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.
Anders Albrechtslund is a Professor of Information Studies and Director of the Center for Surveillance Studies (CENSUS) at Aarhus University. He specializes in exploring the societal impact of surveillance technologies, particularly in healthcare contexts like dementia care. His research focuses on balancing safety, dignity, and privacy through ethical technology use. He leads major projects such as the Carlsberg Semper Ardens-funded 'Human Agency and Data-Intensive Surveillance' (2024–2029) and the Velux Foundation-supported 'LIVSTEGN' initiative (2019–2025). Albrechtslund teaches in Aarhus University’s Information Studies and Digital Design programs, supervising topics related to surveillance, data ethics, and digital media. He collaborates internationally with public and private sector partners on surveillance technology applications. His work emphasizes empirical ethics and participatory design, addressing dilemmas arising from emerging technologies in caregiving and family contexts. Grants & Projects Carlsberg Semper Ardens Grant: Human Agency in Data-Intensive Surveillance (2024–2029) Velux Foundation Grant: LIVSTEGN (2019–2025) Labs/Teams Directs the Center for Surveillance Studies (CENSUS), a hub for interdisciplinary research on surveillance technologies and societal implications.
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.
Janine Leschke is Professor in Political Economy of Labour Markets at Copenhagen Business School's Department of Management, Society and Communication and a member of CBS Sustainability's Sustainability Governance Group. Her work centers on European labour markets, welfare states, and social policy through institutional and micro-data analysis. Her research focuses on comparative European labour market and welfare state analysis, emphasizing flexibility-security dynamics. Key areas include non-standard employment, job quality, gender disparities, platform work, and social sustainability. She investigates big data and algorithmic impacts on public employment services using institutional frameworks and micro-data. Recent publications (2023-2025) highlight platform work regulation, job quality measurement in digital markets, and intra-EU migration effects on wage structures. Her work consistently examines technological change, social protection, and flexicurity models within European governance. Leschke supervises projects on labour market inequality, digital transformation, migration, and social policy using diverse methodologies. She led the EU Horizon 2020 HECAT project (2020-2023) on disruptive labour market technologies and participates in EUSOCIALCIT, ReNEW, and STYLE through ESPAnet networks. She contributes to CBS's Sustainability Governance Group and European research networks including ESPAnet and Nordic ESPAnet to advance comparative social policy analysis.
Farshad Moradi is a Professor at the Department of Electrical and Computer Engineering at Aarhus University, specializing in neuromorphic engineering, spintronics, and biomedical device design. His work focuses on integrating advanced materials and circuits for applications in neural interfaces, energy-efficient computing, and wireless biomedical systems. Research Interests include: Spintronic-based neuromorphic computing architectures Ultra-low power analog/mixed-signal integrated circuits Ultrasonically powered implantable medical devices Neural signal processing and seizure detection systems Wireless energy transfer and structural health monitoring Key Projects (2016-2026): SPICE: Spintronic-Photonic Integrated Circuit Platform PHOTON-NeuroCom: Photonic-assisted Neuromorphic Computing Neuro-Sense: Flexible bioinspired neuroprostheses CorroSense: Self-powered corrosion monitoring HERMES: Hybrid Enhanced Regenerative Medicine Systems Recent innovations include: Ultrasonically powered optogenetic implants Low-power neural amplifiers for deep-brain interfaces Spin-torque nano-oscillator-based neuromorphic hardware Energy harvesting systems for structural monitoring
Jeppe Rich is a Professor at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His primary research focuses on statistical and mathematical modeling applied to transport-related challenges, including transport demand modeling, discrete choice models, freight transport, cost-benefit assessments, and strategic long-term demand models. Rich holds a Mathematical Planning qualification from the University of Aarhus (1989–1995). He has held external positions as a Senior Consultant at Atkins A/S (2001–2002) and as a Researcher at the National Environmental Research Institute (1995–1998). His work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and climate action (SDG 13). His research interests span transport policy, transportation science, and the application of advanced modeling techniques to address urban mobility challenges. Notable areas include EV infrastructure planning, bicycle network optimization, and cost-benefit analysis of transport projects. He supervises several PhD students in topics like electric freight transport, micromobility safety, and urban charging infrastructure. Rich has published extensively on transport policy, demand modeling, and sustainable mobility solutions. His work emphasizes interdisciplinary approaches to solving complex transport challenges, combining engineering, economics, and data science methodologies.
Andrea Crovetto is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the National Centre for Nano Fabrication and Characterization and the Department of Nanofabrication. His research focuses on advanced materials for photovoltaic applications, including solar cell technologies, thin films, and semiconductor materials. His work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key research interests include photovoltaic materials discovery, tandem solar cell design, and semiconductor characterization. Notable achievements include developing monolithic selenium/silicon tandem solar cells and pioneering studies on phosphosulfide semiconductors. He has authored over 90 publications and datasets, including high-impact articles in journals like JPhys Energy and PRX Energy . Dr. Crovetto has received the Young Scientist Award (2016) and actively supervises PhD students in projects such as Experimental Discovery of Phosphosulfide Materials for Solar Cells and Thiophosphate Thin Films for Quantum Technology . His research also involves collaborations on material synthesis, computational modeling, and device fabrication. He has presented at international conferences, including talks on monolithic tandem solar cells and materials discovery methodologies. His lab, based at DTU’s Produktionstorvet , integrates experimental and theoretical approaches to advance sustainable energy technologies.
