جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Bioinformatics
• Deep Generative Models
• +۳ مورد دیگر

Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.

Susanne Ekmanمشاهده پروفایل

دانشیار

• Social Sustainability
• Welfare State Analysis
• Organizational Ethics
• +۳ مورد دیگر

Susanne Ekman is an Associate Professor at the Department of People and Technology Organization, Ethics & Social Sustainability at Roskilde University. Her research focuses on social sustainability, welfare state analysis, and organizational ethics, particularly examining public sector challenges, workplace morale, and ethical dilemmas in modern work environments. Her work critiques systemic issues like public debt dynamics, toxic social debt in education systems, and the erosion of welfare state principles. She has published extensively on topics including employee engagement, public sector governance, and the intersection of technology with social policy. Dr. Ekman has received the Nykredits Forskningspris (2014) and is frequently engaged in media commentary on labor market trends, appearing in outlets like Politiken and Ingenioeren . Her research outputs include over 39 peer-reviewed publications and 54 media contributions, demonstrating her impactful contributions to sociological discourse. Her advisory work and grant activities are not explicitly detailed here, but her involvement with centers like the Center for Arbejdslivsforskning and Center for Organisationspsykologi underscores her interdisciplinary collaborations in workplace studies and organizational psychology.

Maxim Kontsevichمشاهده پروفایل

استاد

• Mathematical Physics
• Algebraic Geometry
• Deformation Quantization
• +۵ مورد دیگر

Maxim Kontsevich is a permanent professor at the Institut des Hautes Études Scientifiques (IHÉS), holding the AXA Chair for Mathematics since 1995 and a visiting chair at Rutgers University (one month annually since 1997). Born in 1964 in Khimki, USSR, he earned his PhD from Bonn University in 1992. His career includes visiting positions at Harvard, the Institute for Advanced Study, and Berkeley, where he was a professor from 1993 to 1995. His research spans mathematical physics, algebraic geometry, and non-commutative geometry. Notable contributions include deformation quantization, mirror symmetry, and motivic integration. His work bridges algebraic structures with geometric and physical concepts, influencing areas like topological field theories, string theory, and integrable systems. Awardees of Fields Medal (1998), Crafoord Prize (2008), and Breakthrough Prize (2014), he also holds editorial roles at Compositio Mathematica and Publications Mathématiques IHÉS. His over 50 publications explore advanced topics such as quantum cohomology, Hodge theory, and categorical structures in geometry.

• Development Economics
• Applied Econometrics
• Development Aid
• +۶ مورد دیگر

Henrik Hansen is a Professor and Head of Department at the Department of Economics , University of Copenhagen , where he has held various academic and administrative roles since 1995. He is associated with the Development Economics Research Group (DERG) and affiliated with the Copenhagen Center for Disaster Research (COPE) network. Education : MSc in Economics (1991) and PhD in Economics (1995) from University of Copenhagen. His research focuses on development economics , with emphasis on development aid effectiveness , applied econometrics , and education research . He has conducted extensive fieldwork in Myanmar and Vietnam , analyzing enterprise surveys, household data, and policy impacts. Recent publications highlight productivity in manufacturing sectors, gender wage gaps, and credit access determinants. He teaches "Applied Research Methods" in the Global Development program and supervises development economics and applied econometrics students. Professional roles include editorial positions at Review of Development Economics and Asia & The Pacific Policy Studies , alongside advisory work for Statistics Denmark and Vietnamese Academy of Social Sciences . He has organized international conferences and contributed to 25+ media discussions on topics like corruption in Africa and development financing .

