Shan He is a Researcher at Aalborg University's Faculty of Engineering and Science, specializing in Power Electronic Control, Reliability, and System Optimization. Their work focuses on advancing grid-connected inverter technologies, voltage control stability, and renewable energy integration. Key projects include reliability modeling of photovoltaic inverters, control strategies for wind power systems, and harmonics mitigation in power electronics. Research interests encompass battery technology, feedforward control systems, and dissipativity-based methods. Collaborations span academic and industrial partners, addressing challenges in grid resilience and energy conversion efficiency. Over 66 publications and 5 active/funded projects highlight contributions to power electronics and renewable energy systems. Email: she@energy.aau.dk
Michael R. Rasmussen is a Professor and Head of Academic Council at the Department of the Built Environment within Aalborg University's Faculty of Engineering and Science. His research focuses on urban hydrology, environmental engineering, and the application of meteorological radar for rainfall estimation and stormwater management. He leads the Urban Hydrology Research Group and has been involved in numerous projects addressing water quality, flood mitigation, and sustainable urban infrastructure. Key research contributions include advancing methods for integrating weather radar data with rainfall sensors to improve urban drainage system design and real-time control strategies. He has supervised four PhD students and contributed to over 180 publications, including influential works on combined sewer overflow management and bathing water safety. Rasmussen actively participates in academic and industry collaborations, serving on boards such as Dryp A/S and the Department of the Built Environment. His work bridges hydraulic engineering, environmental science, and data-driven solutions, with applications in Denmark and beyond. Notable projects include optimizing stormwater detention ponds using control algorithms and developing models to predict water quality impacts from fecal contamination in lakes.
Galadrielle Humblot-Renaux is a Research Fellow at Aalborg University's Technical Faculty of IT and Design, affiliated with the Department of Architecture, Design and Media Technology and the Section for Media Technology in Aalborg, Denmark. Her work focuses on AI-driven solutions for computer vision, robotics, and uncertainty quantification in machine learning systems. Key Research Areas Out-of-Distribution Detection and Robustness Testing 3D Semantic Segmentation and Point Cloud Processing Uncertainty Quantification in Renewable Energy Systems Human-Robot Interaction and Speaker Identification Marine Ecology Image Analysis via Multi-Annotator Datasets Scientific Contributions She has created two influential datasets: JAMBO (2024) for underwater benthic habitat classification and Why Talk to People When You Can Talk to Robots? (2021) for far-field speaker identification challenges. Her publications across 2018-2025 demonstrate interdisciplinary expertise bridging AI theory with practical applications in robotics, automotive systems, and ecological monitoring.
Dalin Zhang is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He holds a position within the Technical Faculty of IT and Design. His research focuses on machine learning, sensor data analysis, and their applications in healthcare, smart cities, and human-computer interaction. Key areas include EEG-based emotion recognition, time series forecasting, and graph neural networks. He leads the PREDICTION-ENABLED DYNAMIC SCHEDULING OF HOUSEHOLD ELECTRICITY USAGE project (2023–2024), demonstrating expertise in energy systems optimization. His work bridges theoretical advancements with practical applications, such as developing lightweight models for real-time traffic flow prediction and creating benchmarks for emotion recognition systems like LibEER. Research interests span: EEG signal processing for emotion and intention recognition Efficient time series analysis and forecasting Graph neural networks and adversarial defense mechanisms Data privacy in wearable sensor systems Recent publications highlight innovations like Pure-GNN (graph neural networks against adversarial attacks), LightCast (traffic forecasting), and Self-Supervised EEG Representation Learning. He advises two PhD students but their names are not listed in the provided texts. His work often emphasizes computational efficiency and real-world deployment across healthcare, smart infrastructure, and consumer electronics.
Jonas Brusokas works as a researcher at the Department of Computer Science , Technical Faculty of IT and Design , Aalborg University. His research focuses on the intersection of Machine Learning , Internet of Things , and Energy Systems , particularly addressing energy flexibility and data compression challenges for IoT applications. Key research areas include time-series forecasting for energy systems, error-bounded lossy compression techniques, and data-driven modeling of heat pumps. His work is applied in smart energy grids, medical imaging, and extreme-scale computing environments. Recent publications highlight trends in device-independent flexibility measurement , model confidence-based forecasting , and IoT data compression for machine learning platforms. Collaborations span energy engineering, computer science, and medical imaging domains. Jonas has contributed to 8 peer-reviewed publications since 2019, with projects like Machine and Deep Learning for Flexible Energy (2020–2023) as Principal Investigator. His research involves partnerships with institutions in Denmark, Singapore, and Greece.
