جست‌وجوی استاد و پژوهشگر

Carsten Wittمشاهده پروفایل

استاد

• Evolutionary Algorithms
• Runtime Analysis
• Genetic Algorithms
• +۷ مورد دیگر

Carsten Witt is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), working within the Algorithms, Logic and Graphs section. His research is centered on theoretical aspects of evolutionary computation, with a strong emphasis on runtime analysis, genetic algorithms, and randomized search heuristics. He is actively involved in guiding PhD research and has a substantial publication record in top-tier conferences and journals. PhD in Computer Science, Technical University of Dortmund, Germany Postdoctoral research at Max Planck Institute for Informatics Professor at DTU since appointment His primary research interests lie in evolutionary algorithms , runtime analysis , and probability theory in algorithmics . He investigates how bio-inspired optimization techniques such as genetic and compact genetic algorithms perform on benchmark problems like OneMax and LeadingOnes. His work often involves rigorous mathematical analysis to derive bounds on expected runtime and convergence behavior. The recent articles show a consistent trend in the theoretical foundations of evolutionary computation , particularly focusing on multi-valued representations, dynamic mutation strategies, neuroevolution models, and self-adjusting mechanisms. These works span subfields such as stochastic optimization, adaptive parameter control, and algorithmic analysis under probabilistic models. The dominant keywords include Computer Science, Theoretical Computer Science, Optimization, and Artificial Intelligence. Carsten Witt has not been explicitly listed with any scientific awards in the provided text. He has supervised several PhD students, including Adak, Rajabi, and Gießen, in projects related to nature-inspired algorithms and theoretical analysis. While specific grant names are not listed, his involvement in multiple funded PhD projects indicates active participation in research funding and academic leadership. Supervision roles include both main supervisor and examiner positions across various DTU research initiatives. Carsten Witt is affiliated with the Algorithms, Logic and Graphs group at DTU, which functions as a research lab focusing on foundational aspects of computing. This team conducts high-level theoretical research in algorithm design, discrete mathematics, and computational complexity, particularly in the context of heuristic and evolutionary methods.

• Algorithms
• Data Structures
• Big Data
• +۵ مورد دیگر

Rasmus Pagh is a Professor in the Algorithms and Complexity section of the Computer Science Department at the University of Copenhagen, Denmark. He is an ACM Fellow and a core researcher at the BARC (Basic Algorithms Research Copenhagen) center, with additional affiliation at the DeLTA Lab. From January 2019 to December 2020, he served as a visiting faculty researcher at Google Research. His primary research interests focus on algorithms and data structures with emphasis on big data applications. His work spans information retrieval, the role of randomness in computing, database applications, knowledge discovery, and more recently, differential privacy. His research has practical implications across multiple domains requiring efficient data processing solutions. Pagh's recent work shows a strong emphasis on differential privacy, particularly exploring correlated noise mechanisms and multiple release scenarios that maintain privacy guarantees while maximizing utility. His research demonstrates how to create multiple privacy-preserving releases without degrading overall privacy protection. ACM Fellow As an active supervisor, Pagh currently mentors four PhD students (Sia Sejer, Lukas Retschmeier, Joel Andersson, and Johanna Düngler) and has successfully graduated twelve PhD students whose careers now span academia and industry at organizations including Akamai Technologies, Microsoft, and Oracle. He serves on numerous prestigious program committees including STOC, FOCS, and ICALP, and has held leadership roles such as steering committee chair for the European Symposium on Algorithms (ESA) from 2022-2024. His research is centered at BARC (Basic Algorithms Research Copenhagen) where he leads work on fundamental algorithmic problems with applications to real-world data challenges. The center provides a collaborative environment for theoretical and applied algorithm research.

