Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.
Andreas Asp Bock is a postdoctoral researcher at the Department of Applied Mathematics and Computer Science within the Technical University of Denmark . His work focuses on scientific computing and numerical linear algebra, with particular emphasis on matrix approximation techniques, preconditioning strategies, and optimization methods. Current affiliation: Technical University of Denmark (College of Engineering) Academic role: Researcher (postdoctoral level) Research interests include: Matrix factorization and truncation Bregman divergence applications Baysian inversion frameworks Curve registration algorithms High-dimensional data analysis Preconditioning for iterative solvers Recent publications demonstrate expertise in improving approximate factorization preconditioners, geometric curve registration, and divergence-based preconditioner design. Collaborations with Martin S. Andersen and others indicate ongoing contributions to sparse linear algebra and computational statistics. Supervision : Currently mentoring J. V. Galvão da Mata in a PhD project focused on Optimization methods for data-sparse models , active from 2022-2025.
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.
Kristoffer Arnsfelt Hansen is an Associate Professor at the Department of Computer Science, Aarhus University. His research focuses on algorithmic game theory, computational complexity, and equilibrium computation in multi-player games. He explores topics such as stochastic games, Nash equilibria, convex optimization, and fair division. His work bridges theoretical computer science with economic applications, particularly in mechanism design and market analysis. Selected publications include studies on PPAD-membership via convex optimization, complexity of Pareto-optimal lotteries, and the computational challenges in analyzing Nash equilibria. He has contributed to conference proceedings like WINE 2022 and SAGT 2021, showcasing his engagement with international research communities. Research interests emphasize theoretical foundations of game theory with practical implications for resource allocation, fair division, and multi-agent systems. His work often intersects with complexity theory, exploring boundaries of efficient computation in economic and strategic scenarios. No awards or grants are explicitly mentioned in the provided data. Advising activities and lab affiliations are not detailed here.
Kevin Schewior is a Researcher at the University of Southern Denmark (SDU), affiliated with the Faculty of Engineering and the Department of Mathematics and Computer Science. His work focuses on algorithms, operations research, and theoretical computer science, with particular emphasis on optimization, online algorithms, and stochastic processes. He has published extensively in top-tier venues, contributing to areas such as scheduling, mechanism design, and combinatorial optimization. His research addresses challenges in decision-making under uncertainty, resource allocation, and algorithmic game theory. Despite no explicitly listed awards, his prolific publication record underscores his scholarly contributions. He has no listed advisees or students in the provided texts.
Andrea Vandin serves as an Associate Professor in the Formal Methods for Safe and Secure Systems section at DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark. Her research integrates theoretical computer science with practical system analysis, focusing on formal verification methodologies for complex systems. Her primary research interests include: Formal methods for system verification Qualitative and quantitative modeling techniques Domain-specific language development Large-scale performance analysis Model reduction algorithms Chemical reaction network analysis Statistical model checking Recent publications demonstrate expertise in constrained lumping of chemical systems, network embedding through equitable partitions, and stochastic conformance checking. Her work bridges computer science, mathematics, and systems biology with applications in sustainable system design. She actively contributes to UN Sustainable Development Goals through formal verification approaches for safe and secure systems. Her research shows strong trends toward applying model reduction techniques to chemical reaction networks and agent-based systems, with increasing focus on statistical verification methods and probabilistic modeling. The integration of MultiVeStA with Mesa for Python agent-based models represents a significant contribution to verification capabilities in simulation environments.