Matthias Rehm is a Professor at the Department of Architecture, Design and Media Technology, Aalborg University (AAU). His work focuses on Human-Robot Interaction (HRI), trust assessment in social robotics, and culturally-aware technology. He leads projects such as RETRO (Regulating Trust in HRI) and IndiKnowTech , exploring participatory design with indigenous communities. His research spans industrial collaboration robots, ethical AI, and robots in educational settings. Key projects include developing trust metrics using EEG and motion data, co-designing robots for children with disabilities, and investigating robots' role in fostering sustainable consumption. He has collaborated internationally, publishing over 160 articles and datasets. Active in EU-funded initiatives, his work bridges technical innovation with sociocultural context, emphasizing ethical and human-centric approaches. Education: Background in computer science and robotics (details not explicitly stated) Affiliations: Center for Applied Game Research, AAU Technical Faculty Grants: Multiple Danish and EU grants for HRI projects Research interests also include multimodal interaction, mobile learning ecosystems, and robotics in healthcare. His work contributes to UN SDGs related to sustainable consumption and quality education.
Stefan Ehlers Jespersen is a Postdoctoral Researcher at the Faculty of Engineering and Science, Aalborg University, Denmark, affiliated with the Esbjerg Energy Section. His work centers on produced water treatment for offshore oil and gas operations, specializing in hydrocyclone separation, oil-in-water monitoring, and advanced process control. His research focuses on enhancing hydrocyclone efficiency for microplastics capture and de-oiling processes through advanced sensing technologies like 3D fluorescence spectroscopy and online microscopy. He integrates deep learning with model predictive control to optimize real-time water treatment, addressing critical environmental challenges in offshore production. Recent publications demonstrate a strong trend toward AI-driven solutions for oil-in-water concentration prediction and dynamic efficiency monitoring, leveraging pilot plant data to validate control strategies for sustainable operations. Dr. Jespersen has secured principal investigator roles for projects like OPTIMAL CONTROL OF DE-OILING HYDROCYCLONES and contributes to eight major initiatives including the Carbon Transformation & Energy Control Hub (C-TECH), with funding from Danish Hydrocarbon Research and Technology Centre and EUDP. He collaborates within research teams utilizing pilot facilities for sensor fusion and control system validation, focusing on real-world implementation of water treatment innovations in offshore environments.
Karoline Assifuah Kristjansen is a Clinical Instructor at the Department of Clinical Medicine, Faculty of Medicine, Aalborg University. Her work focuses on integrating AI technologies into healthcare research methodologies, infectious disease biomarker discovery, and clinical trial design. She has contributed to studies evaluating ChatGPT's role in systematic reviews, metabolic signatures of viral diseases, and perioperative management in oncology. Her research interests emphasize applying machine learning to clinical workflows, optimizing prognostic marker identification for disease severity, and advancing surgical treatment protocols through pharmacological pathway analysis. Recent publications highlight innovations in healthcare informatics and translational medicine. Collaborations include multi-center clinical trials and international teams studying metabolomics applications. While no formal awards are listed, her work demonstrates significant contributions to clinical research methodologies and patient outcome prediction systems.
Søren Kejser Jensen is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University. He is affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on time series management systems, lossy compression techniques, and their applications in renewable energy analytics, IoT data management, and edge computing. He has contributed to the development of ModelarDB, a scalable model-based time series management system. Key research areas include: Optimizing lossy compression for sensor data and wind turbine analytics Designing scalable database solutions for high-frequency data streams Integrating machine learning with compressed IoT data Exploring freshness metrics in communication systems His publications highlight advancements in edge-cloud architectures, model-based data management frameworks, and holistic analytics for renewable energy systems. Collaborations extend to global projects like 6G wireless systems and AI-driven multi-network deployments.
Associate Professor Arnt Louw is affiliated with Aalborg University's Department of Culture and Learning within the Faculty of Social Sciences and Humanities. His research focuses on vocational education, youth studies, and educational environments. He leads and contributes to projects addressing student retention, educational equity, and the integration of theory and practice in upper secondary education. Notable projects include 'Hvordan giver det mening at være ung på EUD?' (2023-2025) and 'Faglig tilblivelse og øget gennemførsel på SOSU H' (2022-2026). His work explores student motivation, assessment practices, and the challenges faced by young adults in vocational training programs. Recent publications analyze changes in student profiles (2025), grading systems (2023), and municipal youth policies (2024). He actively engages with stakeholders through talks and media contributions, advocating for inclusive educational environments and pedagogical innovation. Louw collaborates with organizations like the Centre for Youth Research (CeFU) and participates in national and international networks such as NordYrk. His research emphasizes practical applications, including strategies for strengthening student reflection and bridging classroom learning with workplace experiences.
