جست‌وجوی استاد و پژوهشگر

Andreas Lynge Vishartمشاهده پروفایل

پژوهشگر ارشد

• Catalysis
• Machine Learning
• Atomic Structure Optimization
• +۴ مورد دیگر

Dr. Andreas Lynge Vishart is a postdoctoral researcher at the Technical University of Denmark , affiliated with the Department of Physics and specialized in the Surface Physics and Catalysis group. He works at the intersection of catalysis and machine learning , contributing to the VISION – Center for Visualizing Catalytic Processes and Center for Catalysis Theory . Research Focus: Atomic structure optimization, electrochemical CO2 reduction, and computational methods for catalysis Key Collaborations: International partnerships in computational physics and sustainable energy research His work advances fundamental understanding of catalytic processes for renewable energy technologies. A graduate of DTU's PhD program in Accelerating catalysis simulations using surrogate machine learning models (2023), he has published in high-impact journals like npj Computational Materials and ACS Catalysis . Scientific Contributions: Developed machine learning frameworks for atomic structure optimization Investigated dual-atom catalysts and fractional atom existence Explored CO2 conversion mechanisms on CuAg alloys His research supports UN Sustainable Development Goals in clean energy and climate action.

• Macroeconomics
• Heterogeneous Agent Models
• Consumption and Saving
• +۳ مورد دیگر

Jeppe Druedahl is an Associate Professor at the Department of Economics, University of Copenhagen, specializing in macroeconomics with a focus on heterogeneous agent models, consumption-saving decisions, and computational methods. His research bridges micro-level data with macroeconomic questions through advanced modeling techniques. PhD in Economics (2015), MA in Economics (2013), BSc in Economics (2010) – all from University of Copenhagen Research areas include life-cycle models, income dynamics, and non-convex optimization in economic behavior His 15 most recent publications span topics such as: 2024: Fiscal multipliers in small open economies with heterogeneous households 2023: Feminist perspectives in mainstream economics 2022: Long-run saving responses to inheritances and intertemporal consumption patterns 2021: Computational tools for non-convex models and unemployment-risk channels in business cycles He actively develops open-source computational toolboxes including ConSav and EconModelTools , and maintains close collaborations with researchers like Thomas Jørgensen and Anders Munk-Nielsen.

Rainer Luegمشاهده پروفایل

دانشیار

• Management Accounting & Control
• Business Model Innovation
• Shareholder Value
• +۶ مورد دیگر

Rainer Lueg is an Associate Professor in the Department of Accounting (ACCT) at the School of Business and Social Sciences , University of Southern Denmark (SDU). His research focuses on Management Accounting & Control , with expertise spanning business models, shareholder value, balanced scorecards, corporate sustainability, and systematic review methodologies. Emerald Literati Awards (2018) Research Trends : Recent publications address intersections of generative AI in business research, climate risk impacts on financial markets, sustainability performance frameworks, and integrated reporting compliance. Methodologies emphasize systematic reviews, empirical analysis, and context-specific modeling. Teaching : Has instructed courses on Management Accounting, Control, and related disciplines from 2018 to 2023. Active in peer review for journals like The British Accounting Review . Network : Collaborations span European and U.S. institutions, with multidisciplinary ties to environmental economics, industrial management, and social sciences.

Oliver Baumannمشاهده پروفایل

استاد

• Organization Theory
• Strategic Management
• Innovation Management
• +۴ مورد دیگر

Oliver Baumann serves as Professor with special responsibilities at the Department of Business & Management within the Faculty of Business and Social Sciences at the University of Southern Denmark. He concurrently leads the Section Strategic Organization Design (SOD), driving research at the intersection of organizational theory, strategic management, and innovation processes. His research program investigates how organizations develop adaptive capabilities amid technological change and environmental uncertainty. Key contributions examine modular organizational designs, performance feedback systems, and collaborative search mechanisms in complex problem-solving contexts. Recent work increasingly addresses the integration of artificial intelligence and blockchain technologies into organizational structures, with empirical studies spanning platform ecosystems and news industry transformations. His scholarship bridges theoretical organization studies with practical challenges of technological adaptation across multiple sectors. Analysis of his publication trajectory reveals growing emphasis on computational approaches to organizational design, particularly regarding how algorithmic systems interact with human decision-making in dynamic environments. His 2023-2025 work shows heightened focus on AI-mediated collaboration, platform ecosystem dynamics, and adaptation mechanisms in disrupted industries like media and manufacturing. The consistent thread across his scholarship remains understanding how organizations balance exploration and exploitation under varying conditions of complexity. As head of the Strategic Organization Design section, Baumann directs research initiatives that connect with SDU's Digital Democracy Centre through investigations of technology-mediated collaboration and information ecosystem dynamics. His leadership role facilitates cross-disciplinary engagement with societal implications of digital transformation.

