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Ivan Adriyanov NikolovView profile

Assistant Professor

• Computer Graphics
• Computer Vision
• Augmented Reality
• +6 more

Ivan Adriyanov Nikolov is an Assistant Professor at the Department of Architecture, Design and Media Technology within Aalborg University's Technical Faculty of IT and Design. He specializes in Computer Graphics, Computer Vision, and Augmented Reality, with a focus on 3D reconstruction techniques like Structure-from-Motion (SfM). His work bridges academic research and industrial applications, particularly in wind turbine blade inspection and educational technology. His educational background includes contributions to computer science education through innovative teaching methods. He has led projects like 'Drone Application for Pioneering Reporting in Wind Turbine Blade Inspection' (2017–2019) and 'Leading Edge Roughness - Wind Turbine Blades' (2015–2019), advancing drone-based inspection and 3D modeling for wind energy sectors. Research interests include synthetic data generation, environmental monitoring datasets (e.g., BrackishMOT, DigiWeather), and improving VR/AR user experiences. He has developed tools for dynamic lighting in pixel art games and multimodal guardian systems in VR. His datasets, such as Sewer Defect Point Clouds and Wind Turbine Blade SfM Reconstructions, are publicly available for academic use. He actively contributes to educational innovation, such as flipped classroom strategies to boost programming class engagement. His interdisciplinary approach spans computer graphics, AI-driven NPC interactions, and collaborative mixed-reality games for trust-building. Labs/Teams: Member of the Computer Graphics Group and Visual Analysis and Perception team at Aalborg University. Collaborates with industry partners on drone technology and environmental surveillance systems.

Markus StrohmaierView profile

Professor

• Computational Social Science
• Data Science
• Machine Learning
• +7 more

Markus Strohmaier is Professor and Chair of Data Science in the Economic and Social Sciences at the University of Mannheim, with affiliations as Scientific Coordinator at GESIS – Leibniz Institute for the Social Sciences and External Faculty Member at the Complexity Science Hub Vienna. His interdisciplinary work bridges computer science, economics, and the social sciences. University of Mannheim – Chair for Data Science in the Economic and Social Sciences GESIS – Scientific Coordinator for Digital Behavioral Data Complexity Science Hub Vienna – External Faculty Former Professor at RWTH Aachen University and University of Koblenz-Landau Previous Post-Doc and Visiting Roles at Stanford University, Xerox PARC, University of Toronto, and Graz University of Technology His research focuses on computational social science , algorithmic fairness , network science , and the modeling of human behavior using machine learning and large-scale data. He develops methods to analyze textual, relational, and emerging data types to understand socioeconomic systems and digital societies. The recent articles reflect a strong trend in studying inequality in algorithmic systems , governance in decentralized organizations (DAOs) , and psychological profiling of AI . His work spans high-impact journals like Nature and Scientific Reports , emphasizing fairness, transparency, and societal impact of data-driven technologies. Notable scientific contributions include: Editor-in-Chief of EPJ Data Science (2018–2022) Founding co-chair of the Computational Social Science section of the German Informatics Society He advises students and leads research projects on algorithmic fairness, digital governance, and behavioral modeling. His team engages in both fundamental methodological development and applied studies in real-world digital platforms. He has been involved in significant grants and collaborative initiatives around digital behavioral data and computational social science infrastructure. His lab and projects include the Algorithmic Fairness initiative and the interactive visualization tool Planets of Disparity , which explores how algorithms behave on different network structures. These efforts aim to enhance public understanding and technical scrutiny of algorithmic systems.

