جستجوی استاد راهنمای دانشگاهی

Alessandra Meddis serves as an Assistant Professor in the Section of Biostatistics within the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences. Her academic work centers on developing and applying advanced statistical methodologies for longitudinal and time-to-event data analysis, with significant contributions to public health research in Denmark and internationally. Her institutional affiliation is clearly established through university contact details and departmental listings. Her primary research interests include correlated survival data analysis, competing risk modeling, informative cluster size methodology, causal inference techniques for observational studies, and environmental epidemiology applications. Dr. Meddis has developed specialized expertise in handling complex survival data structures while maintaining focus on real-world public health problems, particularly in HIV comorbidity patterns, environmental exposure effects, and pandemic-related mortality analyses. Her methodological innovations directly address challenges in clustered and censored data common across medical research domains. Analysis of Dr. Meddis's recent publication record reveals a consistent trajectory of high-impact interdisciplinary research spanning clinical medicine, epidemiology, and statistical methodology. Her work appears in leading journals across biostatistics, infectious diseases, and public health, demonstrating strong collaborative networks with clinical researchers and epidemiologists. Key thematic areas include HIV treatment outcomes, environmental health exposures, and critical care applications during the pandemic, with recent methodological papers advancing survival analysis techniques for complex data structures. Scientific Awards: No scientific awards were specified in the available institutional profile. Advising and Grants: The institutional profile does not provide details regarding graduate student supervision or specific research grant funding. Her collaborative publications suggest involvement in multi-investigator projects including the COCOMO HIV cohort study and pandemic-related research initiatives. Labs and Teams: Dr. Meddis is affiliated with the Section of Biostatistics within the Department of Public Health, though specific laboratory facilities or dedicated research teams are not described in the source material. Her extensive co-authorship patterns indicate active participation in multiple research consortia across medical specialties.

• Longitudinal Data Analysis
• Time-to-Event Analysis
• Survival Analysis
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Professor John W. Edmunds is a leading academic in Infectious Disease Modelling at the London School of Hygiene and Tropical Medicine (LSHTM). Holding a Professor position since 2013 and serving as Dean of the Faculty of Epidemiology and Population Health (2013-2019), he combines mathematical, statistical, and economic models to inform public health policy. His part-time role at the Health Protection Agency (HPA) since 2008 underscores his policy advisory contributions. PhD in Infectious Disease Modelling (Imperial College, 1994) MSc in Health Economics (University of York, 1995) BSc in Biology (Imperial College, 1989) His research focuses on understanding disease transmission and optimizing control strategies. He has pioneered methods integrating social contact surveys , participatory surveillance (e.g., Influenzanet), and economic analysis to evaluate vaccines and interventions. Recent work includes SARS-CoV-2 dynamics , Ebola spatial forecasting , and typhoid vaccine prioritization . His 15 most recent publications highlight social contact patterns (Reconnect, CoMix studies), vaccine impact (HPV, typhoid), and real-time outbreak analytics (Ebola, cholera). Key trends include digital epidemiology , behavioral surveillance , and cross-country modeling for global health. Knighthood (2024) for services to epidemiology Weldon Memorial Prize (2022) for biostatistics contributions FMedSci (2018) for medical science excellence OBE (2016) for public service As an educator, he teaches "Modelling and the Dynamics of Infectious Diseases" and co-organizes "Pandemics: Emergence, Spread and Response" . Current grants include National Institute for Health and Care Research projects on post-pandemic surveillance and Bill & Melinda Gates Foundation funding for polio eradication. He leads collaborations with the Centre for Mathematical Modelling of Infectious Diseases , Vaccine Centre , and Health in Humanitarian Crises Centre , while advising UK and WHO committees on zoonotic influenza , variants , and testing programs .

• Infectious Disease Modelling
• Epidemiology
• Public Health Policy
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Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

• Computational Statistics
• Causal Inference
• Machine Learning
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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.

• Process Mining
• Business Process Management
• Petri Nets
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Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
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Søren Lundbye-Christensen is an Associate Professor and Biostatistician affiliated with the Clinical Institute at the Faculty of Health Sciences, Aalborg University, and Aalborg University Hospital in Denmark. He specializes in biostatistical support for medical research, with a strong emphasis on cardiovascular and epidemiological studies. His research interests include biostatistics, survival analysis, cohort studies, clinical epidemiology, and statistical modeling in public health. He has contributed to a wide array of healthcare research, particularly in cardiovascular diseases, cancer, maternal health, and infectious diseases. His methodological expertise spans time-to-event analysis, registry-based research, and interval-censored data modeling. The recent publications highlight a strong trend in applying advanced statistical methods to large-scale clinical and population-based datasets. His work often involves collaboration with medical researchers to derive prognostic models, validate clinical databases, and assess public health outcomes. Key themes include cardiovascular risk, fertility, cancer biomarkers, and implementation of medical training programs. Scientific Contributions and Recognition: Published over 320 research articles and datasets. Active contributor to methodological advancements in biostatistics. Regular peer reviewer, including for journals like the R Journal. Public engagement through media appearances on statistics and health. Academic Advising and Grants: Søren has supervised 31 student theses, formally serving as PhD supervisor for 14 theses and as a biostatistical advisor for 19 others, primarily in mathematics and statistics. He has participated in numerous research projects funded through institutional and national grants, including studies on seasonal disease trends, postoperative complications, and metabolic disease prediction. His work often involves interdisciplinary collaboration across medicine, public health, and data science. Labs and Research Teams: He is embedded in collaborative research networks at Aalborg University Hospital and Aalborg University, contributing statistical expertise to clinical research groups. He is involved in projects utilizing Danish national health registries and has contributed to the development and validation of clinical databases. His work supports both hypothesis-driven medical research and methodological innovation in biostatistics.

