monte Carlo methods. Current Position: Tenure Track Assistant Professor, University of Copenhagen (2023–present) Previous Role: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Education: Ph.D. in Statistics, University of Toronto (2020), advised by Daniel M. Roy and Jeffrey S. Rosenthal Research Interests: Jun’s work addresses the intersection of computational statistics and machine learning, including: - High-dimensional Markov chain Monte Carlo (MCMC) algorithms - Bayesian variable selection in complex models - Spectral inference for nonlinear time series - Quantitative bounds and complexity analysis for MCMC Publications: His publications highlight advancements in high-dimensional sampling, time series analysis, and algorithm design. Key contributions include: - Dimension-free mixing results for Bayesian variable selection - Stereographic projection techniques for MCMC - State-domain change point detection in nonlinear regression Awards: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Collaborations: Jun collaborates with researchers like K. Łatuszyński, G.O. Roberts, and J.S. Rosenthal, advancing statistical theory and applications in econometrics, machine learning, and stochastic processes.
Sara Shafiee is a Senior Researcher at the Department of Civil and Mechanical Engineering , Technical University of Denmark (DTU) . She specializes in product configuration systems, manufacturing engineering, and AI-driven innovation. Her work bridges technical systems with organizational agility, emphasizing sustainability and customer-centric design. External Roles: Founder & CEO of DivERS (Jan 2021–) External Lecturer at Copenhagen Business School (2022–2024) Senior Business Consultant at Haldor Topsoe AS (2017–2019) Research Focus: Her work addresses challenges in product configuration systems, generative AI applications, and sustainable construction. Key themes include: Optimal product design through recommendation systems Agile methodologies in knowledge-intensive development Environmental impact monitoring via configurators Publications Trends (2023–2025): Recent work explores AI-driven manufacturing optimization, consumer-centric innovation strategies, and the integration of environmental monitoring into design systems. High-impact areas include generative AI applications (13K+ downloads) and modular construction configurators. Awards: Agnes & Betzy Award (2025) Nordic Women in Tech Leadership Award (2022) Best Digital Startup (Venture Cup Denmark, 2021) Innovation Fund Denmark Role Model (2018) Advising & Grants: Supervised PhD projects on recommendation systems and configurator design. Lead PI of the RECODE project (DFF Grant DKK 10M+, 2024–2027) focusing on deep learning for engineer-to-order systems. Labs & Teams: Core member of DTU’s Design and Manufacturing Systems group, collaborating with industry partners like Haldor Topsoe and DivERS to develop scalable configurator solutions.
Lesia Mitridati is an Assistant Professor at the Department of Wind and Energy Systems, Technical University of Denmark (DTU). Her research focuses on optimizing energy systems, particularly in renewable energy integration, energy market design, and prosumer behavior modeling. She leads and collaborates on projects involving smart grids, distributed energy resources, and privacy-preserving market mechanisms. Her work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key projects include AI-driven electricity market optimization, hydrogen-wind trading strategies, and risk-aware energy communities. She supervises multiple PhD students in areas like VPP bidding strategies and market-based heat-electricity coordination. Dr. Mitridati has published widely on energy communities, grid services, and reinforcement learning applications. Notable contributions include dynamic pricing frameworks for grid services and privacy-preserving market mechanisms. She co-organizes annual DTU summer schools on future energy systems and AI-driven optimization. Her research integrates machine learning with operational research techniques to address challenges in renewable energy integration, market design, and system resilience. Current initiatives focus on electrolyzer plant bidding strategies and feature-driven trading of renewable resources.
Juan C. Vasquez is a Professor at Aalborg University's Faculty of Engineering and Science, Department of Energy Technology, and Co-Director of the Center for Research on Microgrids (CROM). He holds a PhD in Automatic Control from the Technical University of Catalonia and has held academic positions at Aalborg University since 2011. His research focuses on microgrid control, renewable energy integration, power electronics, and smart grids. He has supervised numerous PhD and master’s students and leads projects funded by EU and national grants. Education: BS in Electronics Engineering (Autonomous University of Manizales, Colombia, 2004); PhD in Automatic Control (Technical University of Catalonia, Spain, 2009). Research interests include operation and control strategies for AC/DC microgrids, maritime microgrids, energy management systems, and IoT integration in smart grids. He has authored 648+ publications, including highly cited works, and received awards like the Young Investigator Award (2019) and Clarivate’s Highly Cited Researcher status since 2017. Key projects: EU-DREAM (Digital Services for Energy Transition), NEST (National Research Infrastructure), and ActRes (Resilience in Energy Systems). Collaborations include Virginia Tech and Ritsumeikan University.
