جست‌وجوی استاد و پژوهشگر

Mikkel Thorupمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Hash Functions
• +۳ مورد دیگر

Mikkel Thorup is a Professor at the Department of Computer Science, University of Copenhagen, and a VILLUM Investigator heading the Basic Algorithms Research Copenhagen (BARC) Center supported by a €5.3 million grant from the VILLUM Foundation. Born in 1965, he holds a D.Phil. from Oxford University (1993). Prior roles include positions at the University of Copenhagen (1993–1998) and AT&T Labs-Research (1998–2013). His research focuses on algorithms, data structures, and hashing, with a particular emphasis on unifying theoretical and practical aspects. Awards include the ACM Fellowship, Villum Kann Rasmussen Award, and Fulkerson Prize. Education: D.Phil. in Computer Science, Oxford University (1993). Earlier positions: University of Copenhagen (1993–1998), AT&T Labs-Research (1998–2013). Research interests encompass algorithm design, data structure optimization, and hashing techniques. His work bridges theory and practice, addressing problems like graph clustering, optimization, and efficient sampling. He is known for contributions to correlation clustering, hash function analysis, and dynamic graph algorithms. Awards include the 2011 MAA Robbins Award, 2015 Villum Kann Rasmussen Award (Denmark’s top research prize), and 2021 AMS-MOS Fulkerson Prize. His Erdős number is 2, and he boasts an H-index above 60 with over 200 publications in top venues like STOC, FOCS, and SODA. Advising and grants: Leads BARC since 2017, funded by VILLUM Foundation. His research has influenced both academia and industry, particularly in telecommunications and algorithmic efficiency. Collaborates internationally, with a focus on foundational algorithmic challenges. Labs/Teams: Founder and head of BARC, fostering research in algorithms and their applications. Engages in interdisciplinary work combining theory with practical implementations.

• Data systems
• Knowledge graphs
• Large-scale data analysis
• +۴ مورد دیگر

Zoi Kaoudi is an Associate Professor at the IT University of Copenhagen, affiliated with the Data, Systems, and Robotics school and the Data-intensive Systems and Applications department. Her research focuses on advancing data systems, knowledge graphs, and large-scale data analysis. She leads the Rank4QO project (2024–2027), funded by the Carlsberg Foundation, which explores query optimization using ranking algorithms. Her work emphasizes machine learning integration in data management systems, including frameworks like Apache Wayang, which unifies diverse data analytics platforms. Key collaborations include projects with Volkswagen Group and SAP, addressing dynamic graph processing and knowledge graph embeddings. Notable contributions include innovative approaches to parameter management (e.g., Good Intentions ), automated data science pipelines ( DORIAN ), and efficient graph processing algorithms. Her recent publications (2023–2025) highlight advancements in machine learning systems, distributed data processing, and adaptive optimization techniques. Current projects aim to bridge machine learning and data management through frameworks like Wayang and Dorian, with applications in air cargo revenue management and semantic web systems.

Mads Rosendahlمشاهده پروفایل

دانشیار

• Computer Science
• Hardware and Software Energy Efficiency
• Static Analysis
• +۵ مورد دیگر

Mads Rosendahl is an Associate Professor at Roskilde University's Department of People and Technology, specializing in Programming, Logic, and Intelligent Systems. He holds an MSc and PhD in Computer Science. His research focuses on energy-efficient software/hardware algorithms, static analysis for hardware design, and programming language approaches to concurrency and performance optimization. Key projects include 'Energy and Performance Trade-offs in Hardware and Software Implementations' (2022-2023) and 'ENTRA: Whole-systems energy transparency' (2016). He has collaborated on initiatives like ICT-Energy (2013-2016) and Situated Design Methods (2013-2014), emphasizing interdisciplinary research. His research interests span energy-efficient computing, static program analysis, algorithm implementation on low-level hardware, and compiler optimization. Notable contributions include work on probabilistic resource analysis and abstract interpretation techniques. Publications highlight advancements in static communication analysis for hardware design (2025), energy-efficient algorithms (2025), and language support for embedded systems (2023). He has also presented widely, including lectures on semantics-based cost analysis (2012) and participation in workshops like DanSAS and Reversible Computation events. Rosendahl contributes to academic service through peer reviews and conference activities, reflecting his commitment to advancing theoretical and applied computer science.

