David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.
Jakob Blæsbjerg Hoof is an Associate Professor in the Department of Biotechnology and Biomedicine at the Technical University of Denmark. His research focuses on synthetic biology, fungal genetics, and enzyme biochemistry, with contributions to understanding fungal secondary metabolites and microbial engineering. Research domains include: Genetic manipulation of filamentous fungi Metabolic pathway engineering Protein biochemistry and enzyme mechanisms Genomics and comparative biology Bioinformatics and AI applications His recent publications focus on fungal genomics, deep learning applications in microbiology, and enzyme characterization. Research primarily involves Aspergillus species and yeast metabolic engineering. Current PhD supervisions include projects on: Precision fermentation optimization Fungal gene cataloging for disease research Microbial enzyme production Secondary metabolite evolution Metabolic pathway modulation
Andreas Hougaard Laustsen-Kiel is Professor at DTU's Department of Biotechnology and Biomedicine, leading research in biologics engineering with focus on antibody technologies and recombinant antivenoms. His work bridges protein engineering, toxin neutralization, and diagnostic applications. Research themes include: Recombinant antivenom development Snake venom biochemistry Antibody engineering platforms Therapeutic protein design Diagnostic immunoassays Recent research advances include computational protein design for toxin neutralization, oral therapeutics for diarrheal diseases, and innovative diagnostic platforms. Publications demonstrate transdisciplinary approaches spanning structural biology, AI-assisted design, and translational applications. Research group activities include: PhD supervision on nanobody engineering and allergen characterization International collaborations on neglected tropical diseases Development of antibody-based solutions for human and animal health
Timothy Patrick Jenkins is Associate Professor at DTU's Department of Biotechnology and Biomedicine, specializing in computational approaches to protein design and proteomics. His research integrates machine learning with experimental biology for biotherapeutic development. Research domains include: AI-driven protein design De novo peptide sequencing algorithms Computational structural biology Venom proteome analysis Directed evolution Recent publications demonstrate innovations in diffusion-based peptide sequencing, plasmid engineering techniques, and computational toxin analysis. Research contributes to therapeutic antibody design and proteomics methodology advancement. Supervision portfolio includes PhD projects on: Deep immune receptor modeling AI-driven protein binder design Poly-specific peptide engineering Nanobody optimization Analytical tool compound development
Jeppe Kari is an Associate Professor in the Department of Science and Environment Chemistry at Roskilde University. His research focuses on biophysical chemistry, particularly biocatalysis and interfacial enzyme kinetics. He aims to bridge inorganic heterogeneous catalysis and biocatalysis to advance heterogeneous biocatalysis as a field. His work emphasizes understanding enzyme behavior at solid-liquid interfaces and optimizing enzyme systems for industrial applications. Key research areas include enzymatic hydrolysis of cellulose, Sabatier principle applications, and computational methods for predicting enzyme activity. Collaborations span international institutions, as indicated by his network of external research partners. His publications highlight advancements in enzyme kinetics modeling, substrate interactions, and enzyme engineering. Notable contributions include studies on cellulase efficiency limitations, ITC analysis of polydisperse systems, and virtual bioprospecting of interfacial enzymes. Kari’s lab integrates computational and experimental approaches to address challenges in biocatalytic processes. Education: Holds an MSc and PhD in relevant fields (specific institutions not detailed in text). No awards or grants explicitly listed. Advises on projects related to cellulase optimization and enzyme kinetics. Maintains a research website at www.jeppekari.com .
Andres Masegosa is an Associate Professor at the Department of Computer Science, Aalborg University (AAU), within The Technical Faculty of IT and Design. He is actively involved in the DarkScience project (2022–present), focusing on metagenomic binning and microbial dark matter analysis. His research interests span Bayesian networks, machine learning, probabilistic graphical models, and educational methodologies in computer science instruction. Key research contributions include advancements in PAC-Bayes theory, genome representation learning, and cold posterior effects in Bayesian models. He has published extensively in top venues like Advances in Neural Information Processing Systems and Transactions on Machine Learning Research. His work often bridges theoretical contributions with practical applications in genomics and education. Masegosa leads the development of tools like InferPy for probabilistic modeling and has contributed to open-source projects such as the AMIDST toolbox. His educational research explores learning styles and active learning strategies, emphasizing live coding and programming exercises. Collaborations include interdisciplinary projects with microbiologists and data scientists, reflecting his expertise in computational methods for complex biological systems. His research portfolio demonstrates a strong focus on scalable probabilistic methods and their real-world applications.
