جست‌وجوی استاد و پژوهشگر

Florian Keppelerمشاهده پروفایل

دانشیار

• Public Administration
• Public Policy
• Public Management
• +۳ مورد دیگر

Florian Keppeler is an Associate Professor at the Department of Political Science and the King Frederik Center for Public Leadership, Aarhus University, Denmark. His academic work centers on public administration, policy, and management, with a particular emphasis on the integration of artificial intelligence in government and the dynamics of the public workforce. He actively contributes to the academic community through research and teaching in the field of public sector human resources and leadership. Keppeler's research spans several critical areas in public administration: Recruiting, selecting, and retaining public sector employees The application and implications of artificial intelligence in government decision-making and recruitment Understanding citizen motivation to contribute to public goods Public sector employee motivation and the role of sustainability in retention Financial incentives and their effects on public health initiatives, such as vaccination campaigns His recent publications (2023-2025) reveal a strong focus on the intersection of artificial intelligence and public sector recruitment, as well as behavioral interventions in public health and human resources. Key trends include: Investigating AI aversion in public sector hiring Designing effective recruitment strategies for healthcare workers Evaluating financial incentives for societal goals (e.g., vaccination) Addressing the human capital crisis in the public sector through systematic reviews Exploring employer branding and value propositions for public sector jobs Scientific Awards: No awards mentioned in the provided text. Teaching and Professional Activities: Teaches courses: Artificial Intelligence in Public Administration, Management and Leadership in the Healthcare Sector, Leadership and Motivation in Public Organizations, Recruitment, Selection, and Retention of Public Employees No specific information about research grants or student advising provided in the available text. He is affiliated with the King Frederik Center for Public Leadership at Aarhus University, which focuses on advancing leadership and management practices in the public sector through research and collaboration.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ervin Dervishaj is a PhD Fellow at the Machine Learning Section of the Department of Computer Science, University of Copenhagen. His research spans theoretical and applied machine learning, with a focus on recommendation systems, medical imaging, and computational modeling. The Machine Learning Section engages in interdisciplinary work across Information retrieval Medical data analysis Remote sensing Sustainability Biological data modeling Recent publications highlight expertise in Recommendation system interpretability (2025) GAN-based collaborative filtering (2022) Linguistic typology through language embeddings (2018) Medical imaging applications in osteoarthritis and neurodegenerative diseases (2016-2018) Optimization algorithms for adversarial learning (2016-2017) He contributes to projects involving the SCIENCE AI Centre and the TreeSense Centre for remote sensing applications.

Birthe Dinesenمشاهده پروفایل

استاد

• Telehealth
• Telerehabilitation
• Welfare Technology
• +۷ مورد دیگر

Birthe Dinesen is a Professor at Aalborg University's Department of Medicine and Health Technology within the Faculty of Health Sciences. Her work centers on the Center for Sensory-Motor Interaction and ExerciseTech, with extensive contributions to telehealth implementation across cardiac, respiratory, and mental health domains. PhD in Health Technology (specific institution not stated) Extensive international clinical trial experience Focus on technology transfer from research to clinical practice Professor Dinesen's research spans telehealth , telerehabilitation , and welfare technology with particular emphasis on gamification and user-driven innovation . Her fingerprint analysis shows dominant work in Telerehabilitation (100%), Heart Failure management (46%), and Rehabilitation Engineering (45%). Current projects integrate machine learning for sleep apnea screening and AI for emergency department imaging analysis, reflecting her commitment to translating technology into practical healthcare solutions. Publication trends reveal consistent output since 2003 with significant acceleration after 2011. Her 211 publications demonstrate specialization in cardiac telerehabilitation (particularly atrial fibrillation and heart failure), COPD management, and stroke rehabilitation using digital tools. Recent work increasingly incorporates machine learning and social robotics. Best poster award 2023 (Klinisk Kvalitet) Professor Dinesen has secured 8 major research projects including the Velux Foundation-funded 'Brain-controlled exoskeletons for stroke rehabilitation' and the Heart Association's 'The Patient of the Future' atrial fibrillation study. She has supervised 3 PhD students and maintains 231 press/media appearances highlighting her role as a public health technology advocate. Her datasets on COPD telerehabilitation have been widely shared through Figshare. Current leadership includes the 'Future Patient: Telerehabilitation and Gamification' project (2019-2022) and active development of AI applications for emergency radiology. Her ExerciseTech lab focuses on sensor-based rehabilitation systems with international clinical validation studies.

