Tereza Blazkova is a Research Fellow at SODAS (Center for Social Data Science), University of Copenhagen, focusing on data-driven solutions to societal challenges through interdisciplinary research. Her core research areas include: Machine Learning Artificial Intelligence Educational Data Mining Algorithmic Fairness Social Data Science Blazkova's work centers on temporal dynamics of bias in educational algorithms, particularly analyzing how predictive models for student dropout evolve nationwide. Her 2025 L@S conference publication establishes her expertise in longitudinal fairness assessment, combining computational methods with social science perspectives to address equity in AI systems. As part of SODAS, she contributes to collaborative projects applying data science to real-world social issues, operating within Copenhagen's academic ecosystem focused on ethical technology development.
Frederik Boëtius Hertz serves as an Associate Professor in the Department of Immunology and Microbiology, specializing in Bacteriology, at the Faculty of Health and Medical Sciences, University of Copenhagen. His office is located at Blegdamsvej 3B, 2200 København N, and he maintains an active research profile with significant contributions to clinical microbiology and infectious disease research. Dr. Hertz's primary research interests focus on antibiotic resistance mechanisms, particularly in $$\text{Staphylococcus aureus}$$, antimicrobial stewardship in intensive care settings, and the application of machine learning to infectious disease diagnostics. His work bridges fundamental bacterial pathogenesis with clinical applications, addressing critical challenges in infection management across Danish healthcare institutions. His research program demonstrates strong translational potential, connecting laboratory findings with patient care improvements. Analysis of Dr. Hertz's recent publications reveals a consistent focus on optimizing antimicrobial therapy through rigorous clinical investigation and innovative diagnostic approaches. His work spans from molecular characterization of resistance mechanisms to large-scale epidemiological studies of antibiotic consumption patterns. A notable trend is his growing integration of computational methods with traditional microbiology, as evidenced by his machine learning applications for bloodstream infection prediction. Dr. Hertz maintains active collaborations across multiple Danish hospitals and research institutions, particularly with colleagues at Rigshospitalet and other University of Copenhagen-affiliated clinical departments. His work on the EMPRESS trial protocol demonstrates leadership in designing clinical studies to address critical questions in sepsis management. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of competitive research funding. His research group appears to focus on bacterial physiology in clinical contexts, particularly examining how pathogens like $$\text{Staphylococcus aureus}$$ adapt to antibiotic pressure and how these adaptations impact clinical outcomes. The team employs a range of methodologies from molecular genetics to large-scale epidemiological analysis and computational modeling.
Peter Christoffer Holm is a Guest Researcher at the Novo Nordisk Foundation Center for Protein Research within the Faculty of Health and Medical Sciences at the University of Copenhagen. He is affiliated with the Brunak Group and maintains an active research profile with numerous publications in high-impact journals. His work spans multiple disciplines at the intersection of genomics, artificial intelligence, and clinical medicine. Dr. Holm's research interests focus on applying computational approaches to biomedical challenges. His work encompasses genomic analysis of population health data, AI-driven medical record mining for disease detection, cardiovascular disease stratification, and cancer risk assessment. He has made significant contributions to understanding genetic variants affecting body mass index across diverse populations and developing novel methods for patient subgrouping in heart disease. His publication record shows a strong trend toward integrating artificial intelligence with clinical data, particularly in cancer detection and cardiovascular disease management. Recent work demonstrates expertise in leveraging national health datasets for predictive modeling, with publications appearing in prestigious journals including Nature Communications, The Lancet family of journals, and BMC Medicine. His research has garnered significant attention, with multiple papers covered by news outlets and widely shared on social media platforms. As a member of the Brunak Group at the Center for Protein Research, Dr. Holm contributes to a collaborative environment focused on computational biology and medical data science. His work often involves large-scale population studies and interdisciplinary collaborations across medical specialties.
