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).

• 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.

Jørgen Arendt Jensenمشاهده پروفایل

استاد

• Medical Ultrasound
• Biomedical Signal Processing
• Digital Signal Processing
• +۴ مورد دیگر

Jørgen Arendt Jensen is a Professor of Biomedical Signal Processing at the Technical University of Denmark (DTU), with dual affiliations in the Department of Health Technology and the Department of Electrical Engineering (DTU Elektro). He leads the Center for Fast Ultrasound Imaging (CFU), a collaborative initiative involving DTU, BK Medical, Rigshospitalet, and DTU Nanotech. His research focuses on advanced medical ultrasound technologies, including synthetic aperture imaging, vector flow imaging, and ultrasound simulation, aiming to improve clinical image acquisition efficiency and accuracy. He teaches medical imaging courses and co-initiated the joint biomedical engineering program between DTU and the University of Copenhagen. Jensen’s work contributes to UN Sustainable Development Goals related to health and innovation. His research interests span algorithm development for fast ultrasound imaging, blood velocity characterization, and simulation of ultrasound systems. He supervises multiple PhD students and collaborates on projects involving transducer design, real-time imaging systems, and microvascular pathology analysis. Recent publications emphasize advancements in super-resolution ultrasound imaging, transducer optimization, and pressure gradient estimation. His lab, CFU, develops cutting-edge imaging solutions for clinical applications. Jensen’s contributions include patents on ultrasound imaging techniques and collaborative ventures to enhance diagnostic capabilities through interdisciplinary engineering.

John Bagterp Jørgensenمشاهده پروفایل

استاد

• Model Predictive Control
• Numerical Optimization
• Process Control
• +۵ مورد دیگر

John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.

Beate Conradyمشاهده پروفایل

دانشیار

• Infectious Disease Epidemiology
• Animal Health Economics
• Veterinary Public Health
• +۷ مورد دیگر

Beate Conrady is an Associate Professor in Infectious Disease Epidemiology and Animal Health Economics at the Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, where she joined in April 2021. She has been affiliated with the Centre for Sustainable Health (CSH) since May 2020. Previously, she led the working group on Epidemiology and Animal Health Economics at the University of Veterinary Medicine in Vienna for eight years. Her research focuses on the development and application of mathematical and economic models to assess prevention and intervention strategies for infectious diseases in veterinary and public health contexts. Key areas include zoonoses, cattle disease control, biosecurity, and One Health. She has extensive experience in international consultancy, including with EFSA, the Austrian Government, and European scientific networks. Beate's recent publications (2020–2024) demonstrate a strong emphasis on disease transmission modeling, surveillance systems, and economic evaluation of health interventions. Her work spans Salmonella, foot-and-mouth disease, Cryptosporidium, and vector-borne diseases, often employing advanced modeling techniques and large-scale data analysis. She has received more than 11 scientific awards for her contributions to infectious disease epidemiology and animal health economics. Consultant for European Food Safety Authority (EFSA) Member of international scientific committees (Med-Vet-Net, DISCONTOOLS) Principal investigator and consortium leader with over 7.5 million EUR in research funding, including more than 1 million EUR in third-party funds She is actively involved in editorial and peer-review activities for leading journals and contributes to policy development in animal health and zoonotic disease control.

Francesco Sanninoمشاهده پروفایل

استاد

• Quantum Field Theory
• Elementary Particle Physics
• Particle Physics
• +۵ مورد دیگر

Francesco Sannino is a Professor of Computational Science at the University of Southern Denmark's Department of Mathematics and Computer Science. He is affiliated with the Danish Institute for Advanced Study (DIAS) and holds a Ph.D. His research spans quantum field theory, particle physics, and complex systems modeling. Key interests include Standard Model duality, black hole physics, and epidemiological dynamics. Education: Ph.D. in Physics (not explicitly stated in provided text, inferred from title). Research focuses on theoretical physics, including conformal field theories, gauge dynamics, and applications of quantum chromodynamics (QCD). Recent work addresses black hole metrics, pandemic modeling via renormalization group methods, and composite dark matter signatures. His studies often bridge high-energy physics and complex systems. Main Research Trends: Over 15 years, Sannino has produced 333+ publications, emphasizing: Black hole physics and effective metrics Standard Model extensions and dualities Quantum field theory at conformal windows Epidemiological modeling of pandemics Awards: Elected Member of the Finnish Academy of Science and Letters (2015) EU Excellence Grant in Theoretical Physics (2005) International Referee for Austrian Science Fund Grants & Projects: Leader of the DG Center for Particle Physics Phenomenology (2014–2019) Carlsberg Foundation Semper Ardens grant (2023–2029) Coordinator for Danish CERN Instrument Center (2017–2019) Labs/Teams: Active in CP³ - Center for Particle Physics Phenomenology and DIAS, collaborating globally on projects like gravitational wave detection and pandemic modeling.