Daniel Hendrik Malzمشاهده پروفایل

استادیار

• Quantum Information Theory
• Quantum Optics
• Many-Body Physics
• +۵ مورد دیگر

Daniel Hendrik Malz is an Assistant Professor in the Department of Mathematical Sciences at the University of Copenhagen. His research focuses on theoretical aspects of quantum technology, with particular emphasis on quantum information theory, quantum optics, and many-body physics. He is embedded in the Quantum for Life center and QMATH research group, and maintains strong collaborations with experimental groups at the nearby Niels Bohr Institute. Dr. Malz received his PhD from the University of Cambridge (2015-2018) under the supervision of Andreas Nunnenkamp, following his BA and MMath at Trinity College, Cambridge (2011-2015). He held postdoctoral positions at the Max Planck Institute of Quantum Optics (2018-2022) working with Ignacio Cirac, and at the Technical University of Munich (2022-2023) with Johannes Knolle, before joining the University of Copenhagen as an Assistant Professor in April 2023. His research program spans several interconnected areas in quantum physics. In quantum optics, he investigates many-body phenomena in open quantum systems, particularly collective effects like superradiance in waveguide QED and chiral systems. His work in quantum information theory focuses on the computational complexity of quantum many-body problems, random quantum circuits, and the boundary between classically simulable and quantum-computationally hard problems. He has made significant contributions to tensor network state preparation, developing efficient methods for creating these important quantum states. In quantum simulation, he explores novel approaches to simulating complex quantum systems, particularly using atomic arrays and superconducting circuits. Dr. Malz's publication record shows a strong focus on theoretical quantum physics with increasing engagement with experimental implementations. His recent work demonstrates a trend toward more applied research that bridges theoretical concepts with experimental feasibility, particularly in the areas of photonic quantum computing and superconducting circuit implementations. The breadth of his work spans fundamental theoretical questions to potential applications in quantum simulation and computation. Dr. Malz is actively building his research group at the University of Copenhagen and is hiring both PhD students and postdoctoral researchers. He maintains strong connections with experimental groups, particularly in the quantum optics section of the Niels Bohr Institute, and is part of Copenhagen's vibrant quantum research ecosystem, which includes the Novo Nordisk Quantum Computing Programme. His research group operates within the Department of Mathematical Sciences but is deeply integrated with Copenhagen's broader quantum research community, including collaborations with the Niels Bohr Institute and participation in the Quantum for Life center. This interdisciplinary environment allows for close interaction between theoretical and experimental researchers across multiple quantum platforms.

• Chronic Disease Management
• Healthcare Coordination
• Primary Care
• +۴ مورد دیگر

Anne Frølich is a Clinical Professor at the University of Copenhagen's Department of Public Health, Section of General Practice. She leads the Department for Coordinated Patient Pathways at Bispebjerg Hospital in Region Hovedstaden. Her work focuses on improving healthcare coordination for chronically ill patients across hospitals, community health centers, municipalities, and general practitioners. Key projects include SIKS Samarbejdet (a cross-sectoral initiative for chronically ill patients), early detection of Chronic Obstructive Pulmonary Disease (COPD), comparative studies of Danish and Kaiser Permanente healthcare systems, and The Bellagio Project to enhance patient-centered primary care in Europe and the US. Her research emphasizes telemedicine, multimorbidity management, healthcare system integration, and chronic disease outcomes. She has supervised projects involving machine learning applications in healthcare (e.g., amVAE clustering algorithm) and nationwide register studies. She teaches quality improvement methods in public health education programs. Frølich collaborates internationally, evidenced by studies with Brazilian and US institutions. Her 83+ publications span journals like BMJ Open, PLoS ONE, and JMIR Cardio, focusing on coordinated care models, drug safety reviews, and healthcare policy analysis.