Lorenzo Dall'Amico is a Postdoctoral Researcher at ISI Foundation in Turin, Italy, and an assistant professor at the University of Torino, where he teaches a course on complex networks. He is a key member of the research team led by Professor Ciro Cattuto, focusing on the analysis of complex, temporal, and proximity networks with applications in epidemiology, social sciences, and human behavior modeling. PhD in Signal, Image, Speech, and Telecommunications, 2021 – Université Grenoble Alpes Master in Physics of Complex Systems, 2018 – Politecnico di Torino M2 in Physics of Complex Systems, 2018 – Paris Sud (XI) BSc in Physical Engineering, 2016 – Politecnico di Torino His research lies at the intersection of statistical physics, mathematics, and computer science. He develops interpretable representations of high-dimensional network data, with a particular focus on temporal and proximity networks. His work has significant applications in epidemic modeling, public health, and social dynamics. He is deeply involved in interdisciplinary projects such as COVID-19 Real Time Epidemiology and Periscope , both aimed at improving pandemic response through data-driven modeling. His recent publications span top journals including Nature Communications , Science Advances , and Physical Review E , with themes centered around temporal graph embeddings, community detection in dynamic graphs, and the integration of socioeconomic factors into epidemic models. He has developed the open-source Julia package CoDeBetHe.jl for efficient spectral community detection in static and dynamic graphs, which is widely used in network science research. His scientific contributions include: Development of embedding-based distances for temporal graphs Creation of generalized contact matrices for improved epidemic modeling Efficient algorithms for distributed representations using SoftMax normalization Analysis of household and school-based contact patterns in disease transmission Lorenzo actively mentors through open science practices, releasing code and datasets alongside his publications. He has advised or collaborated with researchers across disciplines, contributing to projects funded by the Botnar Foundation. His work emphasizes data for good , applying advanced network science to real-world public health challenges. He leads and contributes to research teams focused on digital epidemiology and network-based modeling, leveraging high-resolution proximity data from projects like SocioPatterns. His future work aims to further bridge theoretical network science with practical applications in public health policy and intervention design.
Pablo A. Astudillo Estévez is an Assistant Professor at the School of Economics, Universidad San Francisco de Quito (USFQ), where he also serves as Director of the USFQ Data Hub. He holds affiliations as an Associate Researcher at the Institute for New Economic Thinking (INET), University of Oxford, and as External Faculty at the Complexity Science Hub, Vienna. He is concurrently working as a Data Scientist at the World Bank-IBRD in the Finance, Competitiveness, and Innovation Global Practice. His research interests lie at the intersection of computational social science, economic geography, and complexity economics. He specializes in analyzing large-scale datasets to understand economic spatial dynamics, innovation ecosystems, and the structure of production and supply networks. His work applies network science and machine learning to reconstruct and model firm-level economic interactions, with implications for systemic risk, inequality, and policy design. His recent publications focus on reconstructing firm-level input-output and supply chain networks, analyzing inequality in exposure to economic shocks, and understanding the foundational structures of economic complexity. These works reflect a strong trend toward data-driven, micro-founded models of economic systems, leveraging machine learning and complexity science. Scientific Affiliations and Roles: Assistant Professor, School of Economics, USFQ Director, USFQ Data Hub Associate Researcher, INET Oxford External Faculty, Complexity Science Hub, Vienna Data Scientist, World Bank-IBRD Former Research Fellow, Harvard Growth Lab Former Visiting Fellow, MIT Media Lab He has advised governments and private sector institutions and has consulted for the Inter-American Development Bank. His international experience spans institutions in the UK, USA, Japan, South Korea, and Romania. He earned his doctorate in Economic Geography and Complexity Economics from the University of Oxford. He is actively engaged in building research capacity in Ecuador and promoting data science applications in economic development. He is also vocal about academic and research challenges in Ecuador, particularly regarding funding and ecosystem support.