Søren Feodor Nielsenمشاهده پروفایل

استاد

• Finance
• Financial Econometrics
• Statistics
• +۳ مورد دیگر

Søren Feodor Nielsen is a Professor in the Department of Finance at Copenhagen Business School, Denmark, holding an active ORCID profile (0000-0002-9399-4918). His academic work spans finance, statistics, and international business with significant contributions to econometric methodology. Research interests center on Finance and Financial Econometrics , particularly volatility modeling and corporate default analysis. His statistical expertise focuses on Missing Data Analysis and Survival Analysis , including coarsening at random mechanisms and imputation techniques. Additional work explores International Business dynamics such as economic sanctions and R&D globalization. This interdisciplinary approach bridges theoretical statistics with real-world financial applications. Publication trends reveal an evolution from foundational statistical methods (2000-2010) toward applied finance and international business (2015-2023). Recent work integrates econometric rigor with contemporary issues like economic sanctions and fintech, while maintaining core contributions to missing data theory. The research demonstrates consistent methodological innovation across finance, statistics, and business disciplines. Advising: Nielsen has supervised 5 students according to institutional records, though specific details of these supervisions are not publicly documented in available sources.

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +۲ مورد دیگر

Anders Aamand is a Postdoc at the Department of Computer Science , University of Copenhagen , affiliated with the Faculty of Science . His research focuses on algorithms and computational complexity, particularly in computational geometry, hashing techniques, and theoretical guarantees for algorithmic efficiency.

Frederik Ravn Klausenمشاهده پروفایل

پژوهشگر

• Mathematical Physics
• Statistical Mechanics
• Quantum Mechanics
• +۳ مورد دیگر

Frederik Ravn Klausen is a Guest Researcher at the Department of Mathematical Sciences, University of Copenhagen, and a postdoc in mathematical physics at Princeton University. His research focuses on mathematical physics, statistical mechanics, and quantum systems. His recent publications explore topics such as Anderson localization in open quantum systems, phase transitions in the Ising model, scalability challenges in analogue quantum simulators, and stochastic cellular automaton models of culture formation. Collaborative work spans interdisciplinary areas between physics, mathematics, and computational modeling.

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.

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.

Mikkel Abrahamsenمشاهده پروفایل

دانشیار

• Computational Geometry
• Algorithms
• Complexity Theory
• +۲ مورد دیگر

Mikkel Abrahamsen is an Associate Professor at the Department of Computer Science, University of Copenhagen. His research focuses on computational geometry and theoretical computer science, particularly in packing, covering, and partitioning problems. Education: MSc in Datalogi, University of Copenhagen (2013). His research interests include ∃ℝ-completeness , geometric algorithms , clustering , and online algorithm design . He has contributed to understanding the complexity of problems like PSPACE-hardness in geometric reconfiguration and polynomial-time solutions for star partitions . His recent work spans computational geometry , clustering optimization , and online sorting algorithms , with acceptance at top venues like FOCS, STOC, and SODA. His 2024 publications include advancements in unit square packing and geometric reconfiguration . Scientific awards include the Carlsberg Semper Ardens grant and the Sapere Aude research leader grant , highlighting his impact in theoretical computer science. He advises PhD students such as Florestan Brunck , Jack Stade , and Hanwen Zhang . His outreach has been featured in Inside Science , Atlas Obscura , and Danish media outlets like Ingeniøren and Datatech .

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +۳ مورد دیگر

Anton Wøldike Friis is a Lecturer affiliated with the Department of Computer Science and the Department of Mathematical Sciences at the University of Copenhagen. He also serves on the Study Board of Psychology. His contact details include multiple email addresses: anfr@di.ku.dk, awf@math.ku.dk, and scj896@psy.ku.dk. Department of Computer Science, University of Copenhagen Department of Mathematical Sciences, University of Copenhagen Study Board of Psychology As part of the Algorithms and Complexity (AC) section at DIKU, Anton contributes to research and teaching in theoretical computer science. The AC section explores fundamental computational limits through mathematical theory, with applications in algorithm design for massive data, optimization, and machine learning foundations. The section hosts the Basic Algorithms Research Copenhagen (BARC) center and collaborates on cutting-edge theoretical work. The AC section’s research spans data structures, graph algorithms, computational complexity, and algorithmic paradigms. Anton’s role involves teaching courses in both Bachelor and Master programs, covering topics like randomized algorithms, discrete mathematics, and computational geometry. His work supports the section’s mission to bridge theoretical rigor with real-world impact, particularly in machine learning and applied domains.

