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Petar DurdevicView profile

Associate Professor

• Reinforcement Learning
• Deep Learning
• Wastewater Treatment
• +3 more

Petar Durdevic is an Associate Professor at Aalborg University's Faculty of Engineering and Science, within the Energy Section. His research focuses on Reinforcement Learning, Deep Learning, Robotics, and Environmental Engineering, particularly in wastewater treatment optimization and offshore energy systems. He leads projects like OPTIMIZE (Deep Reinforcement Learning for microbial fermentation) and AITEKS (AI-driven tech support). Durdevic collaborates with the Offshore Drones and Robotics and AI for the People teams, addressing challenges in energy technology and sustainable systems. His research interests include predictive control for wastewater treatment, autonomous robotics for pipeline inspection, and AI applications in environmental monitoring. Notable contributions include developing grey-box models for N2O emissions and amphibious quad-rotor systems for infrastructure inspection. Durdevic has supervised one PhD student, Lars D. Hansen, and secured grants from the Novo Nordisk Foundation and Innovation Fund Denmark. Key achievements include advancing fiber rope technology for wind turbines and publishing extensively on topics like deep reinforcement learning, synthetic data generation, and environmental control systems. His work bridges robotics, AI, and environmental science, with applications in offshore energy and sustainable wastewater management. PhD Supervision: Lars D. Hansen (Deep Learning Model Predictive Control for N2O Emission Reduction) Labs/Teams: Offshore Drones and Robotics, AI for the People, BLUE – Marine & Maritime Research Grants: OPTIMIZE (Novo Nordisk Foundation, 2024–2028), AITEKS (Innovation Fund Denmark, 2024–2025)

• Data Management
• Location-Based Services
• Trajectory Data
• +7 more

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.

Stephanie ForrestView profile

Professor

• Computational Immunology
• Computer Security
• Automated Software Repair
• +12 more

Stephanie Forrest is a Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where she also serves as Director of the Biodesign Center for Biocomputation, Security and Society. She is an External Faculty member of the Santa Fe Institute (SFI), where she previously held leadership roles including Co-Chair of the Science Board (2010–2013) and Interim Vice President for Academic Affairs (1999–2000). Prior to joining ASU, she was a Regents Distinguished Professor and Department Chair of Computer Science at the University of New Mexico (2006–2011). B.A., St. John's College M.S., Ph.D., University of Michigan, Computer Science Her research lies at the intersection of biology and computation, focusing on computational immunology, automated software repair, evolutionary computation, and modeling complex systems such as cancer and immune responses. She pioneers bio-inspired algorithms for cybersecurity and develops evolutionary techniques for software optimization and repair. Her work integrates principles from immunology, genetics, and complex adaptive systems to solve problems in computer science. The trends in her recent publications reveal a sustained focus on automated program repair using evolutionary computation, GPU code optimization, and modeling biological systems. Her work increasingly combines machine learning with mutation-based search, explores semantic representations in code repair, and applies bio-inspired models to real-world software and security challenges. ACM/AAAI Allen Newell Award (2011) Presidential Young Investigator Award (1991) Stanislaw Ulam Memorial Lectures, SFI (2013) IEEE Fellow UNM Annual Research Lecture (2012) IEEE S&P Test of Time Award (2020) ICSE Most Influential Paper Award (2019) ACM/SIGEVO Impact Award (2019) SEAMS Best Paper Award (2019) WEIS Best Paper Award (2015) Stephanie Forrest has advised numerous students and researchers, often in collaboration with W. Weimer, C. Le Goues, and M. Moses. Her research is supported by major funding agencies including the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory (AFRL), and the Santa Fe Institute. She has also contributed to public policy as a Jefferson Science Fellow at the U.S. Department of State (2013–2014), advising on cyber-policy. She currently chairs the Government Affairs Committee of the Computing Research Association (CRA). She leads the Biodesign Center for Biocomputation, Security and Society, which brings together interdisciplinary teams to study the co-evolution of technology and society. Her research group has developed tools for intrusion detection (e.g., STIDE, pH, RISE), automated software repair (e.g., GenProg), and biological modeling (e.g., CancerSim). Current projects include engineering diversity for enhanced cybersecurity, measuring internet censorship, and modeling immune and cancer systems.

Alexander Arndt Pasgaard XylanderView profile

Research Fellow

• Biomedical Engineering
• Public Health
• Telemedicine
• +3 more

Alexander Arndt Pasgaard Xylander is a Post.Doc researcher at Aalborg University's Department of Health Science and Technology, affiliated with The Faculty of Medicine Public Health and Epidemiology. He holds a PhD in Biomedical Engineering and Neuroscience (2024) and a Master of Science in Public Health (2018). His research focuses on leveraging data-driven methods to improve healthcare equity, particularly in telemedicine and chronic disease management. Key areas include AI applications for telemonitoring, geospatial analysis of healthcare access, and machine learning for risk prediction in chronic heart failure patients. External positions include Subject Support Teacher at Olivia Danmark since December 2023. His work intersects biomedical engineering, public health, and machine learning, with notable contributions to synthetic data generation for medical signals and register-based cohort studies on healthcare utilization. Recent publications highlight innovations in interpretable AI models, telemedicine equity, and geospatial healthcare insights. No scientific awards are explicitly stated, but his research has garnered attention for its practical implications in public health policy.