• Theoretical Computer Science
• Mathematical Logic
• Structured Programming
• +7 more

Siddharth Bhaskar is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research lies at the intersection of theoretical computer science and mathematical logic, with a focus on programming language foundations and formal structures. His research interests include structured programming, imperative languages, graph traversal algorithms, and the application of category theory (particularly functors) to computation. These areas reflect a deep engagement with the mathematical underpinnings of programming and algorithmic processes. The two recent publications show a consistent trend in formalizing programming concepts through mathematical structures—particularly using universal constructions from category theory and extending structured programming into transfinite domains. This indicates a research trajectory grounded in computability, logic, and the semantics of programming languages. No scientific awards mentioned in the provided text. Siddharth Bhaskar has no listed advisees or grant information in the available data. However, his peer-reviewed publications in high-quality conference proceedings (CiE and MFCS) suggest active research supervision and potential involvement in collaborative projects, though specific details are not disclosed. There is no mention of specific laboratories, research groups, or teams in the provided text. However, his affiliation with the Department of Mathematics and Computer Science and his research profile suggest potential collaboration within formal methods or logic research circles at the University of Southern Denmark.

Julian Antony QuickView profile

Researcher

• Wind Power
• Wind Turbine Engineering
• Compressed Air Motors
• +3 more

Julian Antony Quick is a Researcher at the Department of Wind and Energy Systems within the Technical University of Denmark . His work focuses on wind farm optimization, energy management, and market-driven renewable energy systems. Active research in wind farm control and market integration Specializes in hybrid power plant design and uncertainty quantification Contributes to UN Sustainable Development Goals through wind energy research His research explores advanced optimization techniques for wind resource assessment, turbine design, and revenue maximization in electricity markets. Key areas include: Compressed air energy storage integration Wind farm layout optimization Surrogate-based system modeling Dynamic energy management strategies While not explicitly listed, his collaborative projects suggest active involvement in PhD supervision and industry partnerships across Europe. His recent publications demonstrate a strong focus on translating technical advances into economic benefits through: Market-aware wind farm design Data-driven flow control Hybrid system efficiency improvements SDG-aligned sustainable energy solutions

• Theoretical Computer Science
• Algorithms
• Computational Geometry
• +3 more

Ivor van der Hoog serves as an Assistant Professor specializing in Theoretical Computer Science. His institutional affiliation remains unspecified in the provided text, though his departmental focus is clearly centered on foundational computational theory within an academic setting. His research spans core domains of Theoretical Computer Science including Algorithms, Computational Geometry, and Data Structures, with significant emphasis on Discrete Mathematics and Graph Theory. This work addresses fundamental problems in computational efficiency, geometric modeling, and network analysis, contributing to the mathematical underpinnings of computer science through rigorous theoretical frameworks. No scientific awards or honors were documented in the available materials. Information regarding academic advising responsibilities, research grants, laboratory affiliations, or collaborative teams was not present in the current text excerpt, indicating these details require further verification through additional sources.

Andreas PavlogiannisView profile

Associate Professor

• formal methods
• algorithmic verification
• automata theory
• +6 more

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University. His research focuses on formal methods , algorithmic verification , automata theory , concurrency , static and dynamic program analysis , network diffusion , evolutionary graph theory , and evolutionary game theory . Teaching courses: Programming Languages (Bachelor) , Algorithmic Model Checking (Master) , and Program Analysis (Master) Service: Program committee member for POPL, ESOP, AAAI, IJCAI, CONCUR, OOPSLA, and organizer of CONFEST'25 His research has been supported by the Austrian Science Fund (FWF), VILLUM Foundation, Stibo Foundation, and Danish Council for Independent Research (DFF). He is actively recruiting PhD and PostDoc researchers. Recent publications span quantum computing , concurrent systems , evolutionary dynamics , and network science , with particular emphasis on symbolic algorithms , dynamic analysis , and graph-based models .

