Yassine Ghannane is a Research Fellow at the Department of Computer Science , University of Copenhagen , specializing in Algorithms and Complexity . University: University of Copenhagen Department: Department of Computer Science Research Focus: Theoretical computer science, permutation-based evolutionary algorithms, computational complexity His recent work includes runtime analysis and theory development for permutation-based evolutionary algorithms, as well as module-based neural network mapping heuristics. Publications span 2022–2024 with interdisciplinary applications in machine learning and optimization. Contact: yagh@di.ku.dk | Office: Universitetsparken 1, 2100 København Ø, Denmark
Christian Igel is a Professor at the Department of Computer Science, University of Copenhagen, and serves as director of the SCIENCE AI Centre . He is also a co-lead of the Pioneer Centre for Artificial Intelligence in Denmark. His academic journey includes a Doctoral degree from Bielefeld University (2002) and a Habilitation degree from Ruhr-University Bochum (2010). Igel is a Juniorprofessor (2002–2010) and has held editorial roles at journals like KI - Künstliche Intelligenz and Artificial Intelligence Journal . Doctoral degree: Faculty of Technology, Bielefeld University, Germany (2002) Habilitation degree: Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany (2010) His research spans Machine Learning , focusing on Support Vector Machines , Evolution Strategies , Reinforcement Learning , Deep Neural Networks , and PAC-Bayesian Analysis . He applies these methods to Environmental Monitoring , Medical Diagnostics , and Climate Research . Recent publications highlight work on adversarial machine learning , environmentally sustainable AI , and tree resource mapping using deep learning. His scientific awards include being a ELLIS Fellow . Igel’s software tools like Shark , woody , and Multi-Planar UNet are widely used in research and industry. Notable grants and collaborations involve projects with European Lab for Learning and Intelligent Systems (ELLIS) , SCIENCE AI Centre , and international teams in Denmark , Germany , and France . His lab leadership emphasizes open-source frameworks and reproducible research. Editorial Roles: German Journal on Artificial Intelligence , Evolutionary Computation Journal , Artificial Intelligence Journal Software Projects: Shark , woody , Multi-Planar UNet , U-Time Collaborations: SCIENCE AI Centre , Pioneer Centre for Artificial Intelligence , European Lab for Learning and Intelligent Systems
Qiongxiu Li is a Tenure-Track Assistant Professor in the Cyber Security group at Aalborg University's Copenhagen campus, part of the Technical Faculty of IT and Design. Her research focuses on cybersecurity, distributed optimization, privacy/security, and federated learning. She has authored/co-authored 38 papers in top-tier venues including IEEE Transactions on Information Forensics and Security, ICLR, and EUSIPCO. Education: PhD in Privacy and Security from Aalborg University (2018-2021). Notable achievements include winning the EUSIPCO 2020 3MT Contest and co-delivering a tutorial on privacy-preserving distributed optimization at EUSIPCO 2024. She actively reviews for conferences like NeurIPS, ICLR, and journals such as TPAMI and TIFS. Research Themes: Privacy-preserving distributed algorithms, federated learning security, differential privacy, and adversarial machine learning. Recent Trends: Focus on securing AI systems (e.g., LLM vulnerabilities, federated clustering privacy), quantization for privacy, and theoretical bounds in decentralized learning. Awards: 2020 EUSIPCO 3MT Winner (outstanding finalist in EURASIP's annual doctoral research competition). Grants/Projects: Co-PI of the AI:SECURITY project (2025-2029) addressing AI security threats like phishing and malicious actors. Labs/Teams: Leads the Cyber Security group at Aalborg's Copenhagen campus, focusing on theoretical and applied research in secure distributed systems.
Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.
Anker Degn Jensen is a Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), where he is affiliated with the CHEC Research Centre. His research spans chemical reaction engineering, catalysis, and particle technology with applications in energy and sustainability. His research interests include chemical reaction engineering , catalysis , combustion , gasification of solid fuels , flue gas cleaning (especially NOx and Hg removal), production of liquid fuels , and fluidized bed processes for coating and agglomeration in white biotechnology. His work significantly contributes to UN Sustainable Development Goals related to clean energy and climate action. The recent publications highlight a strong trend in bio-oil upgrading , ammonia synthesis , plasma-assisted methane conversion , and adsorption modeling . These works reflect a deep engagement with sustainable fuel production, catalytic process optimization, and fundamental surface reaction mechanisms. Conversion of Furfural as a Bio-Oil Model Compound An Adsorption Isotherm That Includes Interactions Plasma-Assisted Non-Oxidative Coupling of Methane Optimisation of a Haber-Bosch Synthesis Loop Production of Phenolic Compounds from Argan Shell Waste Anker Degn Jensen is actively supervising multiple PhD students and leading research projects in hydrogen production, CO2 and H2O electrochemical reduction, bio-oil hydrotreating, and catalytic upgrading of biomass. He is involved in over 100 projects, including active grants on Conversion of hydrocarbons to hydrogen , Modeling electrochemical CO2 reduction , and Catalytic upgrading of pyrolysis oil . He is associated with the CHEC Research Centre at DTU, a hub for chemical engineering and catalysis research. His team collaborates on advanced reactor designs and catalytic processes for renewable fuels and chemicals.