Dimitri Pierre Johannes Gominski is an active Assistant Professor in the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. His research bridges computer vision and environmental science, with a focus on applying deep learning techniques to satellite imagery for environmental monitoring. He leads the C-Trees project aimed at mapping individual trees globally, with recent work spanning multiple continents including Europe, India, Rwanda, and the Peruvian Amazon. Education: PhD in Computer Vision (2018-2021) from École Centrale Lyon and IGN Paris MS in Electrical Engineering (2012-2017) from INSA Lyon Gominski's research focuses on making deep neural networks work in real-world conditions where perfect experimental setups are unavailable. His primary research interests include deep learning, generalization techniques, and image retrieval/detection/segmentation specifically applied to environmental monitoring. His work demonstrates expertise in adapting computer vision approaches for practical environmental applications, particularly in forest monitoring and land use analysis. Analysis of his recent publications (2024-2025) reveals a strong emphasis on using satellite imagery, particularly PlanetScope data, for environmental monitoring across diverse geographical contexts. His research shows interdisciplinary collaboration spanning computer science, environmental science, and geography, with applications in forest conservation, agricultural monitoring, and climate change mitigation. The publications demonstrate methodological innovation in domain adaptation for deep learning models applied to environmental monitoring challenges. Research Leadership: C-Trees project: Mapping individual trees from satellite imagery across Europe with plans for global expansion Active collaborations with researchers across multiple countries and institutions Gominski maintains an active research profile with multiple high-impact publications in 2024-2025 across journals including Nature Sustainability, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and Communications Earth & Environment. His work has received attention from policy sources, news outlets, and academic communities as evidenced by references in policy documents, media coverage, and social media discussions.
Dhouha Grissa serves as an Assistant Professor in the Department of Veterinary and Animal Sciences at the University of Copenhagen, Denmark, specializing in Preclinical Disease Biology. Her research applies advanced data mining and knowledge extraction techniques to biomedical challenges, with primary focus on early detection of alcoholic liver fibrosis through analysis of medical records and metabolomic data. Education: PhD in Computer Science, University of Clermont-Auvergne, France (awarded November 2, 2013) Research Interests: Dr. Grissa's work integrates bioinformatics, machine learning, and computational biology to develop knowledge discovery frameworks for metabolomic and clinical data. She specializes in identifying predictive biomarkers for disease progression, particularly in alcoholic liver disease, through hybrid analytical approaches combining text mining, process mining, and multi-omics integration. Publication Trends: Her recent publications (2020-2022) reveal a consistent trajectory in applying data integration and knowledge extraction to biomedical databases and clinical registries. Key themes include disease-gene association mapping, GWAS analytics for drug target illumination, and exploratory metabolomic analysis—all converging on improving disease prediction models and understanding pathophysiological mechanisms. Advising and Grants: No specific information regarding student advising or grant funding was provided in the source material. Labs and Teams: Dr. Grissa is embedded within the Novo Nordisk Foundation Center for Protein Research and actively contributes to the EU-funded Galaxy project, which develops integrated systems models for multi-omics and clinical data analysis using resources like the Danish National Patient Registry.
Marek Mutwil serves as an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen, specializing in plant biochemistry with research spanning genomics, systems biology, and computational approaches to plant science. His work bridges experimental biology and data-intensive methodologies to address fundamental questions in plant environmental responses. His research program focuses on deciphering regulatory networks in plant stress adaptation, particularly through cross-species analyses of abiotic stress mechanisms in hydroponic systems. A significant portion of his recent work involves developing computational infrastructure for plant science, exemplified by the PlantConnectome knowledge graph that integrates literature-derived biological relationships across 71,000+ plant research articles. This dual emphasis on experimental stress physiology and bioinformatics resource development positions his work at the intersection of molecular plant biology and data science. Analysis of his 2025 publications reveals a consistent trajectory toward integrative plant systems biology, combining hydroponic crop stress experiments with large-scale knowledge graph applications. These works demonstrate growing emphasis on translational bioinformatics tools that convert fragmented plant science literature into structured, queryable biological networks. Scientific Awards: No awards were documented in the provided text. Advising and Grants: The source material contains no references to graduate students, postdoctoral trainees, or research funding sources. Labs and Teams: While affiliated with the Section for Plant Biochemistry, no specific laboratory structure, team members, or collaborative consortia are described in the available information.
Rosa Ellen Lavelle-Hill serves as an Assistant Professor of Social Data Science and Psychology within the Faculty of Social Sciences at the University of Copenhagen since March 2023. Her academic appointments include postdoctoral research at the University of Tübingen (Germany) and the Alan Turing Institute (UK). She holds a PhD in "Big Data Psychology" from the University of Nottingham. PhD: Big Data Psychology, University of Nottingham Postdoc: University of Tübingen, Germany Postdoc: Alan Turing Institute, UK Her research integrates psychological theory with big data and machine learning methodologies to analyze human behavior in social good contexts. Key focus areas include sustainable development, environmental psychology, health applications, and migration studies. She specializes in Explainable AI techniques that challenge and refine existing social science theories through data-driven insights. Her methodological expertise spans longitudinal machine learning, meta-analysis of educational outcomes, and analysis of non-traditional data sources like mobile money records. Recent publications demonstrate consistent application of machine learning to educational psychology (2024-2025), migration risk assessment (2022), and consumer behavior (2020). Her work shows increasing emphasis on ethical AI applications in social science with publications in high-impact journals including Psychological Bulletin , PNAS , and Journal of Educational Psychology . Research collaborations span Tanzania, Germany, and the UK, with particular focus on urban migration risks and educational data science. Her work has been referenced in policy sources and gained significant attention across academic social media platforms including X and Bluesky.