Line Clemmensenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning
• Data Science
• +۶ مورد دیگر

Line Clemmensen serves as Associate Professor at DTU Compute (Department of Applied Mathematics and Computer Science), Technical University of Denmark, where she has held faculty positions since 2010. Her interdisciplinary work bridges statistical learning, machine learning, and real-world applications in mental healthcare, biotechnology, and agricultural informatics. Her academic credentials include: Ph.D. in Image Analysis and Computer Graphics from DTU (2010), thesis: "High-dimensional sparse data analysis" M.Sc. in Applied Mathematics from DTU (2006) Exchange studies at Universitat Politecnica de Catalunya, Barcelona (2004) Mathematical graduate from Falkonergården upper secondary school (2000) Clemmensen's research centers on machine learning and statistical learning with emphasis on low-resource modeling, representation learning, and AI evaluation methodologies. She applies these techniques to mental health (developing biosensor-based OCD monitoring systems), biotechnology (spectral data analysis for pharmaceutical quality control), and environmental science (crop health assessment via remote sensing). Her work consistently addresses challenges in data scarcity and model interpretability. Analysis of her recent publications reveals dominant trends in computational psychiatry (e.g., detecting OCD episodes through physiological signals and oxytocin biomarkers) and agricultural AI (linking soil microbiome composition to crop health via machine learning). Methodologically, she pioneers interpretable deep learning frameworks for low-resource settings and robust time-series analysis techniques for physiological data. She has supervised 10 PhD students to completion (including Jacob Søgaard Larsen on NIR management and Gudmundur Einarsson on psychiatric motion quantification) and mentored 13 Master's/Bachelor's students. Current funding includes the LundbeckFonden LF-Experiment grant for the FAST project (Fast Assessment of psychiatric Symptoms to Transform Mental Health Care). Within DTU Compute's Section for Statistics and Data Analysis, Clemmensen leads collaborations with Novo Nordisk (biostatistics), the Danish Meat Industry, and clinical psychiatry teams, focusing on real-world AI deployment in mental healthcare and industrial applications.

Benjamin Lippمشاهده پروفایل

استادیار

• Science and Technology Studies
• Human-Machine Interfacing
• Digital Health
• +۸ مورد دیگر

Benjamin Lipp serves as Assistant Professor at the Technical University of Denmark (DTU) within the Department of Technology, Management and Economics, specializing in Science and Technology Studies. He joined DTU in 2023 following a Marie Curie Postdoctoral Fellowship at Cornell University and the University of Hamburg. Dr. Lipp earned his PhD in Science and Technology Studies from the Technical University of Munich in 2019, with a dissertation examining assistive care robots in Europe, and holds a Diploma in Sociology from Ludwig Maximilian University of Munich (2013). His research program investigates the socio-material dimensions of human-machine interfaces in healthcare contexts through the lens of Science and Technology Studies and sociology. Key research themes include: Socio-technical aspects of care robotics implementation User experiences with digital pain management technologies Co-creation processes involving patients and healthcare professionals Policy implications of healthcare technology adoption Discursive effects of synthetic data in AI development Dr. Lipp's recent publications demonstrate a clear trajectory examining interface theory, digital health technologies, and the social implications of AI in healthcare settings. His work spans theoretical contributions to STS while maintaining strong empirical grounding in healthcare contexts, particularly chronic pain management and elderly care robotics. His scholarly achievements include publications in high-impact venues such as Nature, Social Studies of Science, and Science, Technology & Human Values, along with the prestigious Marie Curie Postdoctoral Fellowship recognition. Dr. Lipp actively supervises PhD research, notably as supervisor for the "Socially Sustainable Algorithms for Chronic Pain" project (2024-2027). He has organized significant public engagement initiatives including a 2017 care robots event at TUM and a 2023 Cornell workshop on digital pain technology, facilitating dialogue between researchers, industry, practitioners, patients, and citizens.