• Population Genetics
• Bioinformatics
• Genomic Data Analysis
• +6 more

Anders Albrechtsen is a Professor at the Bioinformatics Centre, Department of Biology, University of Copenhagen, specializing in computational and RNA biology. He leads research in statistical and computational methods for genomic data analysis with a focus on population genetics, particularly in isolated populations like the Greenlandic Inuit. Education: Natural Science Basic Education, Roskilde University Centre, Denmark (2000-2002) BSc in Molecular Biology, Roskilde University Centre, Denmark (2003) Mathematics, Roskilde University, Denmark (2002-2004) MSc in Bioinformatics, Copenhagen University, Denmark (2006) PhD from the Department of Biostatistics, Copenhagen University, Denmark (2009) Albrechtsen's research focuses on developing statistical and computational methods for genomic data analysis, with particular emphasis on multi-loci association studies, population structure analysis, and next-generation sequencing data. His work bridges computational methods development with applied population genetics, contributing significantly to understanding human and wildlife genomics. His research group develops open-source software available at www.popgen.dk/software. His recent publications demonstrate expertise across population genetics, genomic methods development, and medical applications, with numerous high-impact papers in journals like Nature, Cell, and Nature Communications. His work often involves large-scale genomic studies of human populations (particularly Greenlandic Inuit) and wildlife species, addressing questions related to population history, adaptation, and disease genetics. Scientific Recognition: Lundbeck Fellow (10M DKK, 2016-2021) Novo Ascending Investigator (10M DKK, 2021-) Villum Young Investigator Programme (2.3M DKK, 2011-2015) Albrechtsen actively supervises students and researchers, currently serving as main supervisor for 6 PhD students (with 8 completed), 2 postdocs, and 1 master's student (with 25 completed). He has secured significant research funding as PI and co-PI, including large collaborative grants like LuCamp (60M DKK) and the UCPH Excellence Programme (36M DKK). His research group maintains strong international collaborations, particularly with institutions in the United States. He serves as a journal reviewer for prestigious publications including Nature Genetics, Genome Research, and American Journal of Human Genetics, contributing to the scientific community through peer review and mentorship.

Bekzod KhakimovView profile

Associate Professor

• Foodomics
• Metabolomics
• Multivariate Data Analysis
• +4 more

Bekzod Khakimov is an Associate Professor in the Department of Food Science at the University of Copenhagen, specializing in Food Analytics and Biotechnology. He has held this position since 2017, following his postdoctoral work (2013-2017) and PhD fellowship (2010-2013) at the same institution. His research group focuses on advanced analytical techniques for food quality assessment and nutritional value determination. His educational background includes: PhD in Plant Metabolomics, Department of Food Science, University of Copenhagen (2013) MSc in Chemical Research, Queen Mary, University of London, UK (2009) BSc in Chemistry, Faculty of Chemistry, National University of Uzbekistan (2006) Dr. Khakimov's research centers on food molecular composition (foodomics) and its health impacts through untargeted molecular screening of bio-fluids (metabolomics). His team works on optimizing standard operating procedures, developing efficient algorithms for processing raw instrumental data, and extracting information from complex omics data using multivariate data analysis (chemometrics). His analytical expertise spans high resolution liquid state NMR spectroscopy, GC-Tof-MS and LC-QTof-MS techniques, with applications ranging from plant foods to dairy products and human health studies. His publication record demonstrates consistent advancement in foodomics methodology and applications, with recent work focusing on metabolomics approaches to understand food quality, nutritional value, and health impacts across diverse food matrices including grapes, potatoes, dairy products, and botanical resources. His research increasingly incorporates advanced statistical modeling and deep learning approaches to overcome analytical challenges like the 'cage of covariance' in metabolomics data. His notable scientific achievements include: Nils Foss Talent Prize (2016) - International Award for ground-breaking science in advanced technologies for improved food quality and safety Best Young Investigator Award in Plant Metabolomics (2015) - 10th International Conference of the Metabolomics Society Dr. Khakimov currently serves as main supervisor for three postdocs, one PhD student, and two MSc students. He has successfully secured multiple competitive research grants as PI or Co-PI from sources including the Independent Research Fund Denmark, UCPH Funding, Danish Dairy Research Foundation, and the European Union. His laboratory maintains state-of-the-art analytical capabilities with responsibility for multiple GC-MS systems, LC-QTof-MS, and co-responsibility for NMR spectrometers. His research infrastructure includes four high-throughput GC-MS systems (Agilent GC-singleQ-MS, LECO Pegasus HT GC-TOF-MS and Bruker EVOQ QQQ systems), one LC-QTof-MS (Bruker Impact II), one LC-UV/Vis-FC system (Thermo Ultimate 3000), and co-responsibility for three NMR spectrometers (600, 500, and 400 MHz) from Bruker.