Fariba Karimiمشاهده پروفایل

استاد

• Algorithmic Fairness
• Computational Social Science
• Network Science
• +۷ مورد دیگر

Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.

Mikkel Lønborg Friisمشاهده پروفایل

دانشیار بالینی

• Surgical Simulation
• Medical Education
• Ultrasound Training
• +۷ مورد دیگر

Mikkel Lønborg Friis is a Clinical Associate Professor at Aalborg University, affiliated with the Department of Clinical Medicine under The Faculty of Medicine. He also serves as a senior consultant ( Ledende overlæge ) at Aalborg University Hospital, where he leads simulation-based training initiatives at NordSim – Centre for Skills Training and Simulation. His dual academic and clinical roles position him at the intersection of advanced surgical practice and innovative medical education. Clinical Associate Professor, Aalborg University Ledende overlæge (Chief Physician), Aalborg University Hospital Member, NordSim – Centre for Skills Training and Simulation His research interests focus on enhancing surgical and diagnostic competencies through technology-driven education. Key areas include simulation-based training, artificial intelligence in fetal and surgical ultrasound, robotic surgery assessment using deep learning, and curriculum development for cross-specialty ultrasound education. He actively contributes to improving clinical outcomes in pilonidal sinus disease and advancing AI integration in medical imaging. The recent publications highlight a strong trend toward interdisciplinary innovation, particularly in blending AI, simulation, and medical education. His work spans clinical surgery, educational methodology, and computational analysis of surgical performance. A significant portion of his research involves designing and evaluating training protocols, developing datasets for skill assessment, and exploring ethical and practical implications of AI in clinical settings. Mikkel Friis has not been publicly recognized with scientific awards in the provided text, but his leadership in simulation and curriculum design suggests significant institutional impact. He is involved in mentoring and advising through collaborative research projects, particularly in simulation and ultrasound education. While formal students are not listed, his role in study protocols and dataset creation implies supervision of junior researchers and medical trainees. He participates in funded or institutionally supported research activities related to medical education innovation and surgical technology. His involvement in press and media coverage further underscores his role as a thought leader in clinical simulation careers. Friis is deeply embedded in NordSim – Centre for Skills Training and Simulation, where he contributes to developing and implementing simulation-based assessment tools, particularly for abdominal ultrasound and surgical skills. His team collaborates across departments and institutions, focusing on creating standardized, scalable training models that integrate emerging technologies like AI and virtual reality.

• Systems Biology
• Stress Response
• Stem Cell Differentiation
• +۳ مورد دیگر

Ala Trusina is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Biocomplexity and Biophysics . Her research integrates coarse-grained modeling to study complex biological systems. Key Research Areas Stress Response Systems (diabetes, aging, cancer, inflammation) Stem Cell Differentiation (cell fate coordination, reversibility of states) Complex Systems (species coexistence, epidemics, CRISPR-phage interactions) Methodologies Theoretical: Agent-based modeling, in-silico simulations Experimental: Quantitative single-cell imaging, RNA/protein profiling Collaborations Joshua Brickman (ES cells), Anne Grapin-Botton (pancreas), Feroz Papa (diabetes), Else Kai Hoffman (p53 dynamics) Thomas Mandrup-Poulsen (inflammation), Savas Tay (spatio-temporal regulation) Teaching Physics of Molecular Diseases Numerical Methods in Physics

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

• Bioinformatics
• Protein Stability
• Molecular Biology
• +۳ مورد دیگر

Amelie Stein is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in Bioinformatics and RNA Biology. Her research focuses on protein stability, molecular mechanisms of disease variants, and computational methods for protein design. She is affiliated with the UCPH Quantum Hub, reflecting interdisciplinary interests in biological systems. Her work integrates bioinformatics tools, mutational scanning, and structural biology to understand protein degradation pathways and their relevance to human diseases such as Lynch syndrome and metabolic disorders. Key research areas include analyzing protein variants using deep learning models (e.g., SSEmb), developing web-based tools like MutationExplorer for 3D visualization, and characterizing disease-linked mutations in proteins such as Parkin and MLH1. Her publications highlight breakthroughs in rapid protein stability predictions, degon mapping, and the interplay between protein toxicity and degradation. No scientific awards are explicitly mentioned in the provided texts. Stein’s research also explores the application of computational approaches to biotechnology and therapeutic development, emphasizing translational applications of her findings. Her lab, linked to the SCARB research group (https://www1.bio.ku.dk/english/research/scarb/), focuses on structural and computational biology, with ongoing projects involving protein quality control networks and enzyme variant analysis. Collaborations span molecular biology, bioinformatics, and interdisciplinary quantum-related research through her UCPH Quantum Hub membership.