Mikkel Thorupمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Hash Functions
• +۳ مورد دیگر

Mikkel Thorup is a Professor at the Department of Computer Science, University of Copenhagen, and a VILLUM Investigator heading the Basic Algorithms Research Copenhagen (BARC) Center supported by a €5.3 million grant from the VILLUM Foundation. Born in 1965, he holds a D.Phil. from Oxford University (1993). Prior roles include positions at the University of Copenhagen (1993–1998) and AT&T Labs-Research (1998–2013). His research focuses on algorithms, data structures, and hashing, with a particular emphasis on unifying theoretical and practical aspects. Awards include the ACM Fellowship, Villum Kann Rasmussen Award, and Fulkerson Prize. Education: D.Phil. in Computer Science, Oxford University (1993). Earlier positions: University of Copenhagen (1993–1998), AT&T Labs-Research (1998–2013). Research interests encompass algorithm design, data structure optimization, and hashing techniques. His work bridges theory and practice, addressing problems like graph clustering, optimization, and efficient sampling. He is known for contributions to correlation clustering, hash function analysis, and dynamic graph algorithms. Awards include the 2011 MAA Robbins Award, 2015 Villum Kann Rasmussen Award (Denmark’s top research prize), and 2021 AMS-MOS Fulkerson Prize. His Erdős number is 2, and he boasts an H-index above 60 with over 200 publications in top venues like STOC, FOCS, and SODA. Advising and grants: Leads BARC since 2017, funded by VILLUM Foundation. His research has influenced both academia and industry, particularly in telecommunications and algorithmic efficiency. Collaborates internationally, with a focus on foundational algorithmic challenges. Labs/Teams: Founder and head of BARC, fostering research in algorithms and their applications. Engages in interdisciplinary work combining theory with practical implementations.

Ioannis Caragiannisمشاهده پروفایل

استاد

• Algorithmic Game Theory
• Social Choice Theory
• Fair Division
• +۴ مورد دیگر

Ioannis Caragiannis is a Professor at the Department of Computer Science, Aarhus University. His research focuses on algorithmic game theory, fair division, mechanism design, and social choice theory. He explores computational aspects of fairness in resource allocation, voting systems, and multi-agent systems. His work often addresses theoretical challenges in designing efficient and fair algorithms with provable guarantees. Key research areas include social choice mechanisms, metric distortion in collective decisions, clustering algorithms with fairness constraints, and truthful mechanisms for budget allocation and facility assignment. He has contributed to understanding the complexity of Pareto-optimal and envy-free outcomes, as well as the efficiency of serial dictatorship mechanisms. His recent work investigates welfare-optimal allocations under computational constraints and the metric properties of sortition-based decision-making. Caragiannis has published extensively in top venues like AAAI, ICML, and SAGT. Notable contributions include studies on proportional fairness in non-centroid clustering, randomized learning-augmented auctions, and the distortion of sortition systems. His research bridges theoretical computer science with practical applications in fair resource distribution and collective decision-making processes.

Marina Georgatiمشاهده پروفایل

استادیار

• Sustainable Energy Planning
• Spatial Analysis
• Urban Demography
• +۴ مورد دیگر

Marina Georgati is an Assistant Professor at Aalborg University, affiliated with the Technical Faculty of IT and Design and the Department of Sustainability and Planning . Her research focuses on sustainable energy systems, spatial analysis, and migration patterns, with a strong emphasis on machine learning applications in urban demography and environmental planning. She leads the Sustainable Energy Planning Research Group and collaborates on interdisciplinary projects such as the Heat Roadmap Europe 5 and iDesignRES initiatives. Key Projects: iDesignRES: Open-source tools for renewable energy system design HRE5: Strategic heat planning across Europe FUME: Migration scenarios analysis for Europe BalticRIM: Maritime cultural heritage management Research Interests: Georgati’s work bridges energy sustainability, urban demography, and computational geography. She develops machine learning models for spatial disaggregation of population data, analyzes migration dynamics using gridded datasets, and evaluates heat potential in district heating systems. Her methodologies include gradient boosting, deep learning, and compositional data analysis. Grants & Activities: Principal Investigator/Participant in 5 EU-funded projects (2017–2027) Peer reviewer for Environmetrics , International Journal of GIS , and others Organizer of academic workshops on data science applications Labs/Teams: She contributes to the Data Science Lab x Academia collaboration and leads the Sustainable Energy Planning Research Group, fostering interdisciplinary innovation in energy and urban systems.