Paul Hünermundمشاهده پروفایل

استاد مدعو

• Causal Inference
• Econometrics
• Innovation Economics
• +۳ مورد دیگر

Paul Hünermund is a Tenure Track Adjunct Professor at the Department of Strategy and Innovation, Copenhagen Business School. His research focuses on causal inference, econometrics, and innovation economics, with notable contributions to strategic management methodologies and AI-driven innovation systems. Current research trends in his publications include: (1) causal inference techniques in management science; (2) impact of AI on knowledge diffusion and innovation ecosystems; (3) control variable selection in empirical analysis; and (4) post-conflict innovation dynamics. His recent work appears in journals like Econometrics Journal and Organizational Research Methods . He has contributed to debates on innovation policy, AI applications for SMEs, and research productivity challenges, with media appearances in Danish and international outlets. His work aligns with UN Sustainable Development Goals related to innovation and sustainable economic growth. Supervised work includes 6 projects, though specific advisees aren't named in the provided texts. The ORCID identifier 0000-0001-9163-038X is associated with his academic profile.

Mohsen Ghaffariمشاهده پروفایل

عضو هیئت علمی

• Reinforcement Learning
• Multi-agent Systems
• Game Theory
• +۴ مورد دیگر

Mohsen Ghaffari is a postdoctoral researcher in Computer Science at the IT University of Copenhagen, Denmark, affiliated with the Digital Research Center Denmark (DIREC). His research focuses on decision-making under uncertainty in multi-agent systems, leveraging software analysis to enhance the reliability and safety of reinforcement learning. He holds a PhD in Computer Science and is a supervisor at the Institute for Advanced Studies in Basic Sciences, Zanjan. Research Areas: Reinforcement Learning, Multi-Agent Systems, Game Theory, Smart Grids, Formal Methods External Roles: Teaching Assistant for Advanced Programming Projects: Co-Investigator on DIREC initiatives focusing on AI safety and autonomous systems. His work emphasizes theoretical foundations and practical applications of reinforcement learning, including formal specification frameworks and real-world implementations like epidemic modeling and smart grid optimization.

• Chemical Informatics
• Computational Chemistry
• Graph Theory
• +۳ مورد دیگر

Prof. Daniel Merkle is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on computational systems chemistry, algorithmic methods for chemical reaction networks, and graph-based approaches in computational biology. He is actively involved in interdisciplinary projects addressing microbiome-driven diseases and computational systems chemistry. Merkle has held leadership roles in EU-funded initiatives and international research networks, including the COST Actions on complex chemical systems and origins of life. He contributes to teaching committees and organizes international conferences like the Dagstuhl Seminar on Algorithmic Cheminformatics. His research interests span chemical graph rewriting, pathway realizability, reaction database curation, and algorithmic design for biochemical systems. Recent work includes advancements in synthetic chemistry frameworks (e.g., ChemReservoir), efficient extraction of reaction rules (SynTemp), and formal modeling of unbalanced chemical reactions using ITS Graphs. Merkle’s projects, such as MATOMIC and TACsy, address microbial community metabolism and train computational systems chemists. He has published over 110 research outputs and actively engages in peer review and editorial roles. Key collaborations include work with Flamm, Stadler, and international research teams on computational methods for chemical systems. His contributions to open-source tools and algorithmic frameworks have advanced both theoretical and applied aspects of systems chemistry.

Jørgen Bang-Jensenمشاهده پروفایل

استاد

• Algorithmics
• Graph Theory
• Discrete Mathematics
• +۳ مورد دیگر

Jørgen Bang-Jensen is a Professor at the Department of Mathematics and Computer Science VIP, University of Southern Denmark. His research focuses on algorithmics, graph theory, and discrete mathematics, with an emphasis on polynomial mathematics, directed graphs, and combinatorial optimization. He has led multiple research projects including AlgoDisc and initiatives in discrete mathematics and optimization methods. He has collaborated internationally, with activities including peer reviews, conference participations, and visiting research positions. Notable projects include work on graph decompositions, arc-disjoint paths, and network flow algorithms. His contributions span over 160 research outputs, primarily in journals like Journal of Graph Theory and Theoretical Computer Science . Bang-Jensen has supervised two major research projects and served as a censor in examinations related to advanced algorithms. His work bridges theoretical computer science and discrete mathematics, addressing open problems in graph theory and algorithm design.

