Dr. Daniel Malz is an Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research focuses on quantum many-body systems, quantum optics, and quantum computing, with affiliations to research groups QA, QMATH, and QfL. His work bridges theoretical physics and mathematical modeling, addressing topics like superradiance, entanglement dynamics, and quantum state preparation. Key research interests include quantum information theory, non-Markovian dynamics, and the development of efficient quantum simulation techniques. His recent publications explore advanced topics such as photonic cluster states, tensor network simulations, and cross-platform quantum network verification. Much of his work addresses foundational questions in quantum mechanics while maintaining practical relevance for quantum technologies. His contributions span both theoretical derivations and numerical methods, with a focus on bridging classical and quantum many-body dynamics.
Kristian Tangsgaard Hvelplund is an Associate Professor with the Department of English, Germanic and Romance Studies at the University of Copenhagen's Faculty of Humanities. His academic work centers on translation studies, with a particular emphasis on cognitive processes in translation and dubbing translation. Translation cognition and automaticity Eye-tracking and keystroke logging methodologies Media translation and dubbing Translation process modeling Hvelplund's research combines theoretical and applied approaches, utilizing experimental methods to analyze translation quality and cognitive resource allocation. Recent publications explore emotional dimensions in translation, post-editing workflows, and methodological innovations in cognitive translation studies. His work demonstrates strong interdisciplinary connections across Translation Studies , Cognitive Science , Applied Linguistics , and Human-Computer Interaction . Methodologically, he focuses on Eye-tracking , Process modeling , and Quantitative translation analysis . As a dedicated educator, Hvelplund supervises BA, MA and PhD students in translation studies while maintaining active participation in international academic collaborations.
John Rasmussen is a Professor and Vice Head of the Department of Materials and Production at Aalborg University's Faculty of Engineering and Science. He founded the AnyBody Research Group and served as CEO of AnyBody Technology A/S. His research focuses on biomechanics, rehabilitation robotics, and musculoskeletal simulation, with applications in exoskeleton design and orthopedic interventions. Education: Ph.D. in Computer-Aided Design (Aalborg University, 1989) M.Sc. in Mechanical Engineering (Aalborg University, 1986) Research Interests: Biomechanics, CAD optimization, finite element analysis, and biomedical engineering. His work bridges computational modeling with clinical solutions for mobility disorders. Article Trends: Recent publications emphasize exoskeleton design, spinal biomechanics, and osteoarthritis interventions, utilizing experimental and simulation approaches across robotics and orthopedics. Awards: Wearable Robotics Innovation Prize (2018) Grand Challenge Competition Winner (2014) Steno Prisen (2012) Teacher of the Year (2011) Leadership: Manages the Center for Rehabilitation Robotics and oversees 37 research projects. Advises industry collaborations in assistive technology.
Troels Henriksen is an Assistant Professor on Tenure Track at the Department of Computer Science (DIKU) at the University of Copenhagen, where he is affiliated with the Programming Languages and Theory of Computation research section. His research focuses on programming languages, particularly functional array programming languages, compiler design, and parallel computing. He maintains an active research profile with numerous publications in top-tier programming language conferences. Dr. Henriksen's research interests center around programming language theory and implementation, with particular emphasis on functional array programming languages. His work bridges theoretical foundations with practical high-performance computing applications. His research spans type systems, compiler optimizations, parallelism, and memory management in the context of array programming languages, contributing to both academic knowledge and practical language implementations. His recent publications reveal a strong focus on array programming language design and implementation. There is a clear trend toward optimizing functional array languages for high-performance computing environments, with significant work on fusion optimizations, parallelism, and memory management. His research often intersects with practical applications in scientific computing and machine learning, particularly through work on automatic differentiation for array languages. Dr. Henriksen is actively involved in the programming languages research community, regularly publishing in prestigious venues such as the ACM SIGPLAN conferences. His collaborations span multiple institutions, indicating an active research network in the programming languages field.
Anders Nedergaard Jensen is an Associate Professor at the Department of Mathematics, Aarhus University, specializing in Computational Tropical Algebraic Geometry. His research bridges polynomial equations, polyhedral geometry, and algorithmic methods, with applications to celestial mechanics and combinatorial algebra. His work focuses on tropical geometry, polynomial system solving, and algorithmic approaches to algebraic structures. He has developed computational tools for mixed volume calculations, homotopy continuation, and tropical prevariety decomposition, aiming to advance Smale's 6th problem in celestial mechanics. Recent publications highlight trends in tropical methods for celestial mechanics, binomial ideal analysis, and parallel computing applications in algebraic geometry. His teaching includes bachelor courses in algebra and master-level topics in tropical geometry and systems of polynomial equations.