Francesco Rosatiمشاهده پروفایل

دانشیار

• Sustainable Entrepreneurship
• Business Model Innovation for Sustainability
• Corporate Sustainability Management
• +۳ مورد دیگر

Francesco Rosati is an Associate Professor at the Department of Management Engineering, Technical University of Denmark (DTU), affiliated with the Centre for Technology Entrepreneurship. His work focuses on the intersection of entrepreneurship, innovation, and sustainable development, particularly addressing the UN Sustainable Development Goals (SDGs). He holds academic qualifications in management and engineering and has mentored numerous Danish and international startups. Rosati has been recognized with the Tietgen Award 2020 for early-career contributions to business-oriented social sciences. Research interests include corporate sustainability management, business model innovation for sustainability, and organizational silos analysis. His work spans multiple sectors, including healthcare and construction industries. Rosati teaches courses on strategy, entrepreneurship, and sustainability at both Master’s and executive education levels, and actively participates in global conferences. Key projects include accelerating SDG-aligned entrepreneurship education, circular business model innovation in construction, and building climate resilience for vegetable farmers in Ghana. His research has been published in journals like Business Strategy and the Environment , Journal of Cleaner Production , and Organization and Environment . Rosati has supervised several PhD students exploring topics such as entrepreneurial resilience, SDG reporting, and business model innovation. His work emphasizes bridging management and engineering disciplines to address global sustainability challenges.

Kevin Crowstonمشاهده پروفایل

استاد

• Information Systems
• Human-Computer Interaction
• Virtual Organizations
• +۵ مورد دیگر

Kevin Crowston is a Distinguished Professor of Information Science at Syracuse University's School of Information Studies (iSchool), where he examines how information technology enables new organizational forms through empirical studies, theoretical modeling, and system design. His work focuses on coordination-intensive processes in virtual settings, with significant contributions to citizen science, data science teamwork, and journalism transformation. Education A.B. in Applied Mathematics (Computer Science), Harvard University, 1984 Ph.D. in Information Technologies, MIT Sloan School of Management, 1991 Research Focus : Crowston investigates coordination mechanisms in human-AI collaboration, particularly through projects like Gravity Spy (combining citizen scientists with machine learning for gravitational wave analysis) and journalism innovation (e.g., ReelFramer for AI-assisted news-to-video translation). His framework addresses how intelligent systems reshape work design, knowledge production, and team dynamics in scientific and media contexts. Publication Trends : Recent articles (2024-2025) reveal three dominant threads: (1) Human-AI co-creation in journalism (deskilling/upskilling dynamics, creative tool adoption), (2) Citizen science evolution with AI (co-learning systems, lexical entrainment), and (3) Socio-technical governance of intelligent machines (control-accountability alignment, project archetypes). These reflect his central inquiry into how technology reconfigures work structures. Scientific Recognition ACM Distinguished Speaker Research Leadership : Crowston currently directs two major NSF initiatives: (1) HCC grant 21-06865 on intelligent support for non-expert information navigation, and (2) FW-HTF grant 21-29047 exploring human-technology collaboration in journalism. He spearheaded a Research Coordination Network establishing socio-technical frameworks for work in the age of intelligent machines, culminating in a special issue of Information, Technology & People . Collaborative Infrastructure : He co-leads the Gravity Spy citizen science ecosystem (integrating LIGO physicists, machine learning systems, and volunteers) and serves as co-editor-in-chief of Information, Technology and People , previously editing ACM Transactions on Social Computing . His MIDST platform research advances stigmergic coordination for data science teams.

• Lipid-protein interactions
• Biomimetic membranes
• Membrane transport
• +۳ مورد دیگر

Claus Hélix-Nielsen is a Professor and Head of Department at the Department of Environmental and Resource Engineering, DTU Sustain, Technical University of Denmark. His research focuses on lipid-protein interactions, biomimetic membranes, membrane transport, and membrane channel proteins, utilizing electrophysiology, fluorescence spectroscopy, and computational modeling. Current Position: Professor and Head of Department Institution: Technical University of Denmark Research Unit: DTU Sustain Department: Environmental and Resource Engineering Research Interests: Dr. Hélix-Nielsen investigates how the hydrophobic coupling between transmembrane proteins and lipid bilayers regulates protein function through bilayer mechanical properties. His work also explores how these properties influence membrane dynamics, including vesiculation. He develops biomimetic membranes for biosensor and separation technologies, employing advanced techniques like Raman spectroscopy and molecular dynamics simulations. Recent Publications Trends: His 15 most recent articles emphasize biomimetic membrane engineering, membrane biophysics, and applications in environmental and biomedical fields. Topics range from artificial ion channels and drug delivery systems to computational modeling of lipid-protein interactions and environmental monitoring sensors. Techniques: Key methodologies include electrophysiology, fluorescence spectroscopy, electron paramagnetic resonance, Raman spectroscopy, and molecular dynamics simulations.

