جست‌وجوی استاد و پژوهشگر

Andrea Burattinمشاهده پروفایل

دانشیار

• Process Mining
• Business Process Management
• Petri Nets
• +۶ مورد دیگر

Andrea Burattin is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His work bridges formal methods and practical process analysis, focusing on process mining, business process management, and hybrid modeling techniques. He actively contributes to research in healthcare process optimization, streaming data analysis, and system verification through Petri nets and CCS transformations. UN Sustainable Development Goals: Poverty eradication, environmental protection, and prosperity for all (via process optimization) Active projects: Immersive Process Mining (2024-2027), Usability and Understandability of Hybrid Process Models (2018-2021) His research explores large language model integration with process mining, proposing frameworks like Tiramisù for multi-faceted process visualization and PN2CCS for formal model translation. Recent work emphasizes real-time monitoring, conformance checking, and IoT-driven process analytics. Key trends in his publications include: 1) Streaming process mining pipelines (2022-2025); 2) LLM-plan generation frameworks (2024); 3) Formal verification techniques (Petri nets, CCS); 4) Healthcare process modeling (2019-2023); 5) Behavioral pattern analysis in process compliance. Scientific Awards Best Demo Award (2022, 2016) Best Process Mining Dissertation Award (2014) Best Workshop Paper (EDBA and PODS4H, 2023) As advisor, he supervises PhD projects on process mining and hybrid modeling. His editorial roles include Information Systems reviewer (2024-2025) and past editor for Engineering Applications of AI (2022-2023). Collaborations span Denmark, Italy, and the Netherlands.

Philip Billeمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• String Processing
• +۹ مورد دیگر

Philip Bille is a Professor and Head of the Algorithms, Logic and Graphs section at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), College of Engineering. His research centers on the design and analysis of efficient algorithms, particularly for string processing, compressed data, and data structures. His research interests lie at the intersection of theoretical computer science and practical applications. He focuses on algorithms , data structures , string indexing , pattern matching , and compressed computation . His work enables efficient querying and processing of large-scale, repetitive data, with applications in bioinformatics, intrusion detection, and green computing. The recent publications reflect a strong trend in developing space-efficient and fast algorithms for modern computational challenges. Key themes include compressed data structures , sliding window indexing , finite automata compression , and energy-aware matrix operations . These works demonstrate expertise in balancing theoretical rigor with practical performance. Philip Bille actively supervises multiple PhD students and leads several research projects. He contributes to advancing sustainable computing aligned with UN SDGs. His work integrates algorithmic theory with real-world efficiency. Supervises PhD projects on hierarchical compression, adaptive computation, and vector processor algorithms. Involved in research on green computing, compressed formats, and efficient data models. He is affiliated with the Algorithms, Logic and Graphs group at DTU, a hub for theoretical and applied algorithmic research. The team explores fundamental problems in data representation and processing, pushing the boundaries of what is computationally feasible in terms of time and space.

• Algorithms
• Algorithmics
• Big Data
• +۷ مورد دیگر

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

Rasmus Paghمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Big Data
• +۷ مورد دیگر

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

Knud Villy Christensenمشاهده پروفایل

دانشیار

• Chemical Engineering
• Membrane Technology
• Biofuels
• +۳ مورد دیگر

Knud Villy Christensen is an Associate Professor at the Department of Green Technology (IGT), University of Southern Denmark (SDU), and serves as Head of Section for SDU-Chemical Engineering. His research focuses on Chemical Engineering with emphasis on Membrane Technology , Biofuels , and Biowaste Valorization . He is actively involved in projects targeting sustainable energy and resource recovery from agricultural and industrial waste streams. His work spans Membrane Separation Technology (e.g., membrane distillation for food processing), Chemical Reactor Engineering , and Mathematical Modeling of unit operations. Recent publications highlight his expertise in green extraction of natural compounds biocascade approaches for biomass valorization biofuel production optimization Christensen has supervised numerous internships and projects at institutions like Fjernvarme Fyn, Carlsberg A/S, and Biogas Clean. His current projects include SUSTEPS (sustainable biofuel scaling) and ReCaP (phosphorus recycling). His research aligns with environmental sustainability and industrial process intensification.