Ryutaro YamashitaView profile

Adjunct Professor

• Quantum Computing
• Cryptography
• Error Correction
• +3 more

Ryutaro Yamashita is an Adjunct Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research spans quantum computing, cryptography, and machine learning. Focus areas include quantum error correction, secret sharing schemes, and adversarial attacks on depth estimation networks. Collaborates on entanglement-assisted codes and finite field applications. Recent work highlights trends in securing quantum information systems and enhancing neural network robustness.

• Natural Language Processing
• Computational Social Science
• Network Science

Anders Giovanni Møller is a PhD Fellow at the IT University of Copenhagen , specializing in Natural Language Processing and Computational Social Science . His work explores the intersection of Large Language Models (LLMs) and human social behavior, with a focus on ethical AI applications in digital communities. Research Interests include: Evaluating LLMs' ability to simulate human dialogue Assessing prompt refinement vs. fine-tuning methodologies Investigating generative AI's impact on social media ecosystems Network science approaches to online community dynamics Current Project : COCOONS (2023-2025): Examines collective coordination through social media, funded by the Carlsberg Foundation

Troels Bjerre LundView profile

Associate Professor

• Algorithmic Game Theory
• Equilibrium Computation
• Equilibrium Refinements
• +4 more

Troels Bjerre Lund is an Associate Professor in the Computer Science Department at the IT University of Copenhagen. His research focuses on Algorithmic Game Theory , Equilibrium Computation , and Mechanism Design , with applications in artificial intelligence and poker game theory. He has served on program committees for major conferences including AAMAS, SAGT, IJCAI, AAAI, and EC. His work includes developing GT-Framework (gtf) for analyzing two-player zero-sum games and creating poker-playing AI systems like Tartanian and GS3. Education: PhD from Aarhus University (2008) Key Contributions: Polynomial-time algorithms for proper equilibrium computation, complexity analysis of game-theoretic concepts, and automated game abstraction techniques.

• Cardiology
• Artificial Intelligence in Medicine
• Electrocardiogram Analysis
• +3 more

Vajira Lasantha Bandara Thambawita serves as an External Researcher in the Department of Biomedical Sciences at the University of Copenhagen's Faculty of Health and Medical Sciences, focusing on the physiology of circulation, kidney, and lung with specialized expertise in AI-driven cardiovascular diagnostics. His research interests include: Development and validation of AI systems for cardiology Electrocardiogram (ECG) analysis using deep learning techniques Medical data privacy implications of generative models Explainable AI for clinical decision support Retinal image analysis for cardiovascular risk prediction Privacy-preserving methods in medical AI Analysis of his 2021-2025 publications reveals a critical focus on AI validation frameworks in cardiology, with significant contributions to ECG interpretation explainability and retinal image-based cardiovascular diagnostics. His work consistently addresses the tension between AI innovation and clinical safety, particularly regarding privacy vulnerabilities exposed by synthetic medical data generation. Dr. Thambawita operates within the University of Copenhagen's cardiovascular research ecosystem, contributing to interdisciplinary projects that bridge computational science and physiological medicine through the Heart, Renal and Circulation research group.

• Computational Genomics
• Machine Learning
• Bioinformatics
• +3 more

Arnór Ingi Sigurdsson serves as a Research Fellow within the Rasmussen Group at the Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, under the Faculty of Health and Medical Sciences. His work bridges computational innovation with biomedical applications, focusing on genomic data analysis and machine learning methodologies to address complex healthcare challenges. His research spans computational genomics, deep learning, and bioinformatics, with specific emphasis on genetic risk prediction for surgical outcomes, metagenomic binning techniques, and integrative models for human genomic data. Sigurdsson develops advanced neural network architectures to improve precision medicine applications, particularly in surgical risk assessment and microbiome characterization, demonstrating strong interdisciplinary collaboration across computational and clinical domains. Recent publications reveal a clear trajectory toward leveraging deep learning for genomic data interpretation, with impactful contributions in PLoS ONE and Communications Biology. His work consistently applies cutting-edge AI techniques—including adversarial autoencoders and integrative modeling—to solve concrete biomedical problems, establishing him as an emerging contributor in computational genomics. As an integral member of the Rasmussen Group, Sigurdsson collaborates extensively with cross-institutional teams at the Center for Protein Research, utilizing large-scale genomic datasets to advance personalized medicine solutions while contributing to the group's reputation in computational biology.

• Computational Biology
• Metagenomics
• Bioinformatics
• +5 more

Jakob Nybo Nissen serves as an Assistant Professor and Affiliate Professor with the Rasmussen Group at the Faculty of Health and Medical Sciences, University of Copenhagen. His research focuses on computational biology with emphasis on metagenomics, bioinformatics, and machine learning applications for genomic data analysis. Dr. Nissen's primary research interests include: Metagenome binning and taxonomic classification Application of deep learning to genomic sequence analysis Development of computational tools for microbial community analysis Influenza virus evolution and zoonotic transmission Multi-omics data integration for disease research His recent publications demonstrate a strong focus on improving computational methods for metagenomic analysis, with several papers published in high-impact journals including Nature Communications, Nature Biotechnology, and Nature Methods. His work bridges computer science and biology, developing novel algorithms that address critical challenges in genomic data processing. Dr. Nissen is actively involved in collaborative research efforts, particularly through the Rasmussen Group, and has contributed to major initiatives like the Critical Assessment of Metagenome Interpretation. His work has practical applications in microbial ecology, disease surveillance, and personalized medicine approaches. Based at the Center for Protein Research at the University of Copenhagen, Dr. Nissen works in a vibrant research environment that integrates computational and experimental approaches to address fundamental biological questions with biomedical relevance.