Mikkel N. Schmidtمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Bayesian Methods
• +۳ مورد دیگر

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

Kunihiko Kanekoمشاهده پروفایل

استاد

• Theoretical Biophysics
• Complex Systems
• Chaos
• +۶ مورد دیگر

Kunihiko Kaneko is a Professor at the Niels Bohr Institute, University of Copenhagen, with a distinguished career in theoretical biophysics and complex systems. He received his PhD and MSc in Physics from the University of Tokyo, and has held leadership roles at the Universal Biology Institute and Center for Complex Systems Biology. PhD Physics, 1984 - University of Tokyo MSc Physics, 1981 - University of Tokyo His research spans five primary areas: Universal Biology, Evolutionary Constraints, Ecosystem Dynamics, Neural Cognition, and Universal Anthropology. He has published extensively on multi-level consistency principles, dimensional reduction in biological systems, and reciprocity between robustness and plasticity across scales. Recent publications show strong focus on microbial ecosystems (2025), evolutionary game theory (2025), neural modular architectures (2024), and dimensional reduction in cellular systems (2024). His work bridges physics and biology through dynamical systems theory applied to diverse phenomena from protocells to human societies.

Claudia Wagnerمشاهده پروفایل

استاد

• Computational Social Science
• Algorithmic Fairness
• Digital Behavioral Data
• +۷ مورد دیگر

Claudia Wagner is a full professor for Applied Computational Social Sciences at RWTH Aachen University and the Scientific Director of the Computational Social Science department at GESIS—Leibniz Institute for the Social Sciences. She is also an External Faculty member at the Complexity Science Hub Vienna. Her work bridges computer science and the social sciences to study algorithmic systems and their societal impacts. Her research focuses on socio-technical phenomena such as inequality, sexism, and perception bias in algorithmically infused societies. She investigates methodological challenges in using digital behavioral data to study human behavior, attitudes, and group dynamics. Her interests span computational social science, algorithmic fairness, network science, and AI ethics. The analysis of her recent publications reveals a strong emphasis on bias, fairness, and methodological rigor in digital data analysis. Her work spans AI psychometrics, gender inequality in online platforms, and validation frameworks for digital traces. She frequently publishes in top-tier venues such as Nature , Science , and AAAI conferences. DOC-fFORTE fellowship from the Austrian Academy of Sciences Four best paper awards at international conferences (ICWSM, CSCW, WWW, AAAI) Associate Editor, EPJ Data Science Steering Committee Member, International AAAI Conference on Web and Social Media Board Member, International Society for Computational Social Science Claudia Wagner has led and co-led substantial research projects funded by national and international agencies. She mentors a diverse group of PhD students working on topics like algorithmic bias, data quality, and dehumanization. She has organized training events such as the CSS Methods Summer School and delivered keynotes globally on inequality and computational social science. She leads the Computational Social Science department at GESIS and collaborates with interdisciplinary teams at RWTH Aachen and the Complexity Science Hub. Her group develops tools for measuring algorithmic impacts and visualizing disparities in socio-technical systems, such as the 'Planets of Disparity' dashboard.

Erwin Schoofمشاهده پروفایل

دانشیار

• Proteomics
• Mass Spectrometry
• Single-Cell Analysis
• +۷ مورد دیگر

Erwin Schoof is an Associate Professor at the Department of Biotechnology and Biomedicine , Technical University of Denmark. He leads the Cell Diversity Lab and focuses on advancing proteomics and mass spectrometry technologies. Expertise in single-cell proteomics , stem cell niches , and bioinformatics . Active in myelofibrosis and leukemia research , with applications in UN Sustainable Development Goals . Research Trends from 2025–2024 include: Machine learning-driven peptide sequencing (InstaNovo, InstaNexus). Single-cell resolution tools for mapping hematopoietic stem cells and tumor microenvironments . Biomarker discovery in chronic diseases and respiratory conditions . Supervision : Mentors multiple PhD students on single-cell proteomics , omics data analysis , and biotherapeutic production . Labs & Collaborations : Collaborates with international teams on plasma proteomics , 3D bioengineering , and advanced mass spectrometry workflows .

