جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).

Konstantinos Kalogeropoulosمشاهده پروفایل

استادیار

• Proteomics
• Snake Venom Research
• Protein Structure Prediction
• +۳ مورد دیگر

Konstantinos Kalogeropoulos is an Assistant Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), leading research at the Cell Diversity Lab. His work bridges proteomics, computational biology, and snake venom research. Current projects: "The Proteomic Landscape during Influenza Infection" (2022-2025) Supervisor for PhD projects on protease network rewiring in psoriasis and wound exudate degradomics Research interests include: Proteomic analysis of inflammatory diseases Snake venom toxin structure prediction Extracellular matrix biomechanics De novo peptide sequencing algorithms Computational modeling of protease networks Recent article trends demonstrate his work in • Database-free proteomics (InstaNovo/InstaNexus) • Snake venom pathophysiology (V-ToCs clustering) • Inflammatory disease biomarkers (psoriasis, impaired healing) • Extracellular matrix mechanics (fibronectin tension, gut inflammation) Advising: Supervises PhD students Polhaus, C. J. M. and Haack, A. M., focusing on protease networks and wound healing.

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 .

Nicola Dragoniمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.

Jun Yangمشاهده پروفایل

استادیار

• Computational Statistics
• Machine Learning
• Bayesian Statistics
• +۳ مورد دیگر

Jun Yang is a Tenure Track Assistant Professor at the Department of Mathematical Sciences, University of Copenhagen. His research spans computational statistics and machine learning, with a focus on high-dimensional inference, time series analysis, and Monte Carlo methods. Current Position: Tenure Track Assistant Professor, University of Copenhagen (2023–present) Previous Role: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Education: Ph.D. in Statistics, University of Toronto (2020), advised by Daniel M. Roy and Jeffrey S. Rosenthal Research Interests: Jun’s work addresses the intersection of computational statistics and machine learning, including: - High-dimensional Markov chain Monte Carlo (MCMC) algorithms - Bayesian variable selection in complex models - Spectral inference for nonlinear time series - Quantitative bounds and complexity analysis for MCMC Publications: His publications highlight advancements in high-dimensional sampling, time series analysis, and algorithm design. Key contributions include: - Dimension-free mixing results for Bayesian variable selection - Stereographic projection techniques for MCMC - State-domain change point detection in nonlinear regression Awards: Florence Nightingale Bicentennial Fellow, University of Oxford (2020–2023) Collaborations: Jun collaborates with researchers like K. Łatuszyński, G.O. Roberts, and J.S. Rosenthal, advancing statistical theory and applications in econometrics, machine learning, and stochastic processes.

• Molecular Imaging
• Positron Emission Tomography (PET)
• Cancer
• +۴ مورد دیگر

Professor Andreas Kjær (MD, PhD, DMSc) serves as Head of the Cluster for Molecular Imaging (CMI) at the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen. He is also Chief Physician at Rigshospitalet and co-founder of Minerva Imaging, a CRO established in 2011. University of Copenhagen Department of Biomedical Sciences Cluster for Molecular Imaging Rigshospitalet His research focuses on translational molecular imaging with PET/PET-MRI and targeted radionuclide therapies (theranostics) for precision medicine in cancer, cardiovascular, and inflammatory diseases. CMI bridges basic research to clinical implementation, leveraging interdisciplinary expertise in physics, chemistry, biology, and medicine to develop imaging agents and therapies. Recent publications highlight advancements in theranostics , PET/CT , hyperpolarized MRI , and radiopharmaceutical development . Collaborations span oncology, cardiology, and nuclear medicine, with clinical trials in neuroendocrine tumors and metabolic imaging. Current affiliations include University of Copenhagen (Professor, Department of Biomedical Sciences) Rigshospitalet (Chief Physician)

Andreas Bjerre-Nielsenمشاهده پروفایل

دانشیار

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +۶ مورد دیگر

Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.