James Gaynor Gaynor serves as an Affiliate Professor within the Department of Clinical Medicine at the University of Copenhagen, maintaining a dual institutional affiliation with the Children's Hospital of Philadelphia (CHOP) as indicated by his professional email address. His academic role bridges clinical practice and research in pediatric cardiovascular medicine. Research interests prominently feature pediatric cardiology , congenital heart defects , and fetal diagnostics , with expanding focus on neurodevelopmental outcomes in cardiac patients and machine learning applications for clinical prediction. His work integrates advanced imaging techniques, longitudinal patient studies, and computational modeling to address critical gaps in congenital heart disease management. Recent publications (2022-2024) demonstrate consistent output in high-impact journals, revealing three dominant research trajectories: 1) Fetal MRI applications for congenital heart defect diagnosis, 2) Long-term neurocognitive and academic impacts of cardiac conditions, and 3) Data-driven predictive modeling for clinical deterioration. These studies frequently involve international collaborations across European and North American institutions. No scientific awards were documented in the source material. Information regarding student supervision, grant funding, laboratory infrastructure, or research teams was not provided in the extracted content.
Ioannis Panagis serves as a Data Specialist at the iCourts Centre of Excellence for International Courts and Governance within the Faculty of Law at the University of Copenhagen. Holding a PhD in Computer Engineering from the University of Patras, he bridges computational methodologies with legal scholarship through interdisciplinary research in legal network analysis and data science applications. Education: PhD in Computer Engineering, University of Patras Research Focus: Panagis specializes in social network analysis of legal citation systems, particularly within European Union law, and develops data science techniques for legal text analysis. His work integrates computer science principles with jurisprudence to examine case law structures, judicial influence patterns, and digital platform regulation. Key methodologies include network metrics, text similarity algorithms, and cloud-based processing of legal datasets. Research Evolution: His publication trajectory (2015-2023) demonstrates progression from foundational studies on EU case law citation networks toward advanced computational approaches. Early work established frameworks for multi-dimensional legal network analysis, while recent research (2023) pioneers paragraph-level citation prediction models. This evolution reflects growing sophistication in applying machine learning and natural language processing to complex legal phenomena. Professional Engagement: As Editor of Social Network Analysis and Mining since 2020, Panagis contributes to scholarly discourse in network science. His research outputs have accumulated significant citations (notably 48 for his 2017 consumer law study) and demonstrate impact through policy references and academic discussion. He remains actively affiliated with iCourts, leveraging computational approaches to advance understanding of international judicial systems.
Jessica Xin Hjaltelin is an Associate Professor in the Department of Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, specializing in Health Data Science and AI. She also serves as a Guest Researcher at the Novo Nordisk Foundation Center for Protein Research in the Brunak Group. Her research focuses on applying artificial intelligence and data science techniques to healthcare data, particularly for cancer detection and disease trajectory analysis. Key areas include: AI-aided mining of medical records for cancer screening Disease progression modeling using population-wide health data Pancreatic cancer risk prediction and early detection Multi-cancer risk stratification using national health registries Visualization of disease trajectories from electronic health records Dr. Hjaltelin's recent work demonstrates significant impact, with publications in high-impact journals including Nature Medicine, The Lancet Oncology, and The Lancet Digital Health. Her research on pancreatic cancer has received substantial attention, with one paper picked up by 106 news outlets and referenced in Wikipedia. She collaborates extensively with researchers across multiple institutions on projects bridging data science and clinical medicine. Among her notable scientific contributions: Development of deep learning algorithms for predicting pancreatic cancer risk Creation of multi-cancer risk stratification models using national health data Analysis of pancreatic cancer symptom trajectories from registry data Research on biomarkers for treatment response in metastatic pancreatic cancer Dr. Hjaltelin maintains active collaborations with clinical researchers and participates in significant oncology studies, including the CheckPAC clinical trial investigating immunotherapy combinations for pancreatic cancer.