Yushuai Li is an Assistant Professor in the Department of Computer Science at Aalborg University. His research focuses on digital twin technologies, energy internet systems, distributed optimization, and cyber-physical security for energy networks. Institution: Aalborg University, Denmark Academic Rank: Assistant Professor Email: yushuaili@ieee.org, yusli@cs.aau.dk Research Interests: Li's work bridges artificial intelligence with energy systems, emphasizing: Digital Twin for Energy and Transportation Integration Reinforcement Learning in Power Trading Distributed Control for Microgrids Privacy-Preserving Energy Dispatch Autonomous Driving-Energy System Coupling Scientific Contributions: His recent publications address critical challenges in energy internet resilience, including: Distributed control under stealthy attacks Noise-resilient microgrid operations Multi-timescale optimization algorithms Event-triggered control strategies Secure peer-to-peer energy trading Honors & Awards: Recipient of multiple prestigious awards, including: Best Paper Awards (MPCE, ICCSIE, IEEE EI2) Excellent Young Expert Award (MPCE, 2023) National Natural Science Prizes (CAA 2022-2023) Highly Cited Papers (8 ESI Highly Cited, 2 ESI Hot Papers) H-index 24 with 2500+ Google Scholar Citations Academic Leadership: Serves as Associate Editor for four IEEE journals and chairs sessions at leading conferences like IEEE SmartGridComm, ISIE, and IEEE EI2. His 70+ publications span top venues including IEEE Transactions on Cybernetics, Smart Grid, and ACM SIGMOD.
Kurt Valentin Mikkelsen is a Professor in the Department of Chemistry at the University of Copenhagen with a Dr. Scient. from the same institution and a Ph.D. in Theoretical Chemistry from Aarhus University. His prolific career spans over 35 years since his first 1987 publication, yielding 325+ research outputs including 200+ reviewed articles, 3 books on molecular dynamics, and an H-index of 34 with 4100+ citations. Education Dr. Scient, University of Copenhagen Ph.D., Theoretical Chemistry, Aarhus University Professor Mikkelsen's research pioneers scientific computing methods for molecular dynamics, solvent effects, and chemical reactions across homogeneous/heterogeneous environments. Current work targets biophotonics (two-photon sensitizer design), photonics (optical component structure-property relationships), nanoscience (carbon nanotube optical properties), and advanced solvation models. His group develops computational frameworks for nanoparticle-organic molecule interactions with applications in atmospheric chemistry and solar energy storage. His 2025 publications reveal a dominant trend in quantum chemical methodologies, particularly cluster perturbation theory for excited states, alongside applications in molecular electronics (Coulomb blockade), spectroscopy (manganese chlorides, pyruvic acid), and renewable energy (azobenzene solar storage). This work bridges fundamental theory with nanotechnology and environmental science. Professor Mikkelsen leads an internationally collaborative research group at the University of Copenhagen, evidenced by extensive co-authorship networks across Denmark and global institutions, driving innovation in computational chemistry through high-impact publications and methodological advancements.
Davide Mottin is an Associate Professor at the Department of Computer Science, Aarhus University. His primary research focuses on graph theory, machine learning, and data mining, with significant contributions to knowledge graphs, algorithm design, and interdisciplinary applications in drug discovery and material science. He holds a leadership role in large international conferences such as CIKM 2024 as a Program Chair. His research explores scalable graph algorithms (e.g., subgraph matching, alignment), robust knowledge graph cleaning, and leveraging large language models for scientific tasks. Mottin has pioneered work on spectral methods for graph analysis (e.g., NetLSD, VERSE embeddings) and developed frameworks for interactive data exploration (e.g., X2Q, MetaExp systems). Key contributions include FUGAL for graph alignment and Ucode for community detection Active in reproducibility efforts, as seen in retraction notices and algorithmic redesigns Focus on practical applications in drug discovery via evolution-based models (EvolMPNN) He has authored over 60 peer-reviewed publications and holds grants supporting interdisciplinary research at the intersection of computer science and life sciences. Mottin is affiliated with the university's AI and data science initiatives, contributing to both theoretical advancements and real-world system implementations.
Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.
Anders Bonde is an Associate Professor at Aalborg University's Department of Communication and Psychology, within The Faculty of Social Sciences and Humanities. His research focuses on music analysis, sound branding, media psychology, and the interplay between music and audiovisual media. He employs both qualitative and quantitative methods, integrating diverse data into aesthetically informed frameworks. His work bridges musicology, media studies, semiotics, aesthetics, affective computing, and data visualization. Teaching responsibilities include courses in music theory, voice leading, sound branding, and epistemology across programs in Music, Communication, and Media Studies. He leads the Study Board for Art, Health, and Technology, overseeing programs in Music Therapy, Art and Technology, and Nordic master's programs in Visual Studies and Media Arts. Research projects include investigations into sound branding, audiovisual interaction effects, and multimodal meaning-making. Notable outputs include works on the aesthetics of ambiguity in 20th-century music, sensory atmospheres in retail contexts, and the effectiveness of non-musical sound branding. His work spans theoretical frameworks, empirical studies, and interdisciplinary collaborations. Recent articles explore interactive music visualization, sound branding conceptualizations, and the role of music in advertising. Media engagement includes commentary on sound branding strategies and its psychological impacts. Grants and collaborations reflect his commitment to bridging theoretical and applied research.
Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.
Pinar Tözün is an Associate Professor and Head of the Data, Systems, and Robotics department at the IT University of Copenhagen. She leads multiple research groups, including the Resource-Aware Data Systems and Data-intensive Systems and Applications. Her roles also include academic responsibility for DASYA and involvement in the Center for Climate IT. Her research focuses on resource-aware computing, machine learning systems, heterogeneous hardware optimization, and sustainable data management. Key areas include workload characterization, GPU utilization, and benchmarking frameworks for edge devices and cloud systems. Dr. Tözün has led significant projects such as MOTH (Machine Learning on Tiny Hardware), RAD+ (Resource-Aware Data Science), and DAPHNE (Integrated Data Analysis Pipelines), funded by institutions like the Novo Nordisk Foundation and the European Commission. She has published extensively in top venues, including Proc. ACM Manag. Data and Dagstuhl Reports, with a focus on efficient machine learning pipelines and hardware-aware systems. Her work has been highlighted in media discussions on sustainable hardware and software practices. Tözün also actively participates in academic governance, serving on hiring committees and shaping future faculty recruitment in data-intensive systems.
Professor Shaoping Bai serves in the Department of Materials and Production at Aalborg University's Faculty of Engineering and Science, where he leads research in rehabilitation robotics and mechanical systems. His work focuses on developing innovative exoskeleton technologies to assist individuals with disabilities in performing daily activities. His primary research domains include: Rehabilitation Robotics and Assistive Technologies Exoskeleton Design and Control Systems Parallel Manipulators and Robotic Mechanisms Biomechanics and Human-Machine Interaction Wearable Robotics for Medical Applications Recent publications demonstrate a strong emphasis on clinical validation of exoskeleton systems, with growing interdisciplinary collaboration between engineering, medical, and human factors disciplines. His work shows particular focus on upper-limb assistance systems and terrain-adaptive walking technologies. Notable recognitions include: Best paper award at IFToMM 2018 Wearable Robotics Association's 2018 Innovation Challenge Grand Prize Winner Professor Bai actively participates in multiple research initiatives including the Center for Rehabilitation Robotics and the Intelligent Hybrid Light Weight Tendon Based Exoskeleton project, securing substantial research funding for assistive technology development. His work frequently involves cross-disciplinary teams comprising engineers, medical professionals, and computer scientists. As a core member of Aalborg University's Center for Rehabilitation Robotics, he contributes to advancing technologies that improve quality of life for individuals with disabilities through sophisticated robotic solutions.
Baoze Wei is an Associate Professor at Aalborg University's Department of Electric Power Systems and Microgrids, part of the Faculty of Engineering and Science. His research focuses on advanced control strategies for power electronics, microgrid stability, and energy management systems. He leads and collaborates on projects such as the Digital Twin-based Reliability Framework for Aviation Systems and Holistic Optimization of Green Fuel-Powered Microgrids. Key contributions include work on distributed energy systems, fault-tolerant architectures, and predictive maintenance for power electronics. His research emphasizes practical applications in renewable integration, smart grids, and industrial electrification. Wei has supervised one PhD student, Q. He, and contributed to projects funded by entities like Horizon JU and Huawei. Notable collaborations include work on hybrid-electric aircraft systems (HECATE) and advanced control algorithms for distributed converters. His research spans technical areas such as voltage source inverters, uninterruptible power systems (UPS), and DC shipboard microgrids. His publications reflect expertise in model predictive control, energy trading strategies, and condition monitoring. Current research trends include data-driven lifetime prediction for power electronics components and optimization of multi-energy systems. He actively participates in international conferences, contributing to both theoretical advancements and real-world system implementations.
Anne Marie Svane is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, specializing in topological data analysis, stochastic geometry, and probability theory. Her research bridges theoretical mathematics with applied statistics, focusing on geometric functionals and computational methods. Education: PhD in Mathematics from Aarhus University. Her recent work explores cobordism obstructions, Gibbs processes, and applications in climate science and medical research. Key contributions include: Advancing kernel persistence methods for topological data analysis. Developing central limit theorems for spatial point processes. Modeling moisture dynamics in building physics with climate data. Investigating the interplay between digital algorithms and geometric structures. She collaborates on interdisciplinary projects like AI-Aalborg Intelligence and contributes to educational initiatives such as Girls' Day in Science.