Sebastian Risiمشاهده پروفایل

استاد

• Neuroevolution
• Evolutionary Robotics
• Self-Organizing AI
• +۹ مورد دیگر

Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .

• Superconductivity
• Magnetism
• Quantum Transport
• +۷ مورد دیگر

Brian Møller Andersen is a Professor in Solid State Physics at the Niels Bohr Institute, University of Copenhagen, where he has maintained continuous academic appointments since completing his PhD. His research spans multiple frontiers of condensed matter physics with significant contributions to superconductivity and magnetism. PhD in Theoretical Physics, University of Copenhagen (2001-2003) PhD studies at Stanford University (2000-2001) MSc in Theoretical Physics, University of Copenhagen (1998-2000) International Exchange at UC Berkeley (1997-1998) BSc in Mathematics and Physics, University of Copenhagen (1994-1997) Andersen's primary research focuses on Superconductivity , particularly high-temperature superconductors where magnetism and superconductivity coexist, and Magnetism in novel quantum materials. His work extends to Quantum Transport phenomena, Ultracold Atoms in optical lattices, Topological Insulators , and Strongly Correlated Systems . Recent publications reveal a growing emphasis on altermagnetism, kagome lattice physics, and topological superconductivity, indicating significant evolution in his research trajectory toward emergent quantum phenomena. Analysis of his 15 most recent publications (2024-2025) shows a clear progression into cutting-edge areas: 60% focus on altermagnetism and novel magnetic states, 40% on unconventional superconductivity in topological materials, and 30% examining quantum confinement effects. His work demonstrates increasing interdisciplinary connections between condensed matter theory, materials science, and quantum information science, with frequent collaborations across Europe and the US. Andersen has received significant research support through prestigious fellowships including the Lundbeck Foundation fellowship (Associate Professor level, 2012-2017) and FNU Steno Stipend (Assistant Professor level, 2009-2013), alongside early career support from the Villum Kann Rasmussen Post. Doc. Stipend. His research group at the Niels Bohr Institute focuses on theoretical modeling of quantum materials, particularly computational approaches to understanding competing orders in correlated electron systems. The group maintains strong connections with experimental teams conducting neutron scattering, STM, and ARPES measurements to validate theoretical predictions.

Thomas J. D. Jørgensenمشاهده پروفایل

دانشیار

• Mass Spectrometry
• Hydrogen/Deuterium Exchange
• Protein Structural Dynamics
• +۷ مورد دیگر

Thomas J. D. Jørgensen is an Associate Professor and Head of the Research Section of Biomedical Mass Spectrometry at the Department of Biochemistry and Molecular Biology, University of Southern Denmark. He leads an active research group focusing on protein structural dynamics using mass spectrometry and hydrogen/deuterium exchange methodologies. His research interests lie at the intersection of biochemistry, structural biology, and systems biology, particularly in understanding how protein dynamics govern function and dysfunction in diseases such as Parkinson’s and metabolic disorders. He employs advanced mass spectrometry techniques to study conformational changes, protein-protein interactions, and enzyme regulation, with a strong emphasis on lipoprotein metabolism and neurodegenerative disease mechanisms. The most recent articles highlight a consistent focus on protein dynamics in lipid metabolism, conformational diseases, and glycoprotein analysis, utilizing hydrogen/deuterium exchange mass spectrometry (HDX-MS) and related techniques. Themes include allosteric regulation of enzymes, protein unfolding, and structural phenotyping of disease-related aggregates. Member of the evaluation committee at the Swedish Research Council (2015) Chairman of multiple PhD evaluation committees (2014) Editorial and peer-review contributions to the scientific community He currently supervises PhD students and is involved in several major research projects funded by organizations such as the Michael J. Fox Foundation and Novo Nordisk Fonden. His lab, the Thomas J. D. Jørgensen Lab, includes postdoctoral researchers, academic staff, and industrial PhD students, fostering a collaborative and interdisciplinary research environment focused on biomedical mass spectrometry and systems biology.

Francisco Camara Pereiraمشاهده پروفایل

استاد

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
• +۳ مورد دیگر

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.