Marco Chiarandiniمشاهده پروفایل

دانشیار

• Operations Research
• Optimization Algorithms
• Integer Programming
• +۱۰ مورد دیگر

Marco Chiarandini is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), with a secondary affiliation to the SDU Climate Cluster. His research focuses on Operations Research, Optimization Algorithms, and their applications in domains such as transportation, healthcare, and education. He has led or contributed to projects addressing dynamic traffic estimation, dementia care app design, and sustainable software development. Chiarandini has supervised students including P. Tampakis and C. McCarthy, and his work has been featured in interdisciplinary collaborations with institutions globally. His recent articles explore topics like goal-scoring prediction in football and urban traffic modeling, reflecting his expertise in data science and algorithmic design. His academic contributions span over 50 publications, emphasizing methodological advancements in optimization and heuristic algorithms. He actively participates in conferences and peer review, and has coordinated EU-funded projects like Data Science for University Management (2019–2023). Chiarandini’s teaching includes courses on linear programming and metaheuristics, integrating practical applications with theoretical foundations.

Peter Schneider-Kampمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.

Theis Erik Jendalمشاهده پروفایل

پژوهشگر

• Recommender Systems
• Knowledge Graphs
• Graph Neural Networks
• +۴ مورد دیگر

Theis Erik Jendal is a Researcher at the Department of Computer Science, Aalborg University, affiliated with the Technical Faculty of IT and Design. He specializes in Recommender Systems and Knowledge Graphs, focusing on areas like explainable AI, graph neural networks, and inductive recommendation architectures. He participates in the Poul Due Jensen Professorate in Big Data and Artificial Intelligence (2019–2025), addressing challenges in query processing, knowledge graphs, semantic web, and open data. Key research interests include hypergraph models for explainable recommendation, gated architectures in knowledge graphs, and addressing challenges in knowledge graph embeddings. His work emphasizes interpretability, similarity search, and practical use cases in AI systems. Contributions include 5 peer-reviewed publications since 2020, spanning conferences like ECIR and CIKM, and a dataset contribution to the Yelp Collaborative Knowledge Graph (Zenodo, 2023). His research bridges theory and application, particularly in improving recommendation systems through advanced graph-based methodologies.

Alexandros Gelastopoulosمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Modeling
• Probability Theory
• Stochastic Processes
• +۴ مورد دیگر

Alexandros Gelastopoulos is a Research Fellow at the Institute for Advanced Studies in Toulouse with a PhD in Mathematics from Boston University (2019). His research employs mathematical modeling to study biological and social systems, focusing on probability theory, stochastic processes, and reinforcement dynamics in social contexts like cumulative advantage and ranking-based behaviors. Primary research interests include analyzing how popularity rankings influence social systems, studying brain oscillations for memory functions, and exploring decision-making paradoxes. His work often bridges mathematical rigor with behavioral sciences to uncover mechanisms driving social inequalities and neural computations. His publication trends show strong emphasis on social dynamics (e.g., lock-in effects, reinforcement processes) and computational neuroscience (e.g., neural rhythms, memory substrates), using stochastic modeling and empirical validation across disciplines. European Commission's Seal of Excellence (2024) Collaborates extensively with international researchers on projects involving mathematical sociology and behavioral experiments. Teaches game theory and mathematical courses while developing expository materials on real analysis and information theory.

• Data Management
• Location-Based Services
• Trajectory Data
• +۷ مورد دیگر

Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.