Philip Loldrup Fosbøl is an Associate Professor in the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), College of Engineering. He is actively affiliated with CERE – Center for Energy Resources Engineering, where he conducts research on CO 2 capture, storage, transport, and utilization. His work integrates thermodynamic modeling, process simulation, and pilot-scale experimentation to address challenges in carbon management and sustainable energy systems. His research interests include: Carbon Dioxide Capture and Storage (CCS) Thermodynamics and Phase Equilibrium of Electrolyte Solutions Process Design, Simulation, and Optimization CO 2 Corrosion in Energy Systems Biogas Upgrading and Cleaning CO 2 Utilization and Conversion Development of Predictive Thermodynamic Models Mobile and Large-Scale Pilot Facilities for CO 2 Capture His recent publications (2025) highlight a strong focus on biogas upgrading, solvent degradation in industrial CO 2 capture, thermophysical property measurements, and novel electrochemical separation methods. These works reflect a consistent trend toward energy-efficient, scalable, and industrially applicable solutions for decarbonization, particularly in flue gas and biogas treatment. Scientific awards received: Top PhD Thesis of the Year (2008) He actively supervises multiple PhD students and leads research projects funded by industrial partners such as Ørsted, Shell, Equinor, and Novozymes, as well as EU initiatives including CASTOR, iCap, and OCTAVIUS. His work contributes to UN Sustainable Development Goals related to climate action and affordable, clean energy. He is involved in laboratory research on thermodynamic equilibrium (VLE, SLE), heat capacity, corrosion mechanisms, and core flooding for CO 2 storage. His team develops experimental methods and operates pilot facilities for CO 2 capture and biogas cleaning, often in collaboration with key researchers like Kaj Thomsen, Nicolas von Solms, and Georgios Kontogeorgis.
Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.
Morten Birkved is a Professor at the Department of Green Technology, University of Southern Denmark, and Head of SDU Life Cycle Engineering. He leads the SDU Climate Cluster and actively contributes to research on Life Cycle Assessment (LCA) , Circular Economy , and Environmental Sustainability . His work spans cross-disciplinary collaborations in Denmark and internationally. Education: M.Sc. in Environmental Chemistry, PhD in Sustainable Development His research interests focus on integrating LCA with circular economy principles, analyzing environmental impacts of construction materials, and developing sustainable solutions for agriculture and energy systems. Recent projects include microbial protein production, green fuels in shipping, and biorefineries for organic waste valorization. Recent publications highlight 2025 advancements in: Eco-design strategies for buildings Hybrid wastewater treatment systems Carbon-storing asphalt pavements Comparative LCA of housing typologies These studies emphasize Denmark as a case study region and leverage both parametric and probabilistic modeling approaches. Scientific Awards Recipient of SCC Fast Track Prize (2025) Teaching & Supervision Supervised PhD research on circular agriculture and plastic circularity Lectured on chemical processes and environmental impacts since 2019 Labs & Projects Coordinates EU-funded initiatives like FLAVOURFERM (plant-based fermentation) and AgriLoop (circular agriculture) Develops microbial platforms for waste-to-protein conversion Advances biostimulants for sustainable agriculture
Navid Bayati is an Associate Professor at the University of Southern Denmark, affiliated with the Institute of Mechanical and Electrical Engineering and the Centre for Industrial Electronics. He leads the Control and Protection of Smart Grids (CAP-SG) group and focuses on renewable/hybrid power systems, microgrid protection, and grid code compliance. Education: Ph.D. in Power Systems & Microgrid Protection (2020, Aalborg University); M.Sc. in Power Systems (2017, Amirkabir University of Technology) His research spans renewable energy integration , transient analysis , grid interconnection , and digital twin applications . Recent work includes machine learning for carbon emission prediction, fault localization in DC microgrids, and supercapacitor resilience in hybrid systems. Collaborations include projects like IEA Wind Task 50 and RePoSys , addressing grid renovation, life cycle assessment, and digital twin resilience. His teaching portfolio covers power electronics , energy management , and microgrid control .