François Raymond J Cornetمشاهده پروفایل

پژوهشگر

• Organometallic Chemistry
• Computational Chemistry
• Catalyst Design
• +۳ مورد دیگر

François Raymond J Cornet is a Postdoctoral Researcher in the Department of Energy Conversion and Storage and a PhD Student in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His dual affiliation bridges energy conversion research and computational science, focusing on AI-driven molecular design. His research spans Organometallic Chemistry , Computational Chemistry , and Machine Learning , with specialization in catalyst design through diffusion models and inverse design methodologies. Key areas include metallocene chemistry, density functional theory applications, and generative modeling for chemical space exploration, targeting organometallic complexes like Vaska's complex. Cornet's publication trajectory reveals a concentrated effort in advancing equivariant diffusion models for molecular generation, particularly addressing small-data challenges in catalyst design. His work consistently integrates quantum chemistry with deep generative architectures, establishing new paradigms for inverse-design pipelines in computational chemistry. No scientific awards were documented in the source material. He recently completed the PhD project Machine learning for electronic scale inverse design of enzymatic catalysts (2021-2025) under supervisors M. N. Schmidt (primary), A. Bhowmik, and O. Winther, with examiners W. K. Boomsma and S. Olsson. Collaborators include P. Deshmukh, B. Benediktsson, and C. A. Naesseth across multiple publications. Research operations occur within DTU's interdisciplinary framework connecting the Department of Energy Conversion and Storage and Department of Applied Mathematics and Computer Science, leveraging computational infrastructure for molecular simulations and AI model training.

Allan Hanburyمشاهده پروفایل

استاد

• Information Retrieval
• Data Mining
• Natural Language Processing
• +۱۳ مورد دیگر

Allan Hanbury is a Full Professor for Data Intelligence at the Faculty of Informatics, TU Wien, and a faculty member at the Complexity Science Hub Vienna. He leads the Data Science Research Unit and serves as the Faculty Representative for financial affairs and internationalization. He holds a PhD in Applied Mathematics from Mines ParisTech and a Habilitation in Practical Informatics from TU Wien. PhD in Applied Mathematics, Mines ParisTech, 2002 Habilitation in Practical Informatics, TU Wien, 2008 Bachelor’s and Master’s in Physics and Applied Mathematics, University of Cape Town His research focuses on information retrieval, data mining, natural language processing, and information extraction, with applications in healthcare, legal, and patent domains. He has coordinated major EU projects including Khresmoi, VISCERAL, KConnect, and DoSSIER, the latter training 15 PhD students. He is co-founder of contextflow, a spin-off commercializing radiology search technology. His recent publications (2024–2022) highlight a strong trend in systematic literature review automation, neural re-ranking, large language models, and domain-specific information extraction. Key themes include improving citation screening, patient-trial matching, evaluation metrics, and dataset creation for offensive language and legal text. His work combines technical innovation with real-world impact in medical, legal, and scientific communication contexts. Allan Hanbury has received no explicitly mentioned scientific awards in the provided text. He actively supervises numerous PhD and master’s students and leads large research projects such as DoSSIER, Transparent Automated Content Moderation, and PLFDoc, funded by FWF, WWTF, and EU. His group develops tools for evidence synthesis, clinical data extraction, and legal document analysis. He also contributes to AI and data strategy in Austria and Europe. He leads the Data Science Research Unit at TU Wien and is involved in multiple interdisciplinary projects including BRISE (building regulation analysis), CDL-RecSys, and TACo, focusing on legal and scientific document processing. His work bridges academia and industry through spin-offs like contextflow and collaborations with Deutsche Telekom, Siemens, and FMA.

Christian Rønn Hansenمشاهده پروفایل

دانشیار

• Radiation Therapy
• Head and Neck Cancer
• Treatment Planning
• +۷ مورد دیگر

Christian Rønn Hansen is an Associate Professor at the Department of Clinical Research, University of Southern Denmark , specializing in radiation therapy and oncology. His work spans head and neck cancer, prostate cancer, and glioma treatment planning, with a focus on advanced radiation techniques like VMAT, IMRT, and hypofractionation. Academic Affiliation : Department of Clinical Research, University of Southern Denmark Research Collaborations : KI (Karolinska Institute), OUH (Odense University Hospital), Danish Head and Neck Cancer Group (DAHANCA) Research Interests : Christian's research integrates artificial intelligence with magnetic resonance imaging for treatment adaptation, clinical trial quality assurance for radiation therapy, and pattern of failure analysis in head and neck cancer. His work also addresses thromboembolic risk in glioma patients and proton therapy applications for grade 1–3 gliomas. Scientific Contributions : His recent publications include systematic reviews on loco-regional failure patterns in head and neck cancer (2025), national data solutions for radiation oncology (2025), and AI-driven prostate volume analysis in hypo-fractionated radiotherapy (2025). He contributed to the 2023 ACTA ONCOLOGICA AWARD-winning work and leads discussions on international radiotherapy quality assurance standards. Scientific Awards : ACTA ONCOLOGICA AWARD (2023)