Richard Martin Lusby is an Associate Professor in the Department of Technology, Management and Economics at DTU Management, Technical University of Denmark. His academic work focuses on Operations Research with specialization in mathematical optimization applied to transportation and energy systems. His research spans railway scheduling, container logistics, maritime transport, and energy storage optimization. Department: Department of Technology, Management and Economics Section: Section for Operations Research, Division for Management Science Location: Building 358, Room 145A, Academy Road, 2800 Kgs. Lyngby, Denmark Contact: rmlu@dtu.dk, Tel: 45253084 ORCID: 0000-0002-8652-6219 Professor Lusby's research interests center around mathematical optimization methods applied to complex transportation and energy systems. His work spans railway scheduling problems, container logistics optimization, maritime transport operations, and energy storage scheduling. He develops advanced algorithms including Lagrangian Relaxation, Benders decomposition, and stochastic programming approaches to solve challenging real-world problems in these domains. His research has practical applications in improving efficiency and sustainability in transportation networks and energy systems. His recent publications demonstrate a strong focus on optimization techniques for transportation systems, particularly in railway operations and container logistics, as well as energy storage scheduling. The research shows a consistent pattern of developing sophisticated mathematical methods to address complex operational challenges in transportation networks, with increasing attention to sustainability considerations in maritime shipping and energy systems. Professor Lusby has been involved in significant research projects including the SWITCH project (Sustainable Network Design - Electrifying Container Shipping), which received funding from Den Danske Maritime Fond. This project explores how battery-powered ships could replace fossil fuels in global shipping through mathematical modeling and AI-driven optimization. He also supervises PhD students, including Georgios Vassos who completed a thesis on "Procurement Policy Analysis in Intermodal Container Logistics". His work has practical implications for improving efficiency in transportation networks, reducing environmental impact in maritime shipping, and optimizing energy systems operations. Through his research, Professor Lusby contributes to both theoretical advances in optimization methods and their practical applications in critical infrastructure sectors.
David Pereñiguez is a Postdoctoral Fellow at the Niels Bohr Institute (NBI), affiliated with the Faculty of Science at the University of Copenhagen. He holds a PhD in Theoretical Physics from the Autonomous University of Madrid and joined NBI in October 2022. His research focuses on black hole physics and gravitational wave propagation , particularly how these waves encode information about black hole structure and new physics. Key themes include superradiant instability , ringdown signals , and electric-magnetic duality . Education PhD in Theoretical Physics, Autonomous University of Madrid Research Trends : His recent work explores gravitational wave dynamics in black hole spacetimes, nonlinear effects in ringdown, and constraints on massive gravity theories. Collaborations include researchers like Vitor Cardoso and Riccardo Brito. Contact : Email: david.pereniguez@nbi.ku.dk Office: Building C, Room Cc3a, Blegdamsvej 17, Copenhagen
Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.
Kim Knudsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His research focuses on inverse problems for partial differential equations, numerical computations, and tomography. He leads projects in hybrid inverse problems and acousto-electric tomography. PhD in Mathematics, Aalborg University (2002) Cand.Scient. in Mathematics and Computer Science, Aalborg University (1999) Postdoc at University of Washington, Seattle (2004-2005) His work spans mathematical modeling, uncertainty quantification, and imaging techniques like electrical impedance tomography. Recent projects address regularization methods, level-set algorithms, and hybrid data tomography. Kim Knudsen supervises PhD students including Asmund Rasmussen, Henrik Schlüter, and Astrid Kirkeby. Collaborations include Tarvainen, Garnier, and Adesokan. Publications highlight Bayesian approaches, spatial regularization, and tomography advancements.
Konstantin Pavlikov is an Associate Professor at the Department of Business & Management (DBM) under Strategic Organization Design (SOD) at the University of Southern Denmark. His research focuses on Operations Research, Integer Programming, and Stochastic Programming , with particular emphasis on vehicle routing optimization and network flow modeling. Education : PhD in Operations Research (University of Florida, 2014), MSc in Applied Mathematics (Moscow State University, 2007) His work spans combinatorial optimization and network interdiction problems , developing exact and approximate solution algorithms for complex logistics challenges. Recent publications analyze heterogeneous vehicle routing capacity inequalities and two-commodity flow formulations for routing problems. Scientific contributions have been recognized with the Best Reviewer Award (2019). He actively reviews for journals like Computational Management Science and supervises academic works through examination roles.
