جستجوی استاد راهنمای دانشگاهی

• Functional data analysis
• Statistical inference for dependent data
• Statistical inference for stochastic processes
• +۳ مورد دیگر

Helle Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. Her work bridges theoretical and applied statistics with interdisciplinary applications in biological and environmental sciences. Education : BSc (1993), MSc (1997), PhD (2000) in Statistics from University of Copenhagen. Employment : Professor (2018–present), Head of Data Science Lab (2018–2021), Professor MSO and head of Laboratory for Applied Statistics (2013–2018), Associate/Assistant Professor across multiple departments (2000–2013). Research Interests focus on: Functional data analysis Statistical inference for dependent data and stochastic processes Applications in biology, agriculture, and food science Her recent publications highlight statistical methodologies applied to: Enzymatic degradation of plant material Multivariate analysis in metabolic studies Random forest efficiency in metric spaces Quantile regression for longitudinal data Child food texture preferences and insect acceptance Teaching includes courses in basic probability, statistical theory, and applied statistics for bio/life sciences students. She supervises BSc, MSc, and PhD students in Statistics with co-supervision roles in interdisciplinary fields.

Mateja Novakمشاهده پروفایل

استادیار

• Model Predictive Control
• Power Electronics
• Multilevel Converters
• +۶ مورد دیگر

Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.

Martin Skovgaard Andersenمشاهده پروفایل

دانشیار

• Optimization
• Signal Processing
• Machine Learning
• +۱۲ مورد دیگر

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

• Algorithms
• Computational Complexity
• Graph Theory
• +۳ مورد دیگر

Jacob Holm is a Tenure Track Assistant Professor in the Department of Computer Science at the University of Copenhagen, specializing in the Algorithms and Complexity research section. His work focuses on theoretical computer science with emphasis on graph algorithms and data structures. Dr. Holm's research interests span multiple areas of theoretical computer science: Dynamic graph algorithms, particularly for planar graphs Biconnectivity and triconnectivity in dynamic settings Efficient data structures for graph problems Parallel and distributed algorithms for graph processing Computational geometry and pursuit-evasion problems His publication record shows 25 research outputs including 17 article in proceedings, 6 journal articles, 1 book chapter, and 1 Ph.D. thesis. His work demonstrates consistent contributions to theoretical computer science, with numerous publications in top venues like the ACM-SIAM Symposium on Discrete Algorithms (SODA). Analysis of his recent publications reveals a strong focus on worst-case performance guarantees for dynamic graph problems, particularly in planar graph settings where maintaining efficiency during updates presents significant theoretical challenges. Dr. Holm maintains an active research profile with an ORCID identifier (0000-0001-6997-9251) and collaborates extensively with researchers in the theoretical computer science community, particularly with Eva Rotenberg as evidenced by multiple co-authored publications. His work bridges theoretical computer science with practical applications, developing algorithms that maintain efficiency even as graphs dynamically change.

• Quantum Information Theory
• Quantum Optics
• Quantum Computing
• +۳ مورد دیگر

Dr. Daniel Malz is an Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research focuses on quantum many-body systems, quantum optics, and quantum computing, with affiliations to research groups QA, QMATH, and QfL. His work bridges theoretical physics and mathematical modeling, addressing topics like superradiance, entanglement dynamics, and quantum state preparation. Key research interests include quantum information theory, non-Markovian dynamics, and the development of efficient quantum simulation techniques. His recent publications explore advanced topics such as photonic cluster states, tensor network simulations, and cross-platform quantum network verification. Much of his work addresses foundational questions in quantum mechanics while maintaining practical relevance for quantum technologies. His contributions span both theoretical derivations and numerical methods, with a focus on bridging classical and quantum many-body dynamics.