• Biostatistics
• Medical Statistics
• Cardiovascular Research
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Wiebke Meesenburg is an Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Thermal Energy. She is actively involved in research on large-scale heat pump systems, district heating integration, and digital twin applications for energy optimization. Her research focuses on sustainable thermal energy systems, particularly the design, monitoring, and optimization of heat pumps in district heating networks. Key areas include dynamic modeling, real-time adaptation, fouling mitigation, and the integration of renewable energy sources. She contributes to advancing energy efficiency and sustainability in urban infrastructure. The recent publications highlight a strong trend toward digitalization and optimization of thermal systems, with an emphasis on model-based monitoring, digital twins, and operation scheduling using advanced algorithms. Her work bridges mechanical engineering, energy systems, and computational modeling to improve system performance and reliability. She has supervised PhD research and contributed to major projects such as the implementation of digital twins for heat pump systems and EnergyLab Nordhavn. Collaborations involve key figures in energy research at DTU, including Professor Brian Elmegaard. While no formal awards are listed, her active participation in conferences and project leadership demonstrates recognition in her field. Wiebke Meesenburg has been involved in organizing and presenting at international events, including the 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems and workshops on Modelica and flexible heat supply. Her work is embedded in interdisciplinary teams focused on future energy infrastructures and smart urban energy systems.

• Thermal Energy
• Heat Pump Engineering
• District Heating
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Abdulkadir Çelikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark, where he is part of the DKW (Data Science and Knowledge) research group. His research focuses on genome representation learning, graph representation learning, and machine learning applications in bioinformatics and network science. Research Interests: His work lies at the intersection of artificial intelligence and biological data analysis, with a strong emphasis on scalable methods for genome and metagenome representation using k-mer profiles, as well as modeling dynamic and complex networks. He develops novel machine learning models to capture the structure and evolution of graphs over time. Recent Research Trends: His recent publications, appearing in top-tier venues like NeurIPS, AAAI, and AISTATS, demonstrate a consistent focus on improving scalability and effectiveness in representation learning. Key themes include revisiting traditional k-mer methods for modern deep learning, modeling citation dynamics, and developing continuous-time node embedding techniques. His work bridges theoretical advances with practical applications in genomics and network analysis. Scientific Awards: Best Paper Award, TGL Workshop @ NeurIPS 2023 Top Reviewer, LoG 2024 Conference Advising and Grants: While current advisees are not listed, he is actively leading research projects as evidenced by his recent publications and project organization (e.g., Nordic ProbAI summer school). His work is supported through institutional affiliations and likely competitive research funding, given the high-impact venues of his publications. Labs and Teams: He is affiliated with the DKW group at Aalborg University. Previously, he was part of the Inria OPIS team and the Centre for Visual Computing during his Ph.D., and worked in the Section for Cognitive Systems at DTU Compute as a postdoctoral researcher.

• Genome Representation Learning
• Graph Representation Learning
• Machine Learning
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Peter D. Ditlevsen is a Professor at the Niels Bohr Institute , University of Copenhagen, specializing in Physics of Ice, Climate and Earth (PICE) . With a background in theoretical physics, he transitioned to climate dynamics and turbulence. Dr. Scient (2004), University of Copenhagen PhD (1991), Technical University of Denmark Research Interests : Focuses on Tipping Points in the Earth System , especially AMOC collapse , using stochastic dynamical systems , alpha-stable processes , and nonlinear climate modeling . His work bridges climate physics , dynamical meteorology , and time series analysis . Recent Publications : 2025 work on ice-core-based Dansgaard–Oeschger event modeling , 2024 studies on AMOC multistability and complex system predictability , and 2023 Nature Communications paper on AMOC collapse early warning (cited 4000+ times in media). Scientific Leadership : Leads CriticalEarth H2020 (2021-24) and contributed to TiPES (2019-23). Holds Carlsberg Fellowship and Ole Rømer Prize . Outreach : Produces weekly climate science podcast with David Trads, delivers 4-6 public lectures/year, and has appeared in 40+ media outlets. Teaches Electrodynamics , Thermodynamics , and Turbulence courses.

• Physics of Climate
• Climate Dynamics
• Turbulence
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Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

• Network Science
• Complex Systems
• Temporal Networks
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Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.