Helle Damgaard Zachoمشاهده پروفایل

استاد بالینی

• Nuclear Medicine
• Prostate Cancer
• Ovarian Cancer
• +۳ مورد دیگر

Helle Damgaard Zacho is a Clinical Professor at Aalborg University's Faculty of Health Sciences and Senior Physician at Aalborg University Hospital's Department of Clinical Physiology and Nuclear Medicine . Her work bridges clinical practice with advanced nuclear medicine research. Primary Affiliation: Aalborg University Hospital Academic Role: Clinical research and education at Faculty of Health Sciences Research Focus: PET/CT imaging for cancer diagnostics Zacho leads or co-leads multiple high-impact projects including FAPI/PSMA PET/CT studies for prostate, ovarian, and gastric cancers . Her fingerprint highlights expertise in: Prostate Cancer (100%) Positron Emission Tomography-Computed Tomography (84%) Bone Metastasis (50%) Gallium 68 applications (65%) Deep learning in radiology Cancer staging methodologies Her recent publications focus on optimizing FAPI and PSMA PET/CT for metastatic cancer detection, with active clinical trials in ovarian and prostate cancer diagnostics. While no formal awards are listed, her 147+ publications and 8 ongoing projects demonstrate substantial research output. Media coverage highlights her team's potential 'super-weapon' for cancer diagnostics (2023-2024), including national press attention for Denmark's best clinical trial 2025. She serves as a peer reviewer for journals like World Journal of Gastroenterology and participates in major urological cancer conferences.

Mette Nyegaardمشاهده پروفایل

استاد

• Genetics
• Genomics
• GWAS
• +۴ مورد دیگر

Mette Nyegaard is a Professor in the Department of Health Science and Technology at Aalborg University, within The Faculty of Medicine. Her research focuses on genetics and genomics, with over 25 years of expertise linking genetic variation to phenotypes in both rare and common complex diseases. She has a strong interest in gender medicine and women’s reproductive health, particularly leading Denmark’s contribution to the largest genome-wide association analysis of endometriosis. Nyegaard has held external positions, including Associate Professor roles at Aarhus University and Senior Scientist at Aarhus University Hospital. Education: PhD in Human Genetics, University of Southern Denmark (2003) MSc in Chemistry-Biotechnology, Aarhus University (1997) Research Interests: Nyegaard’s work spans genetics, genomics, GWAS, endometriosis, coronary artery disease, and personalized medicine. Her research emphasizes translating genetic insights into clinical applications, such as polygenic risk scores and biomarker development. She collaborates internationally, with projects addressing calmodulinopathies, cardiovascular genetics, and reproductive health. Projects & Grants: Lead PI on projects like 'Understanding the Genetic Architecture of Common Complex Diseases' and 'FEMaLe: Finding Endometriosis Using Machine Learning.' Co-Investigator in studies like 'The Deadly, the Deleterious, and the Dangerous – Decoding the Differential Pathogenicity among CALM1, CALM2, and CALM3.' Labs/Teams: Nyegaard’s work is part of interdisciplinary teams at Aalborg University and collaborating institutions, focusing on genomic medicine, endometriosis, and cardiovascular genomics. She contributes to initiatives like Neuroboost and the International Calmodulinopathy Registry.