Diogo Luís Neves dos Santos PiresView profile

Researcher

• Social Data Science
• Computational Social Science
• Data Science
• +2 more

Diogo Luís Neves dos Santos Pires is a postdoctoral researcher at the Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, specializing in interdisciplinary data-driven social science research. His core research interests bridge quantitative methodologies and societal analysis, with emphasis on: Social Data Science Computational Social Science Large-scale societal pattern modeling Algorithmic impact on social structures Ethical data governance frameworks No scientific awards were documented in the available materials. Details regarding student mentorship, research grants, or laboratory affiliations remain unspecified in the provided text, though his SODAS affiliation indicates active participation in collaborative social data science initiatives at the university level.

• Medical Simulation
• Vascular Surgery
• Machine Learning in Medicine
• +3 more

Michael Strøm serves as a Clinical Associate Professor within the Department of Clinical Medicine at the University of Copenhagen's Faculty of Health and Medical Sciences. His institutional affiliation combines clinical practice at the Capital Region of Denmark (Region Hovedstaden) with academic responsibilities in medical education and research. Dr. Strøm's research program focuses on the critical intersection of surgical simulation and artificial intelligence, specifically targeting endovascular procedures. He pioneers machine learning applications for objective proficiency assessment in aortic repair simulations, addressing fundamental challenges in surgical training standardization. His work bridges computational science with hands-on clinical skills development, emphasizing data-driven evaluation methods that transcend traditional subjective assessments. His 2024 publication in Current Problems in Surgery demonstrates a clear research trajectory toward AI-enhanced surgical education, with growing emphasis on quantitative performance metrics and real-time feedback systems. This work reflects broader trends in medical technology where computational methods increasingly validate clinical competencies. Dr. Strøm actively collaborates with the ENHANCE research consortium on vascular surgery innovation. While specific advising roles aren't detailed in available materials, his research output indicates leadership in interdisciplinary projects merging clinical expertise with engineering solutions. His institutional position suggests integration within both university academic structures and regional hospital training programs.

• Multimodal Communication
• Multimodal Corpora
• Spoken Language and Gestural Behaviour
• +6 more

Patrizia Paggio serves as an Associate Professor and Senior Researcher within the Department of Nordic Studies and Linguistics at the University of Copenhagen's Faculty of Humanities, concurrently holding a full professorship at the University of Malta's Institute of Linguistics and Language Technology since September 2011. Her scholarly work centers on the intricate relationship between verbal and nonverbal communication modalities, with international recognition for advancing methodologies in multimodal analysis. Academic Background: PhD in Computational Linguistics from the University of Copenhagen (1997), dissertation: "The Treatment of Information Structure in Machine Translation" Professor Paggio's research program investigates how gestures, head movements, and other nonverbal cues interact with spoken language to construct meaning in natural communication. She has pioneered methodologies for constructing and analyzing multimodal corpora, while maintaining technical expertise in machine translation systems, grammar engineering, and content-based querying frameworks. Her theoretical work spans formal syntactic structures, discourse phenomena, information packaging, and ontological representations for linguistic data, consistently bridging computational methods with linguistic theory. Analysis of her recent publications (2020-2025) reveals a sustained focus on computational approaches to nonverbal communication, particularly the automatic detection and annotation of head movements and gestures in both physical and digital environments. Key contributions include the GEHM Zoom corpus for online interaction analysis, eye-tracking studies of emoji processing, and diachronic modeling of historical language change. Her work strategically integrates eye-tracking, corpus linguistics, and machine learning techniques, establishing her at the convergence of linguistic theory, cognitive science, and artificial intelligence applications. Professional Leadership: Coordinator of the international GEHM (Gestures and Head Movements in Language) research network Organizer of MULTIMODAL CORPORA 2018, 4th European/Nordic Symposium on Multimodal Communication, and LREC2022 Workshop on People in Vision, Language and the Mind