Tobias Weidnerمشاهده پروفایل

دانشیار

• Surface Chemistry
• Biochemistry
• Biomedical Materials
• +۳ مورد دیگر

Tobias Weidner is an Associate Professor in the Department of Chemistry at Aarhus University. His research focuses on surface chemistry, protein structure analysis, and advanced spectroscopic techniques applied to biomedical and materials science challenges. Key areas include interfacial interactions, nanomaterial coatings, and protein aggregation mechanisms. He has contributed to studies on seed mucilage adhesion, anti-corrosion nanofilaments, and cryo-EM insights into amyloid polypeptides. Education details are not explicitly listed, but his affiliations indicate a strong background in physical chemistry and biochemistry. His work bridges fundamental chemical research with biomedical applications, particularly in material design for medical interfaces and molecular-level understanding of protein behaviors. Recent publications emphasize ultrafast chemical dynamics under UV irradiation and peptide design for tailored surfaces. His projects include the Aeromicrobiology initiative (ongoing until July 2025), exploring airborne microbial interactions with surfaces. No awards or grants are explicitly listed, but his extensive publication record reflects active research collaboration. He leads a lab focused on spectroscopic methods for biomedical and material systems, though specific team composition details are not provided.

Pawel Wargockiمشاهده پروفایل

استاد

• Indoor Air Quality
• Air Pollution Control
• Thermal Comfort
• +۶ مورد دیگر

Pawel Wargocki is a Professor at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU). His research focuses on indoor environmental quality, air quality management, and human health impacts of building systems. He leads projects addressing airborne infection risk, thermal comfort, and ventilation strategies in schools, offices, and healthcare settings. Key research areas include: Optimization of ventilation systems for infection control Thermal insulation in sleep environments Low-cost sensor validation for pollutant exposure CFD modeling of aerosol dynamics Public health implications of CO₂ levels Healthy building frameworks Notable projects include: IEA EBC Annex 86: Energy-Efficient IAQ Management in Residential Buildings WHO Collaborations on Airborne Infection Risk Assessment International sleep quality studies funded by Danish Research Councils His 396+ publications emphasize interdisciplinary approaches, with recent work published in Building and Environment , Infectious Disease Modelling , and Indoor and Built Environment . He supervises PhD students exploring cognitive performance in classrooms and HVAC energy savings.

Jonas L. Juulمشاهده پروفایل

استادیار

• Network Science
• Social Networks
• Epidemiology
• +۵ مورد دیگر

Jonas L. Juul is an Assistant Professor in the Computer Science Department at the IT University of Copenhagen . With a background in network science and complex systems, he employs statistical methods, mathematical modeling, and computer simulations to study social networks, spreading processes, and human behavior. Focus areas include: information diffusion in social networks Disease spread mitigation in human populations Interdisciplinary collaboration with medical doctors, economists, and computer scientists Recent research highlights include improving statistical models for pandemic forecasting through the InForM project funded by the Novo Nordisk Foundation , and groundbreaking work on contact tracing optimization and information cascade dynamics. Notable recognitions: 2025 H.C. Ørsted Research Talent Prize 2024 Novo Nordisk Foundation Data Science Emerging Investigator Grant 2025 Young Academy membership He has contributed to mathematical modeling efforts during Denmark's COVID-19 reopening in 2020 and maintains active collaborations with institutions including Cornell University , Technical University of Denmark , and Niels Bohr Institute .

• Medical Image Analysis
• Image Segmentation
• Image Registration
• +۳ مورد دیگر

Jens Petersen is an Associate Professor at the Department of Computer Science , University of Copenhagen, specializing in medical image analysis. He works within the Image Analysis, Computational Modelling, and Geometry research section. Education: B.Sc. (2005), M.Sc. (2010) in Computer Science from University of Copenhagen; Ph.D. (2014) in Medical Image Analysis from University of Copenhagen with visiting researcher experience at University College London. Research Interests: Focus on medical image analysis techniques for segmentation and statistical analysis of tubular/branching structures like airways and carotid arteries. Key methodologies involve graph cuts, optimal surface methods, and computational modeling. Current research includes AI-guided tumor delineation, longitudinal image synthesis, and physics-based deformable registration. Publications Trends: Recent work spans AI applications in oncology, computational methods for cardiovascular imaging, and algorithm development for radiation therapy. Collaborations include international institutions and cross-disciplinary teams in machine learning and medical imaging.