Dolores Romero Moralesمشاهده پروفایل

استاد

• Data Science
• Supply Chain Optimization
• Revenue Management
• +۳ مورد دیگر

Dolores Romero Morales is a Professor of Operations Research at the Department of Economics, Copenhagen Business School (CBS). She holds editorial roles as Editor-in-Chief of TOP (Spanish Society of Statistics and Operations Research journal) and Associate Editor of Journal of the Operational Research Society and INFORMS Journal on Data Science . Prior roles include Full Professor at the University of Oxford (2003–2014) and Assistant Professor at Maastricht University (2000–2014). Education: BSc and MSc in Mathematics, Universidad de Sevilla PhD in Operations Research, Erasmus University Rotterdam Research Interests: Her work focuses on Data Science (explainability, fairness, visualization), Supply Chain Optimization (environmental sustainability, robustness), and Revenue Management (large-scale network models). Recent contributions include counterfactual analysis frameworks for machine learning models and optimization-driven solutions for decision-making under uncertainty. Grants & Projects: Leading the EU NeEDS project (€1M+ budget) to advance Data-Driven Decision Making through intersectoral collaboration. Recipient of Otto Mønsted Visiting Professorship funding for conference participation. Teaching: Current courses include Analytics and Big Data (MBA), Data Science: Data Driven Decision Making (MSc), and Mathematical Optimization (MSc). Supervised ten PhD dissertations with one ongoing. Awards & Recognition: Honorary SAS Fellow (SAS Institute) Member of SAS Academic Advisory Board

Jonas Krogh Madsenمشاهده پروفایل

دانشیار

• Public Administration
• Welfare Systems
• Citizen-State Interactions
• +۴ مورد دیگر

Jonas Krogh Madsen is an Associate Professor at the Department of Social Sciences and Business within the Roskilde School of Governance at Roskilde University. His research focuses on citizen-state interactions, administrative burden, and public sector efficiency. He holds a Cand.scient.adm from Roskilde University and an MA in History of Political Thought from University College London/Queen Mary University. His research investigates how citizens experience interactions with the state and how frontline bureaucrats perceive their roles through surveys, register data, and experiments. Key themes include red tape, welfare recipients' trust, and the psychological impacts of administrative processes. Madsen's recent work explores the effects of algorithmic tools on public sector workforces and the dual frictions faced by both bureaucrats and citizens. He has contributed to high-impact journals like Public Management Review and Public Administration, with a focus on evidence-based policy insights. He participates in interdisciplinary projects such as 'Two dimensions of transition policies in vocational education' and 'Decision-Support Systems in Public Administration.' Madsen is affiliated with organizations like the OECD and has conducted research visits at Yonsei University. His datasets are archived in platforms like Harvard Dataverse and Zenodo.

Chris Schwiegelshohnمشاهده پروفایل

دانشیار

• Machine Learning
• Algorithm Design
• Data Mining
• +۳ مورد دیگر

Chris Schwiegelshohn is an Associate Professor at the Department of Computer Science, Aarhus University, specializing in theoretical computer science and algorithm design. His research focuses on clustering algorithms, coresets, approximation algorithms, and fairness in machine learning. He has contributed significantly to the development of efficient algorithms for large-scale data analysis and privacy-preserving techniques. Key research interests include optimization in k-means clustering, distributed privacy protocols, and fair recommendation systems. His work bridges theoretical foundations with practical applications in data mining and machine learning. Publications span topics such as PAC learning for k-means, dynamic facility location, and fair projections for balanced recommendations. He has explored tradeoffs between computational efficiency and accuracy in big data clustering, as well as low-distortion clustering techniques for ordinal data. No scientific awards or grants are explicitly listed in the provided texts. His advising record remains unspecified.

