Arash Nemati is an Assistant Professor at the Department of Energy Conversion and Storage, Technical University of Denmark (DTU). His research focuses on sustainable energy technologies, particularly solid oxide fuel cells, ammonia-fueled systems, and multiphysics modeling of energy conversion processes. He leads and participates in EU-funded projects such as RESCUE and X-SEED, aiming to advance renewable energy storage and green hydrogen production. His work addresses challenges in electrochemical systems, including durability, efficiency, and integration of renewable energy sources. He has received notable recognition, including a Highly-cited Paper award (Web of Science, 2022) and the DAAD Green Hydrogen Research Tour Scholarship (2024). Nemati actively supervises PhD students in areas like co-electrolysis and solid oxide electrolysis cell modeling. His expertise spans thermochemical processes, numerical simulations, and techno-economic evaluations of energy systems. Key projects include developing ammonia-driven reversible solid oxide cells for grid storage and optimizing pyrolysis-electrolysis systems for methanol and char production. His research aligns with UN Sustainable Development Goals, emphasizing clean energy and climate action.
Elham Ramin is a Researcher at the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), affiliated with the Center for Energy Resources Engineering (CERE) and the Process and Systems Engineering Centre (PROSYS). Her work contributes to multiple UN Sustainable Development Goals, particularly in clean water and sanitation, affordable and clean energy, and industry innovation. Dr. Ramin completed her PhD at DTU (2010-2014) with research focused on modeling water quality in sewer-WWTP systems. Her academic journey demonstrates a strong foundation in environmental process engineering with applications to real-world water treatment challenges. Her research interests span wastewater treatment optimization, Power-to-X applications for water resource recovery, industrial symbiosis in water management, and biomanufacturing process modeling. She specializes in computational fluid dynamics, activated sludge modeling, and one-dimensional simulation models for wastewater treatment plants. Her work bridges environmental engineering with sustainable resource management, focusing on practical solutions for water-energy nexus challenges. Analysis of her recent publications reveals a strong trend toward integrating sustainable energy solutions with water treatment processes, particularly Power-to-X technologies. Her research portfolio shows increasing focus on digitalization of water resource recovery facilities and cross-sectoral industrial symbiosis for optimal resource utilization. The work demonstrates strong interdisciplinary connections between environmental engineering, chemical process modeling, and sustainable development. Dr. Ramin has participated in significant research projects including ERASE (Evaluation of Resource recovery Alternatives in South African water) and GECKO (Green and Circular Innovation for Kenyan Companies), demonstrating international collaboration and application of research to diverse water management contexts. Her work has generated substantial academic interest with multiple publications receiving significant downloads and reader engagement on platforms like Mendeley. She is actively involved with research centers including CERE and PROSYS at DTU, contributing to interdisciplinary teams focused on energy resource engineering and process systems optimization. Her collaborations extend to pharmaceutical industry applications, particularly in vaccine manufacturing development and digital twin implementation for bioprocesses.
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
Beate Conrady is an Associate Professor in Infectious Disease Epidemiology and Animal Health Economics at the Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, where she joined in April 2021. She has been affiliated with the Centre for Sustainable Health (CSH) since May 2020. Previously, she led the working group on Epidemiology and Animal Health Economics at the University of Veterinary Medicine in Vienna for eight years. Her research focuses on the development and application of mathematical and economic models to assess prevention and intervention strategies for infectious diseases in veterinary and public health contexts. Key areas include zoonoses, cattle disease control, biosecurity, and One Health. She has extensive experience in international consultancy, including with EFSA, the Austrian Government, and European scientific networks. Beate's recent publications (2020–2024) demonstrate a strong emphasis on disease transmission modeling, surveillance systems, and economic evaluation of health interventions. Her work spans Salmonella, foot-and-mouth disease, Cryptosporidium, and vector-borne diseases, often employing advanced modeling techniques and large-scale data analysis. She has received more than 11 scientific awards for her contributions to infectious disease epidemiology and animal health economics. Consultant for European Food Safety Authority (EFSA) Member of international scientific committees (Med-Vet-Net, DISCONTOOLS) Principal investigator and consortium leader with over 7.5 million EUR in research funding, including more than 1 million EUR in third-party funds She is actively involved in editorial and peer-review activities for leading journals and contributes to policy development in animal health and zoonotic disease control.