Pourya ForooghiView profile

Associate Professor

• Fluid Mechanics
• Heat Transfer
• Power-to-X
• +3 more

Pourya Forooghi serves as Associate Professor in the Department of Mechanical and Production Engineering at Aarhus University's School of Engineering, Denmark. His active research profile is anchored in the university's heat and fluid flow group, with direct contact available via telephone (+45 93 52 23 03) and email (forooghi@mpe.au.dk). Current activities include conference contributions such as the 2024 ERCOFTAC Symposium lecture on electrolyzer modeling. Research Interests His work spans fundamental and applied fluid dynamics with emphasis on: Turbulent flow over complex rough surfaces (anisotropic, patchy, irregular) Thermohydraulic roughness characterization Data-driven modeling for drag and heat transfer prediction Power-to-X (PtX) energy systems Secondary flows in boundary layers Cryogenic heat transfer (frost formation, evaporators) Publication Trends Analysis of his 2023-2025 publications reveals a dominant focus on roughness effects in turbulent flows using DNS and data-driven methods. Key patterns include hydrodynamic/thermal property characterization of realistic rough surfaces, drag reduction via spanwise forcing, and laminarization techniques in pipe flows. Applications concentrate on energy systems like CO2 heat pumps, PtX electrolyzers, and refrigeration evaporators. Scientific Awards No scientific awards, fellowships, or medals were documented in the provided materials. Advising and Grants The source text contains no explicit information regarding graduate students, postdoctoral advisees, or grant funding activities. His research group involvement suggests likely supervision responsibilities absent specific listings. Laboratories and Teams Forooghi leads computational research within Aarhus University's heat and fluid flow group, utilizing DNS/LES techniques and data-driven frameworks. His work integrates high-fidelity simulations with engineering applications, particularly in energy conversion systems requiring advanced roughness modeling.

Yurij HolovatchView profile

Professor

• Statistical Physics
• Phase Transitions and Critical Phenomena
• Complex Systems
• +13 more

Yurij Holovatch is a Professor and Chief Researcher at the Institute for Condensed Matter Physics (ICMP) of the National Academy of Sciences of Ukraine in Lviv, where he founded the Laboratory for Statistical Physics of Complex Systems. He is a co-founder and co-director of the L4 collaboration and the International Doctoral College in Statistical Physics of Complex Systems, linking ICMP with the Universities of Leipzig (Germany), Coventry (UK), and Lorraine (France). He is also a full member of the National Academy of Sciences of Ukraine. Research Interests: His work focuses on phase transitions and critical phenomena in structurally disordered magnets, scaling of macromolecules and conformational properties of complex polymers, complex networks (ordering, stability, spreading), and increasingly extends into digital humanities and human migration . His research bridges theoretical physics with data-driven modeling of social and urban systems. Publication Trends: His recent publications (2023–2024) reveal a sustained focus on critical behavior in disordered systems using Monte Carlo simulations, exact and asymptotic analysis of models like the Potts and Blume-Capel models, and innovative applications of statistical physics to collective decision-making, transportation networks, and migration. These works reflect a strong interdisciplinary trend, integrating physics-based modeling with data analytics and social science questions. Davydov Prize for studies in theoretical and biological physics (2020) Honorary Ambassador of Lviv (2020) Visiting Professor (Honorary), Coventry University (since 2018) Advising and Grants: As co-director of the International Doctoral College, he plays a central role in training PhD students in statistical physics across Ukraine, Germany, UK, and France. He organizes the annual Ising Lectures workshop in Lviv since 1997, fostering international collaboration. He serves as editor of the book series Order, Disorder and Criticality (World Scientific), with Volume 7 published in 2023. Labs and Teams: He founded and leads the Laboratory for Statistical Physics of Complex Systems at ICMP Lviv. He co-directs the L4 collaboration and the International Doctoral College, which function as cross-institutional research and educational networks integrating theoretical and computational physics across Europe.

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +6 more

Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.

Yan ZhaoView profile

Associate Professor

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +5 more

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.