Jesper Buus Nielsenمشاهده پروفایل

استاد

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

Jesper Buus Nielsen is a Professor at the Department of Computer Science, Aarhus University. He specializes in cryptography, blockchain technology, and secure multiparty computation. His research focuses on cryptographic protocols, privacy-preserving systems, and theoretical computer science. He has contributed to advancements in anonymous payments, consensus algorithms, and fault-tolerant distributed systems. Notable research interests include blockchain security, threshold cryptography, and formal verification of cryptographic protocols. His work often addresses challenges in achieving privacy, security, and scalability in decentralized systems. Selected publications include innovations in OCash (anonymous blockchain payments), CRAFT (secure MPC protocols), and formalizing network security under adaptive corruptions. He has participated in numerous international conferences and serves as a peer reviewer for leading cryptographic venues.

• Algorithms
• Complexity
• Sorting
• +۷ مورد دیگر

Riko Jacob is an Associate Professor and Head of Section in Theoretical Computer Science at the IT University of Copenhagen. His research focuses on algorithms, complexity, sorting, differential privacy, and combinatorial optimization. He leads projects such as DIREC (Digital Research Centre Denmark) and DISTRUST (Distributed business process execution under partial trust), emphasizing interdisciplinary collaboration. Research Interests: Jacob's work spans algorithm design, competitive analysis, randomized algorithms, and matrix operations. His recent studies address bichromatic sorting, saddlepoint detection, and parallel sorting with comparison errors, contributing to foundational advancements in theoretical computer science. Projects: Jacob is the Principal Investigator (PI) in key initiatives including DIREC (2020–2025), focusing on digital research innovation, and DISTRUST (2020–2025), addressing secure distributed systems. He also contributed to SSS (Scalable Similarity Search) under EU funding. Awards: No scientific awards explicitly listed, though his extensive publication record reflects scholarly impact. Advising & Grants: Supervised 1 student (name unspecified). Secured funding through Innovation Fund Denmark and EU grants, totaling millions in research support. Labs/Teams: Affiliated with the Center for Information Security and Trust and collaborates on global networks in algorithmic research.

Lars Rohwedderمشاهده پروفایل

دانشیار

• Algorithms
• Scheduling Theory
• Integer Programming
• +۳ مورد دیگر

Lars Rohwedder is an Associate Professor in Algorithms at the Department of Mathematics and Computer Science, University of Southern Denmark (SDU). His research focuses on theoretical computer science, particularly in the development of efficient algorithms for scheduling and optimization problems. Research Interests: Algorithms, Scheduling Theory, Integer Programming, Approximation Algorithms, Operations Research, Theoretical Computer Science. Recent work includes advancements in coflow scheduling approximation, makespan minimization algorithms, and fine-grained complexity analysis. Rohwedder is involved in the NWO-funded project The Twilight Zone of Efficiency: Optimality of Quasi-Polynomial Time Algorithms , exploring computational efficiency boundaries.

Torben Johansenمشاهده پروفایل

استادیار

• Machine Learning
• Econometrics
• Historical Data Analysis
• +۳ مورد دیگر

Torben Johansen is an Assistant Professor in the Department of Economics VIP at the University of Southern Denmark. His research focuses on machine learning applications in historical data analysis, econometrics, and causal inference. He actively collaborates on projects combining machine learning with socioeconomic data, such as improving census transcriptions and analyzing breastfeeding impacts on educational outcomes. Johansen teaches courses in statistics and historical economics applications, supervises master’s students in economics and medicine, and engages in academic activities through conferences and peer reviews. His work bridges computer science and social sciences, with notable contributions to occupational standardization and policy optimization. He has contributed datasets to platforms like Harvard Dataverse and engages in public discourse on child health policies. Education: Implied PhD in Economics or related field (not explicitly stated) Research Interests: Machine Learning in socioeconomic contexts, causal inference, historical data digitization, and health economics. His methodologies include neural networks, transfer learning, and optimization algorithms applied to real-world policy problems. Recent research trends emphasize interdisciplinary approaches, blending econometric models with machine learning to address challenges in healthcare, labor markets, and historical record analysis. Key contributions include improving transcription accuracy in historical censuses and evaluating long-term effects of early childhood health interventions. Awards: None explicitly mentioned Advising involves supervision of master’s students in economics and medicine. His teaching portfolio includes applied statistics and historical perspectives on economics. Grants and funding details are not detailed in the provided text. Labs/Teams: Active member of the HEDG (Historical Economics & Development Group) and collaborates with researchers in computer science and public health.