Lene Monrad Favrholdt is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), Faculty of Science. Her research is centered on algorithms, particularly online algorithms, graph algorithms, and combinatorial optimization. She is an active contributor to theoretical computer science with a focus on algorithmic performance under uncertainty and with predictions. Her research interests include: Online Algorithms and Competitive Analysis Algorithm Design with Predictions Graph Algorithms (e.g., edge coloring, minimum spanning trees) Combinatorial Optimization (e.g., bin packing, knapsack problems) Resource Allocation and Scheduling The recent trend in her publications shows a strong emphasis on integrating machine learning predictions into classical online algorithms to improve competitive ratios and practical performance. Her work spans theoretical analysis and experimental evaluation, appearing in top venues such as Algorithmica, ICML, and WADS. Her scientific achievements include receiving SDU's teaching award in 2022. She has served as an editor and peer reviewer for major algorithmic conferences, including the European Symposium on Algorithms (ESA) and Scandinavian Symposium on Algorithm Theory (SWAT). SDU's teaching award Lene Monrad Favrholdt has been involved in teaching courses such as Logic and Linear Algebra and has contributed to PhD supervision. She is actively engaged in research collaboration and academic service, including participation in workshops and conferences on new techniques in online algorithms. While specific grants are not mentioned, her extensive publication record and academic activities indicate sustained research funding and collaboration.
Ioana O. Bercea is a tenure-track Assistant Professor in the Computer Science Department at KTH Royal Institute of Technology, affiliated with the Division of Theoretical Computer Science. She holds a PhD in Computer Science from the University of Maryland (advised by Samir Khuller), a Master's from the same institution (advised by Aravind Srinivasan), and a BS in Mathematics and Computer Science from the University of Chicago. Her research focuses on Data Structures (dictionaries, Bloom filters), Randomized Algorithms (hashing, clustering), and Computational Geometry (Traveling Salesman Problem). Her recent publications emphasize scalable data structures, dynamic filters, and algorithmic efficiency, with applications in database systems and high-dimensional optimization. Awards & Honors: Best Artifact of ACM SIGMOD 2023 Future Digileader (Digital Futures, 2023) VR Starting Grant (2024) She advises PhD student Jonas Østergaard Klausen (University of Copenhagen) and collaborates with research groups including BARC (Copenhagen) and Digital Futures. Her VR Starting Grant funds the project "DataTech: Techniques for scalable data storage and analysis."
Nicki Skafte Detlefsen serves as an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing within the Cognitive Systems research section. His institutional affiliation includes the Richard Petersens Plads campus in Kgs. Lyngby, Denmark, with primary research activities centered on advanced machine learning methodologies. Dr. Detlefsen's research program focuses on deep learning architectures for representation learning, particularly emphasizing invariant representations through inductive bias incorporation. His work spans protein sequence analysis using generative models, temporal alignment networks for time-series data, and stochastic representation frameworks via Laplacian autoencoders. Key contributions include developing methods for predictive uncertainty estimation and addressing suboptimal performance in biological sequence modeling, with strong connections to computational biology and neural network theory. Analysis of his 2019-2025 publications reveals an evolving research trajectory from foundational representation learning techniques toward broader AI ecosystem development. Early work centered on protein sequence modeling and temporal alignment algorithms, culminating in high-impact publications like the Nature Communications paper on protein representations. His most recent 2025 publication demonstrates strategic expansion into European HPC/AI infrastructure development, indicating a shift toward large-scale collaborative AI initiatives while maintaining core expertise in representation learning. No formal scientific awards or fellowships are documented in the source material. Dr. Detlefsen completed his PhD at DTU in 2020 under Søren Hauberg's supervision through the "Deep Metric Learning" project (2017-2021), which received significant academic attention with 328 Orbit downloads and coverage by 6 news outlets. His current research appears institutionally funded through DTU's Cognitive Systems section, with emerging involvement in pan-European AI infrastructure projects as evidenced by the 2025 HPC/AI ecosystem publication. He operates within DTU's Cognitive Systems research environment, collaborating extensively with Søren Hauberg, Ole Winther, and international researchers. His work shows strong interdisciplinary connections between computer science, bioinformatics, and neural engineering, with recent expansion into AI policy and infrastructure development through European collaborative networks.
Martin Rainer Bladt is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen, focusing on Applied Probability and Insurance Mathematics. He works on statistical and stochastic modeling, particularly in Markov processes and risk theory. Research trends include Markov jump processes, phase-type distributions, survival analysis, and applications in insurance and financial risk modeling. His recent publications emphasize methodological developments in actuarial science, extreme value theory, and homogeneous approximation techniques for inhomogeneous processes.