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 .
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.
N. Asger Mortensen is a Professor and D-IAS Chair at the Danish Institute for Advanced Study , University of Southern Denmark. He serves as Scientific Director of the DNRF Center of Excellence POLIMA , focusing on polariton-driven light-matter interactions. His career spans leadership roles at SDU and DTU, including VILLUM Investigator grants. Education : Dr. scient. (2021) University of Copenhagen; Dr. techn. (2006), PhD (2001), MSc (1998) from Technical University of Denmark. Research Interests : Quantum plasmonics, nanophotonics, metamaterials, optofluidics, and light-matter interactions in structured materials. His work bridges classical electrodynamics and quantum physics, emphasizing nonlocal effects and polaritonic phenomena. Recent Articles : Explore nonlocality in photonic materials, plasmonic systems in 2D materials, and polariton dynamics. Keywords include Nanophotonics , Quantum Optics , and Condensed Matter , with subfields like Surface Plasmons , Exciton Polaritons , and Topological Insulators . Scientific Awards : Fyens Stiftstidendes Forskerpris (2023) Elected Member, Royal Danish Academy of Sciences and Letters (2022) VILLUM Investigator (2017) European Optics Prize (2008, 2004) Grants : Leads DNRF CoE (2023-2029, ~60 MDKK) and VILLUM Investigator (2017-2023, ~40 MDKK). Co-applicant on numerous international collaborations. Editorial Roles : Associate Editor for Science Advances and Nanophotonics , with past roles at Optics Express and Journal of Physics: Condensed Matter .
Wiebke Meesenburg is an Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Thermal Energy. She is actively involved in research on large-scale heat pump systems, district heating integration, and digital twin applications for energy optimization. Her research focuses on sustainable thermal energy systems, particularly the design, monitoring, and optimization of heat pumps in district heating networks. Key areas include dynamic modeling, real-time adaptation, fouling mitigation, and the integration of renewable energy sources. She contributes to advancing energy efficiency and sustainability in urban infrastructure. The recent publications highlight a strong trend toward digitalization and optimization of thermal systems, with an emphasis on model-based monitoring, digital twins, and operation scheduling using advanced algorithms. Her work bridges mechanical engineering, energy systems, and computational modeling to improve system performance and reliability. She has supervised PhD research and contributed to major projects such as the implementation of digital twins for heat pump systems and EnergyLab Nordhavn. Collaborations involve key figures in energy research at DTU, including Professor Brian Elmegaard. While no formal awards are listed, her active participation in conferences and project leadership demonstrates recognition in her field. Wiebke Meesenburg has been involved in organizing and presenting at international events, including the 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems and workshops on Modelica and flexible heat supply. Her work is embedded in interdisciplinary teams focused on future energy infrastructures and smart urban energy systems.
Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.