Tiago de Paula Peixotoمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Statistical Physics
• +۱۳ مورد دیگر

Tiago de Paula Peixoto is a Professor of Complex Systems and Network Science at the Institute of Science and Technology Austria (IT:U), where he leads the Inverse Complexity Lab. He has previously held faculty positions at Central European University (2019–2024) and the University of Bath (2016–2019), and conducted postdoctoral research at the University of Bremen and Technical University of Darmstadt. He holds a PhD in Physics from the University of São Paulo (2008) and a habilitation in Theoretical Physics from the University of Bremen (2017). His research lies at the intersection of statistical physics, computational statistics, information theory, Bayesian inference, and machine learning , with a central focus on inverse problems in network science . His group develops principled mathematical and computational models to infer the local interaction rules of complex systems from observed macroscopic behavior. Key research themes include statistically sound pattern detection in networks, network reconstruction from indirect data, uncertainty quantification, generative modeling of modular hierarchies and latent spaces, and scalable inference algorithms. His recent publications (2020–2025) reflect a consistent focus on advancing the theoretical and algorithmic foundations of network inference. A major theme is the development of Bayesian and information-theoretic frameworks for robust network reconstruction, moving beyond simplistic heuristics like correlation thresholding. He has pioneered methods for posterior sampling to quantify uncertainty and for minimum description length to prevent overfitting. His work also addresses scalability, with algorithms achieving subquadratic time complexity. Applications span diverse domains, including social systems (migration flows), political networks, and biological systems. Erdős–Rényi Prize from the Network Science Society (2019) Alexander von Humboldt Foundation Fellowship (2008) Karate Club Club Prize (6th recipient) Peixoto advises a vibrant group of PhD students and postdoctoral researchers, including Thomas Robiglio, Sebastian Kusch, Martina Contisciani, and Bukyoung Jhun. His former students include Felipe Vaca, Lizhi Zhang, and Silvia Guerrini. He has not received any specific grant mentions in the text, but his group’s sustained activity suggests successful funding. His lab is strongly committed to open science, with most of their methods implemented in the widely used graph-tool library, which is extensively documented and freely available. The lab organizes events like the annual Inverse Complexity Retreat and participates in major conferences such as NetSci and STATPHYS. The group is actively recruiting new PhD candidates and postdocs, indicating ongoing expansion and research momentum.

Gabriel Renaudمشاهده پروفایل

دانشیار

• Population Genomics
• Ancient Environmental DNA
• Pangenome Graph
• +۳ مورد دیگر

Gabriel Renaud is an Associate Professor at the Department of Health Technology , Technical University of Denmark (DTU) , leading the Bioinformatics Modern and Ancient Genomes group within the DTU Microbes Initiative . His research focuses on computational genomics, ancient DNA analysis, and pangenome graph algorithms. Active projects: PhD supervision on algorithms for pangenomic graphs (2024-2027), genomes and genes (2022-2025) Research Interests include: Population Genomics of Postglacial Eurasia Pangenome Graph Applications in Taxonomic Identification Next-Generation Sequencing Data Processing Ancient Environmental DNA Analysis Phylogenetics and Evolutionary Genetics Publications (2023-2024) highlight work on: High-resolution taxonomic identification using mitochondrial pangenome graphs Population turnovers in Neolithic Denmark Ancient Clostridium DNA and tetanus toxin evolution Benchmarking NGS tools for ancient DNA Supervision: Current main supervisor for four PhD students in bioinformatics and genomics.

Søren Deboisمشاهده پروفایل

دانشیار

• Digitalization
• Process Modelling
• IT Security
• +۳ مورد دیگر

Søren Debois is an Associate Professor at the IT University of Copenhagen , specializing in the intersection of process technologies and IT security . His research focuses on technical solutions for trust between parties, including applications of blockchain to process management and declarative process models for legal-compliant municipal systems. He is a principal author on the DCR process modelling notation and architect of the DCR Workbench , contributing to the commercial dcrgraphs.net engine. He leads a work package in the Innovation Fund Denmark -funded EcoKnow project, aiming to align municipal case-management with legal compliance. A frequent expert in Danish media on IT security, he teaches Distributed Systems , Security I , and Security II at ITU. His accolades include the 2017 ITU Excellence in Teaching Award and a BPM '18 Best Paper Honorable Mention .