جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Machine Learning
• Computational Biology
• +۷ مورد دیگر

Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.

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.

Jalal Kazempourمشاهده پروفایل

استاد

• Energy Markets
• Power Systems
• Renewable Energy Integration
• +۱۳ مورد دیگر

Jalal Kazempour is a Full Professor at the Technical University of Denmark (DTU) in the Department of Wind and Energy Systems (DTU Wind), where he leads the Energy Markets and Analytics (EMA) section and serves as Head of Studies for the MSc program in Sustainable Energy Systems. He is an Associate Editor for Operations Research and a Senior Member of both IEEE and INFORMS, and he contributes to EU energy policy through ACER’s Expert Group on Flexibility Needs Assessment. His research lies at the intersection of optimization, game theory, control, and machine learning, focusing on data-driven approaches for modern power systems with high renewable penetration. He investigates market design, grid services, and coordination mechanisms for integrated energy systems involving electricity, hydrogen, natural gas, and district heating, aiming to improve system efficiency and decision-making. Recent publications highlight trends in privacy-preserving optimization, bidding strategies for wind and hydrogen, flexibility aggregation, and market clearing in coupled energy systems, reflecting a strong emphasis on stochastic and robust optimization, machine learning, and real-world applicability in energy markets. Scientific Awards: Best Paper Award of IEEE SmartGridComm 2023 Best Paper Award of IEEE Transactions on Power Systems (2019–2021) Best Teacher Award, DTU Electrical Engineering Department (2019) IEEE Senior Member (2018) INFORMS Senior Member (2025) Outstanding Editor, International Transactions on Electrical Energy Systems (2017) Advising and Grants: He supervises multiple PhD students across projects on power-to-X, virtual power plants, and AI for market design. He has secured major funding, including a 9.5 million DKK EUDP grant for privacy-preserving data sharing and an Industrial PhD project with Energinet funded by Innovation Fund Denmark. Labs and Teams: He founded and leads the Energy Markets and Analytics (EMA) section at DTU, formerly known as the Energy Analytics and Markets (ELMA) group, which hosts over 10 researchers and organizes the annual DTU PES Summer School.

Bernardo Machado Davidمشاهده پروفایل

دانشیار

• Cryptography
• Secure Multiparty Computation
• Blockchain Technology
• +۳ مورد دیگر

Bernardo Machado David is an Associate Professor at the IT University of Copenhagen (ITU) within the Theoretical Computer Science department and a member of the Center for Information Security and Trust. Previously, he was an Assistant Professor at Tokyo Institute of Technology (2017–2018) and completed his Ph.D. in Computer Science at Aarhus University (2013–2017) under Ivan Damgård and Jesper Buus Nielsen. His research focuses on cryptography and information theory, particularly secure multiparty computation (MPC), blockchain technologies, and privacy-preserving protocols. He has held visiting positions at NTT Secure Platform Laboratories and Bar Ilan University’s Cryptography Group. Educational Background: Ph.D. in Computer Science, Aarhus University (2017) Visiting Researcher at NTT Secure Platform Laboratories and Bar Ilan University Research Interests: Bernardo’s work bridges theoretical and practical aspects of cryptography, emphasizing MPC protocols, blockchain consensus mechanisms, and privacy-enhancing technologies. Key areas include constructing efficient cryptographic primitives with provable security guarantees, exploring asymptotic limits of protocols, and designing scalable solutions for privacy in decentralized systems. His projects are funded by institutions like the Danish Independent Research Fund (DFF), Concordium Foundation, and Protocol Labs. Recent Research Trends: His publications (2023) highlight advancements in privacy-preserving smart contracts, differential privacy in financial systems, and composable MPC frameworks. He frequently collaborates on blockchain security, time-based cryptographic primitives, and anonymous credential systems. Awards and Grants: Best Paper Awards at SBSEG 2012 and ICoFCS 2011 Principal Investigator for projects like PUMA (Publicly Verifiable MPC), TrA²C (Transaction Anonymity in Cryptocurrencies), and Scalable Proof-of-Stake Blockchain Protocols Advising & Teams: Supervises PhD students (e.g., James Hsin-yu Chiang, Anders Konring) and collaborates with postdocs (e.g., Esra Yeniaras). Leads research groups focused on MPC, blockchain, and privacy-preserving applications. Active in organizing academic events like the Summer School on Privacy-Preserving Machine Learning and workshops at CRYPTO and Eurocrypt. Labs & Collaborations: Engages with industry via roles like Scientific Advisor at Concordium. His work integrates academic rigor with real-world applications, such as privacy-preserving decentralized exchanges and audit-compliant financial systems.