Jonas Valbjørn Andersenمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Big Data
• +۶ مورد دیگر

Jonas Valbjørn Andersen is an Associate Professor at the IT University of Copenhagen within the Information Systems and Digital Innovation research group. His expertise lies in applying data science and machine learning to solve organizational challenges in healthcare systems, FinTech, and decentralized digital ecosystems. PhD in Information Systems and Management from Warwick Business School Focus areas: Algorithmic Decision Making, Blockchain Governance, and Complex Adaptive Systems His research leverages unstructured data from large registries, system logs, and open data sources, combining systems modeling with natural language processing and network analysis. Recent work emphasizes clinical decision support algorithms and decentralized platform ecosystems. Scientific contributions span blockchain dynamics, FinTech classification, and IoT data business models. Awards include the ECIS Best Research-In-Progress Paper Award (2019) , ECIS Outstanding Associate Editor Award (2022) , and the Warwick Doctoral Research Scholarship (2012) .

Geir Draugsvollمشاهده پروفایل

استاد

• Classical Accordion Performance
• Chamber Music
• Accordion Repertoire Development
• +۵ مورد دیگر

Geir Draugsvoll is a Norwegian accordionist and Professor at the Danish Academy of Music and Dramatic Arts (DKDM) , where he has been employed since 1995. He specializes in classical accordion performance and serves as a subject coordinator for accordion education. Draugsvoll has pioneered the accordion’s role in classical music through international collaborations, premieres of contemporary works, and editorial contributions to accordion notation. Born: 1967 Education: Danish Academy of Music and Dramatic Arts Appointments: Professor at DKDM since 2012; Visiting Professor at Tianjin Conservatory, Graz Hochschule, and others Research and Artistic Practice focuses on: Expanding classical accordion repertoire through commissions and world premieres Development of notation standards and digital tools like Accordeon Font software Distance learning pedagogy for accordion Cultural exchange via international festivals like Nordaccordion Projects and Publications include the Classical Accordion Edition series (1995–2012), the Handbook in Accordion Notation (2001), and software innovations for accordion notation. His 2025 Fachwerk recording with the London Symphony Orchestra marks a milestone in accordion-symphony integration. International Engagement spans performances at major venues like Concertgebouw, Barbican Hall, and Mariinsky Theatre, alongside masterclasses at institutions such as CNSM Paris and Sibelius Academy. He has served as jury member in prestigious competitions including Klingenthal (Germany) and Castelfidardo (Italy).

Mie Sylow Liljendahlمشاهده پروفایل

پژوهشگر ارشد

• Epidemiology
• Register-based Epidemiology
• Clinical Epidemiology
• +۵ مورد دیگر

Mie Sylow Liljendahl is a Postdoc at the National Institute of Public Health, University of Southern Denmark. Contact: lilj@sdu.dk, +4565502079. Her research focuses on epidemiology and advanced statistical methods, with emphasis on child and adolescent health and smoking cessation interventions. Her primary research areas are register-based epidemiology, clinical epidemiology, and social epidemiology. She investigates how early-life weight status influences later well-being, and she develops machine learning algorithms for register data. Her goal is to improve public health through data-driven insights. Recent publications (2024-2025) center on smoking cessation, including challenges among socially vulnerable groups, perioperative interventions, and nature-based settings. Her work often involves randomized trials and cohort studies, highlighting the importance of follow-up and tailored approaches in real-world practice. She serves as Co-Investigator on two projects: "Children's Health - Well-being and Development from Birth to Primary School" and "Well-being and Weight Status in Childhood" using the Child Health Database. These projects aim to understand early-life determinants of child health.