Pooya Davari is a Professor and Head of the Section for Applied Power Electronic Systems at Aalborg University , Denmark. He leads the EMI/EMC in Power Electronics Research Group and serves as Vice Chair of the Energy Efficiency Mission. His research focuses on electromagnetic interference (EMI) and harmonic mitigation in power electronic systems, with over 200 publications and significant contributions to renewable energy integration. Education: B.Sc. and M.Sc. in Electronic Engineering (2004, 2008), Ph.D. in Power Electronics from Queensland University of Technology (2013) Prior Roles: Lecturer at QUT (2013–2014), Postdoc at AAU (2014) Research Interests: Harmonic and EMI analysis in grid-tied converters High power density converter design Signal processing for converter modeling Reliability of power electronic systems Article Trends: Recent work emphasizes EMI/EMC in renewable energy systems, wide bandgap semiconductors (SiC/GaN), and reliability modeling for EVs and hydrogen production via electrolysis. Sub-fields include converter topologies, grid integration challenges, and AI-driven diagnostics. Scientific Awards: Equinor 2022 Prize (Denmark’s oldest engineering award) IEEE EMC Society Young Professional Award (2020) World’s Top 2% Highly Cited Scientist (Stanford, 2021–2025) Multiple best paper awards (IEEE, Applied Sciences, etc.) Grants & Editorial Roles: Recipient of grants from Innovation Fund Denmark (Supra-EMC project), Horizon Europe (SOLARIS), and industry partnerships. Serves as Area Editor for IEEE Transactions on Transportation Electrification , Associate Editor for IEEE Transactions on Power Electronics , and Editor-in-Chief of Circuit World Journal (2020–2025). Labs & Standards: Coordinator of the EMC Laboratory at Aalborg University. Member of IEC standardization Working Groups 6 and 8 (TC77A), focusing on EMC strategies for power grids.
Mark van Loosdrecht is a Full Professor of Environmental Biotechnology at Delft University of Technology (The Netherlands). He holds academic ranks including Prof.dr.ir. and is affiliated with the Faculty of Applied Sciences and the Department of Environmental Biotechnology. His research focuses on biofilm processes, nutrient conversion, and wastewater treatment innovations such as the Anammox, Nereda, and BCFS processes. He has pioneered technologies for resource recovery and sustainable water treatment. Dr. van Loosdrecht has published over 700 scientific papers, holds 15 patents, and supervised over 50 PhD students. He is Editor-in-Chief of Water Research , a member of prestigious academies (KNAW, AcTI, NAE), and has received major awards including the Stockholm Water Prize and Lee Kuan Yew Water Prize. His work integrates scientific discovery with practical process development, addressing global environmental challenges. Education: MSc/PhD from Wageningen University. Active roles include leadership in the International Water Association (IWA) and editorial boards. Key achievements include the development of full-scale processes like Nereda aerobic granular sludge technology. He has been knighted in the Order of the Dutch Lion and recognized with the Beijing Great Wall Friendship Award. Research interests emphasize microbial ecology, bioremediation, and sustainable engineering solutions. His contributions span from fundamental microbiology to industrial-scale process implementation, with a focus on circular economy principles in water management.
Yang Cheng is an Associate Professor at the Department of Materials and Production, Aalborg University, Denmark. He holds a PhD in Mechanical Engineering from the same institution (2011), focusing on manufacturing strategy and network dynamics. His research spans supply chain management, sustainability, and global operations, with a focus on integrating technology and environmental policies into manufacturing systems. He leads or participates in high-impact projects like MAASive (2024–2026) and the Sino-Danish Center Research Project (2011–present), addressing resilience in value networks and global operations innovation. Research Interests: Supply Chain Management & Integration Sustainability & Green Technologies Manufacturing Strategy & Networks Technology Policy & Digitalization Global Operations & Cross-Border Collaboration Recent Work Trends: Prof. Cheng's 2025 articles emphasize blockchain in sustainable supply chains, green technology investments under carbon policies, and digitalization's ethical implications. His 2024 research explores smart factories, EU battery regulations, and robotization in manufacturing. These studies blend quantitative models with case-based analysis to address real-world challenges. Awards: 2024 Emerald Literati Awards – Outstanding Reviewer Advising & Grants: As PI for multiple Global Operations Management PhD programs (2019–2025), he guides research on digital transformation and university-industry collaboration. His projects receive funding from Danish and international grants, focusing on innovation and resilience in manufacturing networks. Labs/Teams: Collaborates with the Center for Industrial Production at Aalborg University and engages in international partnerships through the Sino-Danish Center. Active editorial roles include Production Planning & Control and Journal of Manufacturing Technology Management .
Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications
Shuang Ma Andersen is a Full Professor at the Department of Green Technology (IGT) and SDU Chemical Engineering, specializing in electrocatalysis, fuel cell technologies, and sustainable resource recovery. Her research focuses on oxygen evolution reaction (OER) catalysts, membrane electrode assemblies, and innovative synthesis methods for iridium/platinum-based systems.
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.