Niels van Berkelمشاهده پروفایل

استاد

• Artificial Intelligence
• Human-Computer Interaction
• Large Language Models
• +۴ مورد دیگر

Niels van Berkel is a Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on Human-Centered Computing and AI for societal benefit, with key projects including the HERD initiative on human-robot collaboration and initiatives to enhance mental health through digital tools. He leads multiple research projects involving AI ethics, healthcare applications, and swarm robotics. His educational background includes a Ph.D. in Computer Science. Research interests span AI explainability, human-AI interaction design, and the ethical implications of AI systems in healthcare and daily life. He has supervised 1 doctoral student and contributed to over 169 research outputs across journals, conferences, and datasets. Notable contributions include work on chatbot-driven data collection, coordination in AI development teams, and swarm robotics for search & rescue. His project 'AI for the People' emphasizes technology's role in improving societal wellbeing, while collaborations with medical professionals address chronic pain management and telehealth innovations. He received the DIS 2024 Honourable Mention and has actively engaged with media to communicate research impacts, including discussions on AI ethics and robot swarms. His work bridges technical innovation with human-centric design principles, addressing challenges in explainable AI, moral agency perceptions, and sustainable urban mobility solutions.

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.

Mohammad Naser Sabet Jahromiمشاهده پروفایل

استادیار

• Explainable Artificial Intelligence
• Biometrics
• Machine Learning
• +۴ مورد دیگر

Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.

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)

• Algorithms
• Computational Complexity
• Differential Privacy
• +۳ مورد دیگر

Lukas Retschmeier is a Research Fellow at the Department of Computer Science , University of Copenhagen , affiliated with the Algorithms and Complexity section under the Faculty of Science . His research focuses on: Algorithms Computational Complexity Differential Privacy The trends in his recent publications highlight expertise in privacy-preserving graph algorithms, computational complexity in graph theory, and optimization under privacy constraints.

Lukas Esterleمشاهده پروفایل

دانشیار

• Autonomous Systems
• Multi-Agent Systems
• Computational Self-awareness
• +۳ مورد دیگر

Lukas Esterle is Associate Professor at Aarhus University's Department of Electrical and Computer Engineering. His research focuses on enabling collaboration among autonomous systems through computational self-awareness, collective learning, and autonomous decision-making. His work draws inspiration from natural systems, psychology, and anthropology to develop resilient distributed systems. Key research areas include: autonomous multi-agent coordination, digital twin architectures, computational trust models, and distributed learning frameworks for robotic systems. His investigations target applications in smart cities, industrial automation, and edge computing environments. Professor Esterle teaches courses in Software Engineering, Computer Games Technologies, and Autonomous Agents & Multi-Agent Systems. He coordinates multiple European research projects including DIAMOND (Developing Personalized Capabilities), FLOCKD (Federated Learning for Collaborative Knowledge), and MARVEL (Multimodal Data Analytics for Smart Cities).

Michele Albanoمشاهده پروفایل

دانشیار

• Internet of Things (IoT)
• Energy Engineering
• Quality of Service (QoS)
• +۵ مورد دیگر

Affiliations & Roles Michele Albano is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design, focusing on research in IoT, Cyber-Physical Systems, and Edge Computing. He leads the Productive4.0 project (2017–2020), funded by Horizon Europe, addressing Industry 4.0 challenges in product lifecycle management. His work integrates formal verification tools like Uppaal with real-world applications in robotics, energy systems, and blockchain-based platforms. Research Interests Albano's research spans IoT architecture optimization , energy-efficient systems , and model-driven engineering . He develops tools for autonomous exploration algorithms (MAES), edge-cloud resource orchestration, and fault-tolerant computation offloading. His work bridges theoretical models (e.g., Uppaal SMC) with practical implementations in smart grids and robotic systems. Recent projects include blockchain-based crowdsourcing for machine learning and energy-aware thermal dynamics estimation in buildings. Collaborations & Impact He collaborates with the European Industry 6.0 community, contributing to the Arrowhead Framework for interoperable IoT systems. His research outputs include 112 publications, with 2025 highlights in human-inspired robotics and cognitive cloud frameworks. Media coverage in 2024–2025 highlights his work on green IT and secure API generation. Albano advises students on system modeling (e.g., ACSmt plugin development) and edge computing optimization. Labs & Teams His research group focuses on Cyber-Physical Systems and Smart Grids , with contributions to tools like RoutesMobilityModel and FlexHousing. He actively participates in workshops on New Trends in Software Architecture (SATrends '24) and IEEE conferences on Industrial Informatics.