Matteo Biagetti is a Researcher at the RIT Institute, located within the Area Science Park in Trieste, where he actively contributes to the LADE research group. His work bridges artificial intelligence, theoretical physics, and applied mathematics, with a focus on understanding fundamental aspects of the universe and developing novel data analysis techniques. Research Interests: Dr. Biagetti's research spans the physics of the primordial Universe and its imprints on large-scale structures. More recently, he has been pioneering applications of topological data analysis to complex datasets arising in both cosmology and machine learning. His interdisciplinary approach integrates advanced mathematical tools with modern AI to extract meaningful structures from high-dimensional data. The recent publications and talks, such as The Shape of Data: From Galaxies to Neural Networks , reflect a growing trend toward applying geometric and topological methods across domains—from astrophysics to neural network interpretability—demonstrating a strong trajectory in data-driven fundamental science. Scientific Awards: Dutch VENI grant (2018), worth €250,000 Advising and Grants: While no formal students are listed, Dr. Biagetti has led independent research following the receipt of the prestigious VENI grant, indicating a capacity for research leadership and mentorship. His funding success underscores recognition of his innovative research program. Labs and Teams: He is an active member of the LADE research group at the RIT Institute, focusing on interdisciplinary research at the intersection of AI, physics, and mathematics.
Horst Bischof is Rector and Professor at Graz University of Technology, affiliated with the Institute for Computer Graphics and Vision in the Faculty of Computer Science. He is an active academic leader, currently serving as Treasurer and previously as Vice Rector for Research. He is also a board member of the Fraunhofer Institute for Graphical Data Processing (IGD) and the scientific board of Joanneum Research. His research focuses on computer vision and pattern recognition, with key interests in object recognition, visual learning, motion and tracking, visual surveillance, biometrics, medical computer vision, and adaptive methods. His work bridges theoretical advances with real-world applications in security, healthcare, and intelligent systems. The recent publications (2010–2025) demonstrate a consistent trajectory in computer vision, emphasizing deep learning, neural networks, image analysis, and pattern classification. His work spans subfields such as medical imaging, surveillance, biometrics, and adaptive vision systems, reflecting both technical depth and interdisciplinary impact. Scientific Awards: 29th Pattern Recognition Award (2002) Main Prize of DAGM (2007) Main Prize of DAGM (2012) Best Scientific Paper Award at BMCV (2007) BMVC Best Demo Award (2012) Best Scientific Paper Award at ICPR (2008) Best Scientific Paper Award at ICPR (2010) Best Scientific Paper Award at PCV (2010) Best Scientific Paper Award at AAPR (2010) Best Scientific Paper Award at ACCV (2012) Horst Bischof has advised numerous students and researchers, though specific names are not listed. He has served in leadership roles in major conferences including CVPR (General Chair 2015), ECCV (Program Co-Chair 2006), and DAGM/ÖAGM (Chair 2012), and as Area Chair for top vision conferences. He is an Associate Editor for IEEE TPAMI, Pattern Recognition, and other leading journals. His research has been supported by extensive grants, though specific funding sources are not detailed. He leads research activities at the Institute for Computer Graphics and Vision, contributing to collaborative projects and international initiatives in computer vision and AI.
Rosario N. Mantegna is a Professor at the University of Palermo and holds visiting positions at Central European University and honorary status at University College London. Since 2017, he has been a member of the External Faculty at the Complexity Science Hub Vienna, reflecting his active engagement in interdisciplinary research on complex systems. His research lies at the intersection of statistical physics and economics, where he is recognized as one of the pioneers of econophysics and economic networks . His work explores financial markets through the lens of complex networks, high-frequency trading, information transfer, and data-driven modeling. He has led and participated in numerous international and national research projects, contributing foundational insights into market structure and dynamics. The recent publications (2017–2023) highlight a strong focus on network analysis in finance , clustering techniques , information theory applications , and fintech evolution . These works span disciplines including physics, finance, computer science, and data analytics, demonstrating a consistent interdisciplinary approach. Key themes include validation of network structures, trader clustering, and systemic risk assessment. His scientific contributions are recognized through editorial roles in major conference proceedings and collaborations with leading researchers in complexity science. While no formal awards are listed, his sustained publication record in high-impact journals and books underscores his influence in the field. Rosario Mantegna advises students and researchers through his leadership in research projects and academic supervision, though specific advisees are not named. He has been involved in significant research grants and collaborative initiatives across Europe, particularly in complexity science and financial network analysis. His work is closely tied to interdisciplinary research groups and networks, including those at the Complexity Science Hub Vienna and Central European University, where he contributes to advancing the science of complex systems.