Jesper Liniger is an Associate Professor at AAU Energy within the Faculty of Engineering and Science at Aalborg University. He works in the Esbjerg Energy Section focusing on Offshore Renewable Energy Systems and is affiliated with AAU BLUE – Marine & Maritime Research. His office is located at Niels Bohr Street 8, 6700 Esbjerg, Denmark. Research Interests Marine Growth Engineering and automated cleaning solutions for offshore structures Underwater robotics including Remotely Operated Vehicles (ROVs) and autonomous inspection systems Wind turbine engineering with emphasis on hydraulic pitch systems and fault detection Fluid power engineering applications in marine environments Development of robotic solutions for offshore renewable energy infrastructure Research Trends Dr. Liniger's recent publications demonstrate a strong focus on developing robotic solutions for offshore renewable energy infrastructure. His work bridges theoretical control systems with practical marine applications, particularly addressing marine growth (biofouling) challenges on offshore structures. The research shows increasing interdisciplinary collaboration, combining robotics, fluid mechanics, and wind energy systems to create integrated solutions that improve operational efficiency and reduce maintenance costs in offshore environments. Scientific Awards Innovation Project of the Year (2024) - For underwater robotics development Esbjerg Universitetspris (2018) - University award recognizing research excellence Advising and Research Leadership Dr. Liniger actively supervises PhD students and serves as principal investigator or supervisor on multiple major projects including "NextGen Robotics" for offshore wind farms and "Towards Enhancing Perception and Navigation for Autonomous Underwater Inspection Drone." His research portfolio includes collaborations with industry partners like Vattenfall and Business Center Funen, demonstrating strong industry-academia connections focused on practical applications with economic impact. Research Teams and Facilities Liniger is part of AAU BLUE – Marine & Maritime Research, which provides specialized facilities for marine robotics testing and development. His work involves close collaboration with researchers in control systems, fluid mechanics, and renewable energy. The research group has developed experimental frameworks for testing underwater and surface vehicle operations, with recent media coverage highlighting their innovative approaches to solving marine growth challenges on offshore structures.
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.
Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at the Central European University in Vienna, and a Research Professor at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest. He leads the Computational Human Dynamics Lab, focusing on data-driven modeling of social and biological systems. He is also the Editor-in-Chief of the journal Advances in Complex Systems . His research interests lie at the intersection of network science, human dynamics, and socioeconomic systems. He specializes in temporal and spatial networks, modeling contagion processes (both social and biological), and analyzing large-scale human behavioral datasets. His work integrates computational methods with real-world data to understand complex social phenomena such as mobility patterns, migration, segregation, and epidemic spread. He is particularly known for using remote sensing and digital trace data to infer poverty and socioeconomic conditions in urban areas. The recent publications highlight a strong trend in applying network science and machine learning to societal challenges. His work spans high-impact journals in complex systems, data science, and computational social science, with recurring themes in epidemic modeling, urban analytics, socioeconomic inference, and the structure of temporal and spatial networks. The research is highly interdisciplinary, combining physics, computer science, and social science methodologies. He has been invited to speak at major events such as the Conference on Complex Systems, the Lake Como School on Complex Networks, and workshops on data for vulnerability assessment. He served as general co-chair of CCS 2021 in Lyon, demonstrating leadership in the complexity science community. General Co-Chair, Conference on Complex Systems (CCS) 2021, Lyon Invited speaker, 4th Workshop on Data for the Wellbeing of the Most Vulnerable @ ICWSM'23 Invited speaker, Complexity72h Workshop Invited lecturer, Lake Como School on Complex Networks Invited talk, Hungarian Academy of Sciences on COVID-19 modeling While specific grant details are not listed, his coordination of projects on segregation, migration, and poverty inference—often in collaboration with the Complexity Science Hub—suggests active involvement in externally funded interdisciplinary research. He advises students through the Department of Network and Data Science at CEU, though specific advisees are not named. His lab, the Computational Human Dynamics Lab, serves as a hub for data-driven research on social systems.
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.
Rasmus Bjørk is a Professor at the Technical University of Denmark (DTU) in the Department of Energy Conversion and Storage. His research focuses on advanced materials for energy systems, particularly in magnetocaloric and elastocaloric cooling, magnetic materials, and additive manufacturing for functional devices. His work contributes to the UN Sustainable Development Goals, especially in affordable and clean energy. PhD Supervision: Active projects include energy storage using topological spin textures, magnetothermal waste heat harvesting, and bio-magnetometers. Key Research Areas: Magnetic refrigeration, energy harvesting, and freeze-casting of functional materials. Recent advancements include 3D-printed elastocaloric coolers and studies on magnetoresistive devices. His team develops novel techniques for optimizing magnetic systems and energy conversion processes. He has published over 160 articles and led projects on regenerator design, magnetic bearings, and sensor technologies. Collaborations span multiple countries and disciplines. Notable contributions include pioneering work on freeze-casting for biomaterials and the MagTense micromagnetic framework. His research bridges theoretical modeling and practical applications in sustainable energy solutions.