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.
Abdulkadir Çelikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark, where he is part of the DKW (Data Science and Knowledge) research group. His research focuses on genome representation learning, graph representation learning, and machine learning applications in bioinformatics and network science. Research Interests: His work lies at the intersection of artificial intelligence and biological data analysis, with a strong emphasis on scalable methods for genome and metagenome representation using k-mer profiles, as well as modeling dynamic and complex networks. He develops novel machine learning models to capture the structure and evolution of graphs over time. Recent Research Trends: His recent publications, appearing in top-tier venues like NeurIPS, AAAI, and AISTATS, demonstrate a consistent focus on improving scalability and effectiveness in representation learning. Key themes include revisiting traditional k-mer methods for modern deep learning, modeling citation dynamics, and developing continuous-time node embedding techniques. His work bridges theoretical advances with practical applications in genomics and network analysis. Scientific Awards: Best Paper Award, TGL Workshop @ NeurIPS 2023 Top Reviewer, LoG 2024 Conference Advising and Grants: While current advisees are not listed, he is actively leading research projects as evidenced by his recent publications and project organization (e.g., Nordic ProbAI summer school). His work is supported through institutional affiliations and likely competitive research funding, given the high-impact venues of his publications. Labs and Teams: He is affiliated with the DKW group at Aalborg University. Previously, he was part of the Inria OPIS team and the Centre for Visual Computing during his Ph.D., and worked in the Section for Cognitive Systems at DTU Compute as a postdoctoral researcher.
Bernhard Palsson is the Scientific Director at the Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain), Technical University of Denmark (DTU). His research focuses on systems biology, metabolic engineering, and computational modeling in microorganisms such as Escherichia coli and Yarrowia lipolytica . Key roles : Scientific Director, Researcher, PhD Supervisor SDG contributions : Biosustainability, Antibiotic Discovery, Climate Action Research Interests : Palsson's work spans metabolic pathways , regulatory networks , and genome-scale modeling . He explores chemical stress tolerance mechanisms, pangenome structures, and integrates machine learning with transcriptomic data to advance biosynthetic applications. His projects include antibiotic discovery via computational resources and metabolic model reconstruction for Lactobacillus and Escherichia coli species. Recent Article Trends : His 2025 submissions highlight interdisciplinary approaches combining machine learning and experimental evolution to enhance biochemical production and address antibiotic resistance. Topics include transcriptional network analysis , pangenome decomposition , and metabologenomic modeling , emphasizing scalability and data-driven methodologies. Supervision & Collaboration : Palsson mentors PhD students like Omid Ardalani and collaborates on projects such as iimena (Integration of Informatics and Metabolic Engineering for Novel Antibiotics) and pangenome metabolic model reconstruction. His team includes researchers from DTU and international institutions. Projects : iimena: Novel Antibiotic Discovery (2017–2023) Lactobacillus Pangenome Modeling (2021–2025) Networks : Collaborated with institutions in 12+ countries Co-authorships in Escherichia coli and Yarrowia lipolytica studies
Kim Klarskov Jeppesen is a Professor of Auditing and Programme Director for the MSc in Accounting and Auditing at Copenhagen Business School (CBS), Department of Accounting. His academic role focuses on advancing auditing practices across private and public sectors. Departments: Department of Accounting, CBS Teaching: Graduate courses in Auditing, Audit Data Analytics, Fraud Investigation, and Internal Auditing Research Interests: Jeppesen's work spans external and internal auditing, public sector accountability, fraud detection, audit regulation, and data analytics. He explores auditors' strategic responses to regulatory challenges and their role in combating corruption. Publications Trends: Recent articles analyze institutional dynamics in auditing, cross-national comparisons of audit frameworks, network perspectives on auditor-board relationships, and the impact of mergers on audit transparency. Key themes include governance, regulatory compliance, and technological advancements in auditing. Supervision: Jeppesen mentors both master’s and PhD students in auditing, emphasizing practical and theoretical rigor. Professional Engagements: He collaborates with organizations like FSR – Danske Revisorer, Beierholm, Mazars, and Rigsrevisionen (2018–2019), contributing to industry-focused teaching and consultancy projects.
Karina Kosiara-Pedersen is an Associate Professor at the Department of Political Science, University of Copenhagen. She coordinates the Center for Voting and Parties (CVAP) and contributes to the Gender & Politics research group. Her work focuses on party membership, organizational development, candidate recruitment, and political harassment. Research keywords include political parties, democracy, electoral systems, and political violence Teaching courses in Danish and Comparative Politics Current research examines gender-based anger in politics (2024-2025), political harassment (DIPAS, 2022-2025), and candidate surveys across municipal/regional/parliamentary elections. She co-leads the Political Parties Database (PPDB) and studies online harassment. Her media contributions (1,193) often address Danish political trends, including recent analysis of electoral shifts and parliamentary dynamics. She serves on the Department’s Faculty board and has been study board chair since 2022.
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.
Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.
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.