Malene Plejdrup Hansenمشاهده پروفایل

دانشیار

• Antibiotics
• Infectious Diseases
• General Medicine
• +۳ مورد دیگر

Malene Plejdrup Hansen is an Associate Professor and medical doctor at the Department of Clinical Medicine, Aalborg University, Denmark. She holds a dual affiliation with The Faculty of Medicine and the Center for General Practice. Her primary research focuses on optimizing general practitioners' management of acute respiratory tract infections and antibiotic use, aiming to balance clinical benefits and harms. She leads a research team investigating infectious diseases in general practice and chairs the General Practice Research on Infections Network (GRIN), a WHO-affiliated group. Key projects include the HAPPY PATIENT initiative, which evaluates antibiotic dispensing practices in EU countries, and the IMAGINE study focusing on nursing home infection management. She has led over 80 research outputs since 2010, with a focus on antibiotic stewardship, diagnostic strategies, and healthcare quality improvement. Hansen has contributed to national guidelines on otitis media and respiratory infections, emphasizing evidence-based practices. Her work spans clinical audits, randomized controlled trials, and systematic reviews. Notable contributions include studies on antibiotic duration for pneumonia, ultrasound-guided diagnoses, and the impact of interventions on prescribing behavior. Hansen actively engages in editorial roles, review committees, and international collaborations to advance primary care research and policy.

Flemming Witt Udsenمشاهده پروفایل

دانشیار

• Health Technology Assessment
• Telemedicine
• Cost-Effectiveness Analysis
• +۳ مورد دیگر

Flemming Witt Udsen is an Associate Professor at Aarhus University, affiliated with the Department of Clinical Medicine within The Faculty of Medicine. He is also associated with the Danish Center for Health Services Research. His research focuses on health technology assessment, cost-effectiveness analysis, and implementation research, particularly in telemedicine and chronic disease management. He holds a PhD in Health Economics (2017) and a Cand. oecon. in Economics from Aarhus University (2005). Udsen is an external examiner for Public Health and Economics programs, supervising multiple PhD students including Danny Anthonimuthu (2025), Natascha Bue (2028), and Sabine Michelsen Raunbak (2026). He has led projects such as the TeleCare Nord initiative evaluating telemedicine for COPD and heart failure patients. His work contributes to UN Sustainable Development Goals related to health and well-being. Research interests include telemedicine efficacy, health economic evaluations, and algorithm-driven predictive models for patient outcomes. Notable publications address COPD management, diabetes interventions, and the cost-effectiveness of telehealth solutions. Udsen has also explored geospatial inequities and digital health innovations in chronic disease care.

Nicklas Andersenمشاهده پروفایل

مدرس پاره‌وقت

• Virtual Reality (VR)
• Human-Computer Interaction (HCI)
• Clinical Informatics
• +۳ مورد دیگر

Nicklas Andersen is a Part-time Lecturer in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. His work bridges computer science with healthcare, education, and interdisciplinary technology applications. He specializes in Virtual Reality (VR) systems for therapeutic and educational contexts, including autism therapy, choral singing, and clinical nutrition studies. Research interests focus on human-computer interaction, clinical informatics, and software engineering tools like the CAAL concurrency workbench. His recent work (2021) emphasizes VR's potential in sensory-based therapies and collaborative artistic experiences. Earlier contributions (2015) include formal methods in concurrency analysis. No scientific awards or grants are listed in the provided text. He has no recorded advisees or students listed. His research spans labs within the Department of Computer Science, though specific lab affiliations are not explicitly stated.