Mogens Fosgerau is a Professor at the Department of Economics, University of Copenhagen, with a research focus on discrete choice theory, rational inattention, transportation and urban economics, congestion modeling, and entropy-based frameworks. He has held an ERC Advanced Grant (2017-2023) and completed a Grand Solutions project for the Innovation Fund Denmark (2016-20). Education: Mathematical Economics (Aarhus University, 1990), PhD in Mathematics (University College London, 1992). Current affiliations: Department of Economics (University of Copenhagen), Faculty of Social Sciences. Former roles: Guest Professor at DTU (2022-2023), member of the Commission for Green Transition of Passenger Cars (2019-2021). His research explores the intersection of information theory and discrete choice models, addressing complex substitution patterns and endogeneity issues through generalized entropy frameworks. He applies these models to transportation planning, urban economics, and climate policy analysis. Recent publications focus on perturbed utility models, inverse product differentiation logit, and rational inattention in spatial choice contexts. His work bridges theoretical econometrics with practical transport and environmental policy challenges. Awards: Recipient of the 2021 Transportation Science Meritorious Service Award. Former Editor-in-Chief of Economics of Transportation (2012-2020). Advising and Grants: Leads research projects funded by the European Research Council and Innovation Fund Denmark. Has participated in policy committees including the Danish Environmental Economic Council (2019-2025) and the Committee on Public Transport Mobility (2023-24).
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.
Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.
Jes Frellsen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU) since 2016. Previously, he held academic positions at the IT University of Copenhagen (2016-2019), postdoctoral roles at University of Cambridge (2013-2016) and University of Copenhagen (2011-2013). Education: PhD in Bioinformatics (2011), University of Copenhagen MSc in Bioinformatics (2007), University of Copenhagen BSc in Mathematics and Computer Science (2005), University of Copenhagen EAP Exchange at University of California, Santa Cruz (2004-2005) Research Focus Jes Frellsen specializes in statistical machine learning , particularly generative AI and deep generative models with applications in bioinformatics . His work integrates Bayesian inference , directional statistics , and Markov chain Monte Carlo methods to address challenges in macromolecular structure prediction and missing data imputation . Recent efforts explore uncertainty quantification in image segmentation and generative modeling for materials science. Advising & Collaborations He actively supervises PhD students and postdoctoral researchers in projects spanning news recommendation systems , medical imaging , and 3D structure generation . Collaborations include work with Zoubin Ghahramani (Cambridge) and Thomas Hamelryck (Copenhagen), with contributions to protein structure prediction and statistical methods in structural bioinformatics .
Martin Aumüller is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen . He serves as Head of Education and Master of Software Design , focusing on algorithm engineering, differential privacy, and similarity search. Research interests include: Algorithm engineering for high-dimensional data Locality-sensitive hashing and nearest neighbor search Privacy-preserving machine learning Fairness in approximate search algorithms Benchmarking and evaluation of similarity search tools Publications trends highlight his work on approximate nearest neighbor search , privacy-preserving techniques , clustering algorithms , and scalable outlier detection in high-dimensional spaces. His recent projects (2024-2025) focus on fairness, differential privacy, and efficient indexing. Grants and projects : DIREC (2020-2025): Digital Research Centre Denmark (Innovation Fund Denmark) DIREC: Bias and Benefit of Approximate Nearest Neighbor Search (2022-2025): Principal Investigator (Innovation Fund Denmark) BARC (2017-2024): Basic Algorithms Research Copenhagen (Villum Fonden) SSS (2014-2019): Scalable Similarity Search (European Commission)
Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.
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.
Carsten Baum is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark, working within the Cybersecurity Engineering Center for Quantum Technologies. His research focuses on cryptography, particularly post-quantum security, secure multi-party computation, and quantum-resistant protocols. Primary Affiliation: Technical University of Denmark (DTU) Academic Rank: Associate Professor Research Centers: Cybersecurity Engineering Center for Quantum Technologies Research Interests: Carsten Baum specializes in cryptographic protocols, including zero-knowledge proofs, multi-signatures, and oblivious computation. His work addresses quantum computing threats to classical cryptography and develops resilient post-quantum solutions. Key areas include: Secure multi-party computation (MPC) Post-quantum key agreement Quantum computing's impact on cryptographic security Privacy-preserving technologies Hash function security analysis (e.g., SHA-3) Time-based cryptographic primitives Advising and Projects: As a main or co-supervisor, Baum mentors multiple PhD candidates in post-quantum cryptography and secure protocols. His projects include proactive post-quantum cryptography, quantum key agreement, and long-term security frameworks, reflecting collaborations with institutions across Europe and Asia. Recent Activities: Baum actively organizes and participates in Nordic cryptography workshops, including NordiCrypt Spring 2024 and 2023, fostering academic exchange in cryptographic research.
Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.