• Astroparticle physics
• Neutrino physics
• Ultra-high-energy astrophysical neutrinos and cosmic rays
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Juno Chan is a Research Fellow at the Niels Bohr Institute , University of Copenhagen , specializing in Theoretical High Energy, Astroparticle and Gravitational Physics . Holding a permanent position since 2025, Chan contributes to advanced research in gravitational wave astronomy and neutron star dynamics. Role: PhD Fellow (converted to Research Fellow) Location: Blegdamsvej 17, Copenhagen Ø Contact: chun.lung.chan@nbi.ku.dk , +45 35 32 87 21 Chan's research focuses on gravitational wave detection , neutron star magnetohydrodynamics , and gravitational lensing in astrophysical contexts. Key contributions include: Developing gravitational wave detection algorithms in lensed systems Modeling magnetized rotating neutron stars with relativistic MHD simulations Curating the LensCAT catalog for known gravitational lenses Multi-messenger lensing studies through LIGO-Virgo-KAGRA collaborations Publications and Research Trends Chan's 15 most recent publications (2023-2025) demonstrate expertise in gravitational wave astronomy , neutron star physics , and computational astrophysics . The work involves: Waveform analysis for lensed events MHD simulations of compact objects Multi-messenger lensing frameworks Signal processing for detector networks Cosmological implications of lensing Collaborations span international institutions in gravitational wave networks, with frequent contributions to Physical Review D and Monthly Notices of the Royal Astronomical Society . No formal awards or student mentorship details are publicly available.

• Gravitational Physics
• Astrophysics
• High-Energy Physics
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Nanna Bach-Møller is a postdoctoral fellow at the Niels Bohr Institute, University of Copenhagen, specializing in astrophysics and planetary research. She completed her PhD in 2024 with a dissertation on exoplanet atmospheres in high-energy radiative environments. Research Interests Exoplanet atmospheres and their interaction with high-energy radiation Microlensing events and brown dwarf characterization Planetary system dynamics and orbital evolution Stellar activity and its impact on exoplanet observations Her work combines theoretical modeling with observational data analysis, focusing on understanding atmospheric processes in extreme environments and the dynamics of planetary systems. Publications Dr. Bach-Møller has published extensively in leading journals such as Astrophysical Journal , Astronomy & Astrophysics , and Monthly Notices of the Royal Astronomical Society . Her recent work includes studies on cloud particle charging, transmission spectroscopy of exoplanets, and precision measurements of brown dwarf masses through microlensing. Collaborations She collaborates with international teams, including the MiNDSTEp Consortium, OGLE Collaboration, and MOA Collaboration, contributing to large-scale surveys and high-impact research projects.

• Exoplanet atmospheres
• High-energy astrophysics
• Microlensing events
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Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.

• Machine Learning
• Artificial Intelligence
• Data Analysis
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Kirsten Gram-Hanssen is a Professor at Aalborg University, specifically within the Department of Construction, Urban and Environmental Engineering at the Faculty of Engineering and Science. Her academic work is centered in the Section for City, Housing and Property, where she leads the Research Group for Sustainable Cities and Everyday Practice. She maintains an active research profile with numerous publications and projects focused on sustainable energy systems. Her educational background is not explicitly detailed in the provided text, but her PhD specialization in socio-technical analyses forms the foundation of her interdisciplinary approach connecting social sciences with engineering perspectives on sustainability. Gram-Hanssen's research interests focus on housing, everyday life, and consumption from a climate and energy perspective . She examines differences in household consumption practices through a practice-theoretical lens that emphasizes routines and technical infrastructures. Her work demonstrates that social organization of everyday life matters as much for home energy consumption as the technical efficiency of buildings and appliances. She also investigates how new technologies like private solar panels and smart home controls affect both energy consumption and daily life practices. Her research has significant implications for sustainable transitions and energy policy. Her recent publications show a clear trend toward addressing the energy crisis, sustainable consumption practices, and the intersection of technology with everyday life. The research spans domains including housing, food, and mobility, with a strong focus on practical solutions for energy transition and sustainability challenges. Her work increasingly addresses inequality, ethics, and policy implications of energy transitions. Active Principal Investigator on multiple major research projects including SGD: Shared Green Deal (2022-2027), FoMoHo: Food, Mobility and Housing in the Sustainable Transition of Everyday Life (2021-2025), and GECKO: Building greener societies (2021-2024) Frequent media commentator on energy and sustainability issues, with 197 media appearances documented Extensive research dataset creation including the eCAPE energy crisis interviews (2024) and smart home interviews (2022) Regular contributor to international conferences and workshops on sustainable consumption and energy practices Professor Gram-Hanssen has supervised numerous PhD students (9 documented) and actively participates in the academic community through peer review, conference organization, and editorial work. Her research group, the Research Group for Sustainable Cities and Everyday Practice, works at the intersection of social science and engineering to address sustainability challenges through understanding everyday practices and their relationship to energy systems.

• Energy Consumption
• Sustainable Transition
• Everyday Life Practices
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