• Astrophysics
• High-Energy Astrophysics
• Transient Astronomy
• +۲ مورد دیگر

Aprajita Hajela is a Postdoctoral Researcher at the Niels Bohr Institute, University of Copenhagen, working within the DARK Cosmology Centre. Her research focuses on high-energy astrophysical transients, particularly tidal disruption events and supernovae, using multi-wavelength observations spanning X-ray, optical, and radio regimes. Her research portfolio centers on time-domain astrophysics, with primary emphasis on tidal disruption events where stars are destroyed by supermassive black holes, relativistic jet formation, and gravitational wave counterparts. She investigates phenomena such as quasi-periodic X-ray eruptions in TDEs, late-time evolution of cosmic transients, and Hubble constant measurements through standard sirens. Her work integrates data from major observatories to unravel black hole physics and explosive stellar phenomena. Dr. Hajela is embedded within the DARK Cosmology Centre, a premier research environment at the Niels Bohr Institute dedicated to observational and theoretical studies of dark matter, dark energy, and cosmic evolution. This affiliation provides critical infrastructure for her investigations into transient cosmic events and their cosmological implications.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

• Graph Theory
• Data Mining
• Network Analysis
• +۲ مورد دیگر

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

Leon Derczynskiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Misinformation Detection
• +۳ مورد دیگر

Leon Derczynski is an Associate Professor of Computer Science at the IT University of Copenhagen , with a dual role as Principal Research Scientist/LLMSEC at NVIDIA . He leads the Strømberg NLP research group and coordinates NLP South at ITU, while also being affiliated with the Machine Learning group. Specializes in Natural Language Processing , Machine Learning , and LLM security Focus on Misinformation detection , Clinical text mining , and Danish language technology Research grants include Verif-AI (2.9M DKK), ClinRead (544K DKK), and LITHME (EU COST action, €11K). He has coordinated major projects like COMRADES and PHEME , and contributed to uComp and TrendMiner . Scientific recognition includes the University of Sheffield Exceptional Contribution Award (twice), WEBIST Best Student Paper award , and FP7 funding . His technical work includes the garak.ai LLM vulnerability scanner and generalised-brown clustering library. Actively supervises students and maintains numerous GitHub repositories (86 public projects) related to NLP, machine learning, and computational linguistics. He has delivered keynotes and guest lectures , including at Innopolis University (Russia) and PET (Danish Security and Intelligence Service).

Yan Zhaoمشاهده پروفایل

دانشیار

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +۵ مورد دیگر

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.

• Greenland
• Climate
• Climate change
• +۵ مورد دیگر

Sune Olander Rasmussen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Ice, Climate and Geophysics . With a background in geophysics, mathematics, and complex systems dynamics, his work focuses on integrating large ice core datasets and developing tools for climate data analysis. Research Interests : Greenland ice core studies, climate change, Dansgaard-Oeschger events, volcanic eruption impacts on climate, firn seismic properties, and probabilistic age modeling. Scientific Contributions : Key developer of the GICC05 time scale, with significant publications in Climate of the Past , Nature Communications , and PNAS . Awards : Carlsberg Foundation Distinguished Associate Professor. Leadership Roles : Chair of the NEEM dating consortium (since 2008) and the INTIMATE research network (since 2011).

Thor Grünbaumمشاهده پروفایل

استاد

• Philosophy of Action
• Philosophy of Mind
• Philosophy of Science
• +۵ مورد دیگر

Thor Grünbaum is a Professor at the Department of Communication and an Associate Professor at the Department of Psychology, both within the University of Copenhagen. He is Head of the Cognition, Intention, and Action Group (CoInAct) and Head of Study for the BSc in Cognitive Data Science program. His work bridges theoretical philosophy and empirical cognitive science. Research Interests: Philosophy of action/mind, philosophy of psychological science, and cognitive science. Specific areas include intention selection, agency, prospective memory, movement consciousness, and the methodology of well-being research. His recent publications focus on intention selection mechanisms, memory systems, and the philosophical implications of cognitive neuroscience. He has secured major grants from the Carlsberg Foundation and Danish Research Council. Teaching: Supervises in theoretical philosophy (philosophy of mind, epistemology, metaphysics) and cognitive science. Service: Executive secretary of the European Society for Philosophy and Psychology and associate editor for multiple journals.