• Cryptology
• Post-Quantum Cryptography
• Embedded Security
• +۳ مورد دیگر

Ruben Niederhagen is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark. His roles include affiliation with the Digital Democracy Centre and the VIP group in Artificial Intelligence, Cybersecurity, and Programming Languages. He holds external positions as Assistant Research Fellow at Academia Sinica (since 2022) and previously led the 'Advanced Cryptographic Engineering' and 'Post-Quantum Cryptography' departments at Fraunhofer SIT (2016–2020). He earned his PhD in Mathematics and Computer Science from Eindhoven University of Technology (2010–2012). His research focuses on cryptology, post-quantum cryptography, and embedded security. Key areas include quantum-resistant algorithms, cryptographic protocols, and hardware security implementations. His work addresses challenges like signature scheme optimization, electric vehicle cybersecurity, and quotable signature systems for data authenticity. Niederhagen has been featured in multiple media outlets discussing post-quantum cryptography’s role in future security. He teaches courses such as Cryptographic Engineering (DM886) and Networks and Cybersecurity (DM572). His research outputs span 33 publications, including book chapters and peer-reviewed conference papers, with a focus on practical implementations and theoretical advancements in cryptographic systems.

Abdulkadir Celikkanatمشاهده پروفایل

استادیار

• Computer Science
• Machine Learning
• Data Science
• +۳ مورد دیگر

Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.

• Hearing Impairment
• Sensory Aid
• Logopedics
• +۷ مورد دیگر

Sabina Storbjerg Houmøller is an Assistant Professor at the Department of Clinical Research, University of Southern Denmark, affiliated with the Research unit of Oto Rhino Laryngology (Odense) at KI, OUH. She holds a dual role as both an academic researcher and a Fitting Audiologist at Widex A/S, demonstrating strong connections between clinical practice and academic research. Her research interests focus on hearing impairment, sensory aids, logopedics, patient-reported outcomes, and presbyacusis, with particular expertise in randomized controlled trials and observational studies in audiology. Her work bridges clinical audiology with health services research, examining how hearing aid technology impacts real-world outcomes and quality of life. Dr. Houmøller's research output shows a clear trend toward understanding the relationship between hearing aid technology, patient-reported outcomes, and quality of life improvements, with recent work focusing on occupational noise exposure, tinnitus annoyance, and adaptation processes in hearing aid users. Her studies predominantly utilize robust methodologies including randomized controlled trials and longitudinal observational designs. She actively presents her findings at major conferences, demonstrating strong engagement with the international audiology community. Her collaborative work involves multiple Danish institutions and researchers, highlighting her integration within the national research ecosystem. As both a researcher and clinician, Dr. Houmøller contributes significantly to advancing evidence-based practices in hearing rehabilitation while maintaining direct connections to clinical applications through her work at Widex A/S.

Harry Lahrmannمشاهده پروفایل

دانشیار

• Traffic and Transportation
• Bicycle Traffic
• Traffic Safety
• +۵ مورد دیگر

Harry Lahrmann is an Associate Professor and Research Group Leader at the Department of Construction, Urban and Environmental Engineering within Aalborg University's Faculty of Engineering and Science. He specializes in traffic safety research with a focus on cyclist-pedestrian interactions, vehicle inspection systems, and urban mobility solutions. Key Research Areas: Bicycle traffic, road safety analysis, traffic engineering, and data-driven transportation policy Recent Work Trends: Utilizes ambulance data and self-reporting mechanisms to identify hazardous road locations; investigates impact of vehicle inspection programs and cycling safety technologies Awards: 1994 - First prize in bicycle safety at intersections Advising & Grants: Supervises PhD students and secures funding from institutions like TrygFonden for projects such as "Better Data on Traffic Accidents." Labs & Teams: Leads the Traffic Research Group, collaborating with experts in infrastructure, hydraulic engineering, and environmental technology.

• Biostatistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Thomas Alexander Gerds is a Professor in the Department of Public Health, Section of Biostatistics, at the University of Copenhagen's Faculty of Health and Medical Sciences. His research focuses on the development and application of statistical methods for analyzing binary, longitudinal, and time-to-event data, with a particular emphasis on causal inference and machine learning in the context of medical and public health studies. Gerds' primary research areas include causal inference, machine learning for pharmacoepidemiology, and statistical methodology for survival analysis. He collaborates with the Danish Heart Foundation on registry data studies, aiming to improve study design and causal conclusions in observational research. His work spans prediction modelling strategies, competing risk models, and the development of statistical software for applications in cardiology and public health. His recent publications (2024-2025) demonstrate a strong focus on applying advanced biostatistical methods to diverse medical fields. Key trends include the development of risk prediction models for conditions like kidney failure and cardiovascular disease, causal inference in pharmacoepidemiology (e.g., studying cholesterol-lowering drugs), and longitudinal mediation analysis. His work often involves registry-based cohort studies and systematic reviews, with applications in dentistry, neurology (cerebral palsy), diabetes, and emergency medicine.

Pernille Heyckendorff Secherمشاهده پروفایل

دانشیار

• Telemedicine
• Digital Health Technologies
• Chronic Disease Management
• +۳ مورد دیگر

Pernille Heyckendorff Secher is an Associate Professor in Nursing Science at the Department of Health Science and Technology, Aalborg University (Faculty of Medicine). She holds a PhD in Medical Informatics and has expertise in telemedicine, digital health technologies, and chronic disease management. Her research focuses on co-creating telehealth solutions to empower patients and improve healthcare efficiency. She is also involved in pedagogical innovation, particularly integrating blended learning and problem-based learning (PBL) in health education using digital tools like virtual reality and podcasts. Education: PhD in Medical Informatics (Aalborg University, 2016) MSc in Clinical Science and Technology (Aalborg University, 2012) Bachelor’s in Nursing Education (University College of Northern Denmark, 2010) Research Interests: Telemedicine for chronic diseases (COPD, diabetes in pregnancy), patient empowerment through technology, digital health tools, and pedagogical approaches for Generation Z learners. She emphasizes co-creation with clinicians and patients to design effective solutions. Teaching Contributions: Advocates for blended learning and PBL integration in health education, focusing on dynamic, multimedia-based methods to engage Gen Z students. Leads projects using VR and podcasts for reflective practice in nursing. Administrative Roles: Semester-coordinator at Clinical Science and Technology (Aalborg University). Active in UN Sustainable Development Goals related to health and education. Collaborations: Works with clinicians, patients, and interdisciplinary teams on telehealth projects. Involved in large-scale trials like the Danish TeleCare North Trial.

Jon Sporringمشاهده پروفایل

استاد

• Computer Science
• Mathematical Image Processing
• Medical Image Analysis
• +۶ مورد دیگر

Jon Sporring is a Professor at the Department of Computer Science, University of Copenhagen, specializing in theoretical and applied image processing, stochastic geometry, and biomedical imaging. He leads research in mathematical and medical image analysis, computer graphics, and pattern recognition. Education: Ph.D. in Computer Science (1998), Master in Computer Science (1995), both from University of Copenhagen Affiliations: Pioneer AI section, Applied Geometry Lab, and Faculty of Science External Roles: Visiting professor at McGill University (2012-13), co-founder of DigiCorpus Aps (2012-16) His research integrates scale-space theory, statistical shape analysis, and advanced imaging techniques for applications in medical diagnostics, materials science, and neuroscience. Recent work focuses on 3D reconstruction, persistent homology for bias correction, and AI-driven biomedical analysis. Jon teaches computer science at all academic levels, currently offering courses in bioimaging, signal processing, and deep learning. He emphasizes collaborative projects with external partners and has held administrative roles including Vice-Chair for Research at DIKU. His 15 most recent publications reflect expertise in medical imaging, 3D modeling, and AI applications, with subfields spanning neurodegenerative disease analysis, mitochondrial ultrastructure, and multi-scale image processing. Articles demonstrate interdisciplinary impact across medicine, biology, and materials science.