Sergey Kucheryavskiyمشاهده پروفایل

دانشیار

• Spectroscopy
• Chemometrics
• Image Analysis
• +۵ مورد دیگر

Sergey Kucheryavskiy is an Associate Professor at Aalborg University's Department of Chemistry and Bioscience within the Faculty of Engineering and Science. His research focuses on spectroscopy, chemometrics, and environmental applications, particularly in chemical process optimization and data-driven methodologies. He holds a Cand. in Physics from Altai State University (1997) and a PhD in Physics and Mathematics from the same institution. Key research areas include H2S scavenging in offshore gas production, waste valorization, and advanced data analysis techniques such as multivariate curve resolution and machine learning. He has led or participated in 17 projects, including EUDP-funded initiatives like INNO-H2S and Zero H2S-DEMO, emphasizing sustainable chemical processes and environmental solutions. His teaching excellence was recognized with the title of 'Årets Underviser' (Teacher of the Year) in 2016. Kucheryavskiy has authored over 150 publications, with recent work spanning anomaly detection via infrared thermography, green H2S scavengers, and bioenergy potential assessments. His contributions extend to educational initiatives, such as developing data science learning materials for students. PhD Supervision: 2 students Grants: EUDP, Danish Offshore Technology Center, and others Labs/Teams: Chemical and Bioengineering group, involved in collaborative projects with industry partners like OKEA ASA

Claus Dethlefsenمشاهده پروفایل

استاد مدعو

• Biostatistics
• Health Informatics
• Diabetes Research
• +۳ مورد دیگر

Claus Dethlefsen is an Adjunct Professor at the Department of Mathematical Sciences, Aalborg University, within The Faculty of Engineering and Science. His research focuses on biostatistics, health informatics, and diabetes-related studies, particularly leveraging Bayesian networks and graphical models in medical applications. Key research contributions include projects such as 'Learning Bayesian networks in R' and 'Prediction of insulin sensitivity index using Bayesian networks.' He has collaborated on initiatives like 'Modelling variation in morbidity' and 'State space models in R.' Recent publications emphasize telemonitoring in diabetes care, adherence to antidiabetic drugs, and behavioral patterns in type 2 diabetes management. His work frequently integrates statistical methodologies with clinical data analysis. He has served as an external examiner for PhD programs, organized conferences, and reviewed manuscripts for journals like BMC Public Health. His activities include presenting at Novo Nordisk on statistical methodologies and participating in international biostatistics conferences.

Munir Eberhardt Hiabuمشاهده پروفایل

دانشیار

• Interpretable Machine Learning
• Nonparametric Statistics
• Statistical Machine Learning Methods
• +۷ مورد دیگر

Munir Eberhardt Hiabu is an Associate Professor of Data Science and Actuarial Mathematics at the Department of Mathematical Sciences, University of Copenhagen. His research focuses on interpretable machine learning, nonparametric statistics, and statistical methods for high-dimensional data, particularly in risk quantification and visualization. University: University of Copenhagen Department: Mathematical Sciences Academic Rank: Associate Professor Email: mh@math.ku.dk Research Interests : Hiabu develops statistical machine learning techniques to exploit low-dimensional structures in high-dimensional data for enhanced prediction accuracy and interpretability. His work bridges actuarial mathematics, survival analysis, and additive models, with applications in non-life insurance reserving and fairness in insurance pricing. Scientific Awards : Not documented in the provided text. Advisory Opportunities : PhD and postdoc positions are available through annual calls. Prospective students must apply via institutional portals mentioning his name.