Joel Daniel Anderssonمشاهده پروفایل

پژوهشگر ارشد

• Differential Privacy
• Continual Observation
• Streaming Algorithms
• +۳ مورد دیگر

Joel Daniel Andersson is a Research Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Faculty of Science . He is a member of the Providentia group under the supervision of Rasmus Pagh , focusing on differentially private algorithms in the continual observation setting. His work bridges theoretical challenges in privacy-preserving data analysis with practical applications in machine learning and statistics. PhD in progress (third-year) at the University of Copenhagen Former Master's student in Engineering Physics (Lund University, 2020) Previous industry experience: Software Engineer at Ericsson (5G protocols) Research Interests Joel's research explores differential privacy in streaming algorithms , particularly for continual observation —a framework where private statistics must be released incrementally over time. He investigates optimal solutions for problems like binary continual counting, comparing noise mechanisms (Laplace vs. Gaussian), and designing efficient algorithms for dynamic data streams. Earlier work includes computational modeling of beam dynamics at CERN for high-energy physics applications. Recent Publications His publications span theoretical computer science and computational physics , appearing in venues like NeurIPS , ICML , FORC , and physics journals. Key themes include: Differential privacy in streaming and machine learning Algorithmic design for continual data release Computational modeling for particle accelerators Privacy expiration and noise mechanism trade-offs Affiliations Joel collaborates with: BARC (Basic Algorithms Research Copenhagen) group Providentia group Adam Smith's lab at Boston University (visiting researcher, Jan–Apr 2025)

Jesper Hemdrup Kristensenمشاهده پروفایل

مدرس پاره‌وقت

• Circular Economy
• Supply Chain Management
• Industry 4.0
• +۵ مورد دیگر

Jesper Hemdrup Kristensen is a Part-Time Lecturer in the Department of Materials and Production at Aalborg University, Denmark, within the Faculty of Engineering and Science. His research focuses on circular economy, supply chain management, Industry 4.0, and sustainable manufacturing systems. He has contributed to projects like TEFFIC (2018–2021), which aimed to enhance educational programs for Industry 4.0 capabilities, emphasizing collaboration between academia and industry. His primary research interests include digital product passports for circularity, IoT integration in manufacturing, closed-loop systems, and educational frameworks for Industry 4.0. He has published extensively on topics such as product take-back challenges, reconfigurable manufacturing, and data-driven decision-making in circular economies. Notable works include developing an ecosystem orchestration framework for digital product passports (2024) and analyzing synergies between IoT and circular economy practices (2023). His articles highlight trends in leveraging technology (e.g., IoT, digital twins) to advance sustainability, optimize supply chains, and bridge educational gaps for industry readiness. While no formal awards are listed, his work has been widely cited, particularly in areas like digital product passports and Industry 4.0 education. He has supervised one PhD student, though specific details are not provided. His involvement in the TEFFIC project underscores his commitment to aligning academic curricula with industrial 4.0 demands. Key affiliations include the Department of Materials and Production, where he contributes to both research and educational initiatives. His work often intersects with Danish manufacturers, emphasizing practical applications of circular economy principles and smart manufacturing technologies.

Benyamin Khoshnevisanمشاهده پروفایل

دانشیار

• Sustainability Assessment
• Biofuel Production
• Waste Management
• +۴ مورد دیگر

Benyamin Khoshnevisan is an Associate Professor in the Department of Green Technology (IGT) at the University of Southern Denmark. He specializes in sustainability assessment of renewable energy systems, biofuel production, waste management, and agricultural systems, applying interdisciplinary methods like life cycle assessment, data mining, and multi-criteria decision analysis. Education: PhD in Biosystem Engineering, University of Tehran Research Focus: His work addresses environmental sustainability through biorefinery platforms, microbial protein production, and circular economy strategies. Key projects include EU-funded initiatives on microalgae biorefineries, plant-based food fermentation, and agrochemical sustainability. Article Trends: Recent publications emphasize life cycle assessment of bioenergy systems, waste valorization technologies, and integration of biological-inorganic processes for environmental sustainability. Topics span from microbial protein upcycling to geopolitical impacts on circular economy. Scientific Awards: Recipient of the Moo-ving Toward Carbon Neutrality Prize (2025) Teaching & Supervision: Leads MSc courses in techno-economic assessment and supervises projects on sustainable aviation fuel, carbon capture, and microbial platforms. Collaborates with international teams on EU projects like AgriLoop and GreenImpro.