Sara Merino-Aceitunoمشاهده پروفایل

دانشیار

• Kinetic Theory
• Emergent Phenomena
• Mathematical Biology
• +۱۲ مورد دیگر

Sara Merino-Aceituno is an Associate Professor at the Faculty of Mathematics, University of Vienna, with prior academic roles at the University of Sussex and Imperial College London. Her research focuses on kinetic theory and mathematical modeling of emergent phenomena in biological, medical, and social systems. PhD in Mathematics, University of Cambridge (2015) MSc in Computer Science and Applied Mathematics, INP Grenoble (2010) BSc in Mathematics, Universitat Politècnica de Catalunya (2009) Her research interests center on understanding how macroscopic patterns arise from microscopic interactions, using tools from partial differential equations, probability, and numerical analysis. She specializes in interacting particle systems, collective dynamics, opinion formation, and cell tissue development. She collaborates closely with experimental biologists to validate and refine her models. The recent publications reflect a consistent focus on kinetic modeling of collective behavior, phase transitions, and multiscale analysis. Her work bridges abstract mathematical theory with concrete applications in biology and social sciences, often involving collaboration with interdisciplinary teams. She develops continuum limits of particle systems and investigates stability, alignment, and pattern formation in complex systems. Sara is actively involved in mentoring and science communication. She has co-authored study guides for students, leads outreach initiatives such as exhibitions at the 'Long Night of Research,' and produces educational videos. She also maintains a blog and YouTube channel to share insights on research, learning, and academic life. Co-authored guide: 'GOOD_STUDY_HABITS.pdf' with Amalio Fernández-Pacheco Created educational video 'Describing Patterns' with filmmaker Sameer Patel Regular contributor to public science events Active blogger on topics including coaching, creativity, and academic mindset She leads a research group called 'The HERD,' which investigates emergence in natural domains, focusing on mathematical models of biological and social systems. Her team includes postdoctoral researchers and students working on kinetic theory, numerical simulations, and interdisciplinary modeling projects.

Yushuai Liمشاهده پروفایل

استادیار

• Digital Twin
• Energy Internet
• Reinforcement Learning
• +۷ مورد دیگر

Yushuai Li is an Assistant Professor in the Department of Computer Science at Aalborg University. His research focuses on digital twin technologies, energy internet systems, distributed optimization, and cyber-physical security for energy networks. Institution: Aalborg University, Denmark Academic Rank: Assistant Professor Email: yushuaili@ieee.org, yusli@cs.aau.dk Research Interests: Li's work bridges artificial intelligence with energy systems, emphasizing: Digital Twin for Energy and Transportation Integration Reinforcement Learning in Power Trading Distributed Control for Microgrids Privacy-Preserving Energy Dispatch Autonomous Driving-Energy System Coupling Scientific Contributions: His recent publications address critical challenges in energy internet resilience, including: Distributed control under stealthy attacks Noise-resilient microgrid operations Multi-timescale optimization algorithms Event-triggered control strategies Secure peer-to-peer energy trading Honors & Awards: Recipient of multiple prestigious awards, including: Best Paper Awards (MPCE, ICCSIE, IEEE EI2) Excellent Young Expert Award (MPCE, 2023) National Natural Science Prizes (CAA 2022-2023) Highly Cited Papers (8 ESI Highly Cited, 2 ESI Hot Papers) H-index 24 with 2500+ Google Scholar Citations Academic Leadership: Serves as Associate Editor for four IEEE journals and chairs sessions at leading conferences like IEEE SmartGridComm, ISIE, and IEEE EI2. His 70+ publications span top venues including IEEE Transactions on Cybernetics, Smart Grid, and ACM SIGMOD.

Jaron Skovsted Gundersenمشاهده پروفایل

دانشگاهی

• Privacy-Preserving Computation
• Secure Multi-Party Computation
• Federated Learning
• +۱۵ مورد دیگر

Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.