• Statistics
• Machine Learning
• Functional Data Analysis
• +۳ مورد دیگر

Anton Rask Lundborg is a Postdoctoral Research Fellow at the Department of Mathematical Sciences, University of Copenhagen. He works in the areas of Statistics, Machine Learning, and Functional Data Analysis. Key research areas: Statistics, Machine Learning, Functional Data Analysis, Causal Inference, Bioinformatics, Nonparametric Statistics Research groups: SPT (Statistical Learning Theory), CoCaLa (Collaborative Causal Learning) His recent work focuses on causal feature selection, variable significance testing, and functional data analysis applications. Publications span journals like Journal of the American Statistical Association , Briefings in Bioinformatics , and Annals of Statistics . Methodological innovations include the Projected Covariance Measure and conditional independence testing in Hilbert spaces. Explore his full research profile and publications via ORCID or the University of Copenhagen's Mathematical Sciences website .

• Energy Engineering
• Home Automation
• Learning Algorithms
• +۱۲ مورد دیگر

Per Printz Madsen is an Associate Professor in the Department of Computer Science at Aalborg University, under The Technical Faculty of IT and Design. His research focuses on distributed, embedded, and intelligent systems with applications in energy efficiency, smart buildings, and renewable energy integration. His research interests span energy engineering, home automation, learning algorithms, fuzzy systems, neural networks, and intelligent control systems. He applies computational intelligence to optimize energy consumption in buildings and district heating systems, with a strong emphasis on real-world implementation through digital twin technology and supervisory control. The recent publications highlight a consistent trend in energy management systems, particularly in forecasting, load control, and energy brokerage in smart grids and buildings. The work emphasizes the integration of renewable generation and storage, leveraging machine learning and distributed control architectures to improve energy efficiency and reduce CO 2 emissions. He has been involved in several major research projects including ENCOURAGE, Homeport, Flexnet, and Mediterranean Virtual University, funded by entities such as Artemis JU and the Danish Enterprise and Construction Authority. These projects demonstrate sustained external funding and collaborative research across Europe. Madsen contributes regularly through technical reports and conference proceedings, primarily through Aalborg University. His work is applied and systems-oriented, targeting practical deployment in energy infrastructure. He is affiliated with the Distributed, Embedded and Intelligent Systems research group at Aalborg University, where he conducts interdisciplinary research bridging computer science and energy engineering to develop intelligent solutions for sustainable energy systems.

Clara Marika Velteمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Turbulence
• Turbulence Theory
• +۱۴ مورد دیگر

Clara Marika Velte is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), where she serves as Head of the Turbulence Centre of Excellence. Her research is centered on fluid mechanics, particularly turbulence, with a focus on theoretical modeling, experimental design, and vortex dynamics. She is actively involved in major research initiatives funded by the European Research Council and the Poul Due Jensen Foundation. PhD in Experimental and Theoretical Fluid Mechanics, DTU (2006–2009) MSc in Turbulence, Chalmers University of Technology (2004–2005) Her research interests include turbulence theory, boundary layers, flow control, vortex dynamics, and the development of theoretical models for turbulent flows. She employs advanced techniques such as Proper Orthogonal Decomposition, Reynolds decomposition, and modal analysis to study non-equilibrium turbulence and jet dynamics. Her work bridges fundamental physics with practical applications in energy and environmental engineering. Recent publications explore triad interactions, spatio-temporal structure functions, and Lumley decomposition in turbulent round jets, indicating a strong trend toward mathematical and physical modeling of turbulence dynamics. These works are targeted at high-impact journals such as Physical Review Fluids and Journal of Fluid Mechanics , reflecting her leadership in advancing theoretical frameworks in fluid dynamics. Best paper award, SEMA’18 (2018) Kalundborg Refinery Prisen (2023) Selected for special issue in Experiments in Fluids (2010) Publication in Journal of Systemics, Cybernetics and Informatics (2009) Clara Velte supervises multiple PhD students and leads several active research projects, including the PDJF Turbulence Centre of Excellence and UniEqTURB, which challenge classical paradigms like the Richardson-Kolmogorov theory. She also contributes to academic service as a member of the Editorial Board for Philosophical Transactions of the Royal Society A . Her laboratory, the Turbulence Research Laboratory at DTU, fosters interdisciplinary collaboration and innovation in turbulence science. The Turbulence Centre of Excellence at DTU, under her leadership, operates as a state-of-the-art research hub integrating experimental, theoretical, and computational approaches. The center collaborates internationally and engages in public outreach, including media appearances on turbulence in aviation and climate systems.