Edlira Dushkuمشاهده پروفایل

استادیار

• Cybersecurity
• Internet of Things (IoT) Security
• Remote Attestation
• +۵ مورد دیگر

Edlira Dushku is a Tenure Track Assistant Professor at the Department of Electronic Systems, Aalborg University (Denmark), affiliated with the Cyber Security Group. She holds a PhD in Computer Science from Sapienza University of Rome and has held postdoctoral positions at DTU Compute, Denmark. Her research focuses on IoT Security, Cybersecurity, Blockchain, and Fog Computing, particularly in remote attestation and hardware security. Education: 2023: University Pedagogical Programme, Aalborg University 2016–2020: PhD in Computer Science, Sapienza University of Rome 2013: MSc in Computer Science, University of Tirana 2010: BSc in Computer Science, University of Tirana Research Interests: Dr. Dushku specializes in securing IoT devices and distributed systems through remote attestation, blockchain applications, and machine learning-based security. She explores challenges in fog computing, PUF protection, and adversarial defense mechanisms. Her work emphasizes trust management and public verifiability in IoT ecosystems. Teaching: She teaches courses like Security in IoT and Cloud Architectures (MSc) and Computer System Security (BSc) at Aalborg University. Previously, she was an academic supervisor for students with special needs at DTU. Grants & Projects: Coordinator of the EUopStart proposal for MSCA (2024) Guest editor for MDPI Sensors Special Issue on IoT Security Program chairs for multiple conferences (e.g., FMEC 2021) Lab & Collaborations: Collaborates with industry partners like Terma and Develco Products, focusing on applied IoT security solutions. Her work bridges academia and industry through co-supervised theses and real-world system implementations.

Frauke Kreuterمشاهده پروفایل

استاد

• Statistics
• Data Science
• Survey Methodology
• +۶ مورد دیگر

Professor Frauke Kreuter holds the Chair of Statistics and Data Science at Ludwig-Maximilians-Universität (LMU) Munich, Germany, and is a faculty member at the Joint Program in Survey Methodology (JPSM) at the University of Maryland, USA. She is Co-Director of the Social Data Science Center (SoDa), and plays a leading role in advancing data science for social research. Her research focuses on the intersection of statistics, survey methodology, and data science. Key areas include: Development and application of modern survey methods Integration of big data and traditional survey data Machine learning in social science research Secure and ethical access to sensitive data Training and capacity building in data science for researchers and policymakers Professor Kreuter has been recognized with several honors, including: Elected Fellow of the American Statistical Association Warren Mitofsky Innovators Award (2020) from the American Association for Public Opinion Research She is the Founder of the International Program for Survey and Data Science (IPSDS), Co-Founder of the Coleridge Initiative—which enables secure access to government data for research—and Co-Founder of the German-language podcast Dig Deep , promoting public understanding of data science. Her work involves extensive collaboration across academia, government, and policy sectors, and she leads initiatives that bridge methodological innovation with real-world impact.

• Cybersecurity
• Digital Watermarking
• Industrial Internet of Things
• +۵ مورد دیگر

Sani Abdullahi is an Assistant Professor at the Maersk Mc-Kinney Moller Institute within the University of Southern Denmark. He specializes in cybersecurity, digital watermarking, and privacy-preserving technologies, with a focus on Industrial Internet of Things (IIoT), audio signal processing, and biometric security. Research Interests : Cybersecurity, digital watermarking, IIoT, biometric security, privacy-preserving technologies, computer vision, and audio signal processing. Teaching : Courses include Advanced Cybersecurity and Biometric Security and CyberSafe: Mastering the Art of Cybersecurity (Summer School) . His recent work explores audio data authentication, recaptured image detection, and secure IIoT frameworks. Notably, he received the Best Paper Runner-up Award at the International Workshop on Distributed Digital Twins for his contributions to cybersecurity.

Ashutosh Dhar Dwivediمشاهده پروفایل

استادیار

• Cybersecurity
• Post-Quantum Cryptography
• Blockchain Security
• +۴ مورد دیگر

Ashutosh Dhar Dwivedi is an Assistant Professor in the Cybersecurity Group at Aalborg University, Copenhagen, Denmark. He specializes in blockchain security, applied cryptography, post-quantum cryptography, and advanced cybersecurity. His interdisciplinary research spans cryptography, IoT security, and AI-driven security analytics. Education: PhD in Cryptography, with postdoctoral research at institutions including the University of Waterloo, Technical University of Denmark, and the Polish Academy of Sciences. His pedagogical focus includes professional upskilling in cyber defense and post-quantum resilience. Research interests include post-quantum cryptographic protocols, privacy-preserving blockchain systems, and machine learning for security. His work has yielded over 50 peer-reviewed papers, including contributions to high-impact journals and conferences. Notable achievements: 2023 and 2024 Stanford University Top 2% Scientist ranking. Contributions: Editorial roles in international journals, program committees for premier conferences, and leadership in academic-industry collaborations like the Quantum Communication Infrastructure (QCI) consortium. Active in developing quantum-secure systems for national and industrial infrastructure.