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Bjørn Leth Møller is a Researcher at the Department of Computer Science , University of Copenhagen , affiliated with the Image Analysis, Computational Modelling and Geometry section. His work bridges machine learning, medical imaging, and geometric modeling to address healthcare challenges. Research Focus: Medical AI, Computer Vision, and Explainable AI Department: Image Analysis, Computational Modelling and Geometry Email: bjm@di.ku.dk Research Interests include AI-driven clinical decision support, neural network interpretability for medical imaging, and sensor-ML integration for biomedical diagnostics. His projects aim to enhance real-time disease monitoring and improve transparency in AI models. Recent Publications (2024–2025) focus on ulcerative colitis severity classification, neural explanation masks for medical image models, and sensor-based tissue analysis. These works reflect interdisciplinary applications of machine learning to gastroenterology, diagnostics, and biomedical engineering.

• Machine Learning
• Computer Science
• Algorithm Development
• +۲ مورد دیگر

Lucas Silva Arenstein is a PhD Fellow in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His research aligns with the department's focus on theoretical analyses, algorithm development, and solving concrete problems for science, industry, and society. Email: lsa@di.ku.dk Location: Universitetsparken 1, 2100 København Ø The department hosts the SCIENCE AI Centre , integrating interdisciplinary AI research. His work likely intersects with computational modelling, geometry, and geometric statistics as part of broader departmental themes.

• Image Analysis
• Computational Modelling
• Geometry
• +۳ مورد دیگر

Magnus Rahbek Dalgaard Hansen is an Instructor at the Department of Mathematical Sciences and a Visiting PhD Student at the Department of Computer Science, Algorithms and Complexity section, both at the University of Copenhagen. He works in interdisciplinary research areas including image analysis, computational modelling, geometry, and machine learning.

Marleen de Bruijneمشاهده پروفایل

استاد

• Medical Image Analysis
• Machine Learning
• AI in Radiology
• +۶ مورد دیگر

Marleen de Bruijne is Professor of AI in Medical Image Analysis jointly appointed at the University of Copenhagen, Denmark and Erasmus MC – University Medical Center Rotterdam, The Netherlands. Within the Department of Computer Science at Copenhagen she belongs to the Image Analysis, Computational Modelling and Geometry section, where she leads research at the intersection of machine learning and medical imaging. Education MSc in Physics, Utrecht University, 1997 PhD in Medical Imaging, Utrecht University, 2003 Research Interests Her work centers on developing and validating machine-learning algorithms for quantitative analysis of medical images. Key themes include: Transfer learning and domain adaptation across imaging centers Deep learning architectures for segmentation and classification of pulmonary, cardiovascular and neuro images Probabilistic graphical models and Bayesian approaches for robust airway and vessel extraction Computer-aided diagnosis systems for emphysema, bronchiectasis, COPD and calcification Her group translates these techniques into clinical workflows to improve early diagnosis and patient management. Scientific Awards NWO-VENI (Netherlands Organisation for Scientific Research) NWO-VIDI NWO-VICI DFF-YDUN (Danish Council for Independent Research) Advising & Grants She has (co-)supervised 30 PhD students to completion and served as principal investigator on several large personal grants. Her funding record demonstrates sustained support from both Dutch and Danish national science foundations. Labs & Teams She is actively involved in the SCIENCE AI Centre at the University of Copenhagen and maintains strong collaborative links with Erasmus MC Radiology and Pulmonology departments, fostering cross-institutional datasets and multicenter clinical validation studies.

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Pietro Tropeano is a PhD Fellow at the Department of Computer Science, University of Copenhagen, affiliated with the Pioneer AI (P1AI) section . His research focuses on foundational topics in machine learning and artificial intelligence. Research Interests : Machine learning algorithms Computer vision Medical imaging analysis Geometric statistics Contact : Email: pitr@di.ku.dk Address: Universitetsparken 1, 2100 København Ø

• Programming Languages
• Theory of Computation
• Algorithms
• +۳ مورد دیگر

Tethys Bundgaard Svensson is a Part-time Lecturer at the Department of Computer Science (DIKU), University of Copenhagen. She is affiliated with the Programming Languages and Theory of Computation research section. Her work aligns with theoretical computer science domains such as algorithm design, computational complexity, and programming language theory. While specific research contributions or publications are not detailed in the provided materials, her institutional context suggests engagement with foundational and applied aspects of computer science, including connections to machine learning and data analysis initiatives. Contact: svensson@di.ku.dk | Research Section Website