Vladimir Gorshkov is a Lab Manager at the Department of Biochemistry and Molecular Biology, University of Southern Denmark, specializing in Biomedical Mass Spectrometry and Systems Biology . His research focuses on Proteomics , Mass Spectrometry , and Posttranslational Modification , with applications in neurodegenerative diseases, cancer therapy, and cultural heritage analysis. Lab Manager, Biomedical Mass Spectrometry and Systems Biology, SDU 46+ publications spanning 2010–2025 Research Trends : His recent work emphasizes ultra-fast proteomic analysis , MS/MS-free quantification , and neuroprotection mechanisms in Parkinson’s and chemotherapy-induced neuropathy. Collaborations span Denmark , Brazil , and International Partners . Scientific Activities : Presented at the 2023 EuBIC-MS conference on Negative Ion Mode Proteomics .
Michael Broberg Palmgren is a Professor in Plant Physiology at the Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen. His research focuses on biological pumps in plants, particularly P-type ATPases, and their roles in growth, nutrient uptake, stress tolerance, and signal transduction. He leads the Section for Transport Biology and has been instrumental in translating knowledge from model plants like Arabidopsis thaliana to crop species including barley and quinoa. His research interests include accelerating the domestication of orphan crops and wild plants for sustainable agriculture, perennial wheatgrass development, quinoa breeding for improved drought and salinity tolerance, and investigating why plants lack sodium pumps and whether they would benefit from having them. His work addresses urgent agricultural challenges including reducing fertilizer and water use while preserving natural ecosystems. His recent publications (2024-2025) demonstrate continued research activity across plant membrane biology, crop domestication, and sustainable agriculture. These works explore chloroplast transport mechanisms, in vitro plant transformation techniques, gene family identification methods, calcium pump regulation, lipid flippase functions, and innovative approaches to quinoa improvement through saponin biosynthesis control. 2017 Scandinavian Plant Physiology Society (SPPS) Award 2016 Communicator of the Year, University of Copenhagen 2016 Knight of the Order of Dannebrog 2012 Best Teacher of the Year, Faculty of Life Sciences 2009 Elected member of the Academy for Technical Sciences, Denmark 2008 Elected member of Faculty of 1000 2005 Lifelong right to inhabit the Knud Sand Honorary Residence 2000 Elected member of the Royal Danish Academy of Sciences and Letters Professor Palmgren has supervised over 22 master's students and 22 PhD students throughout his career, with more than half of these supervisions occurring since 2006. He has served as opponent at over 10 PhD examinations outside the University of Copenhagen. His research is supported by major grants including the Novo Nordisk Foundation project 'NovoCrops' (2020-2026), Carlsberg Foundation's 'RaisingQuinoa' (2019-2024), and Innovation Fund Denmark's 'LESSISMORE' (2017-2020). He maintains an active presence in the scientific community through his leadership of the TRAP research group (http://www.traplabs.dk), editorial roles including Co-editor for The Plant Cell, and extensive public outreach with over 57 media appearances since 2014 discussing plant biology in Danish and international media.
Christer S. Ejsing is an Associate Professor and Principal Investigator at the Department of Biochemistry and Molecular Biology, University of Southern Denmark (SDU). He leads a research group dedicated to advancing mass spectrometry-based lipidomics and proteomics for systems-level understanding of lipid metabolism in health and disease. His research focuses on lipidomics, proteomics, mass spectrometry, systems biology, and lipid metabolism . His group integrates cutting-edge technologies to study lipid homeostasis across model organisms, with particular interest in organelle interactions, metabolic regulation, and diurnal dynamics. He is a key contributor to the standardization of lipidomics data reporting and nomenclature, promoting transparency and reproducibility in the field. The publication record shows a strong trend in developing and applying advanced mass spectrometry workflows, with recent work emphasizing data standardization, phosphoproteomic regulation of lipid synthesis, and inter-organ lipid crosstalk. His work spans fundamental biophysical processes like membrane curvature sensing to systemic metabolic regulation. Christer Ejsing has no listed scientific awards in the provided text. He is actively involved in research leadership and collaboration, supervising research projects and contributing to major international initiatives such as the LIPID MAPS consortium and the Lipidomics Standards Initiative. His group's work is supported by ongoing research grants, though specific funding sources are not detailed here. He leads the research group hosted at www.msLipidomics.info , which serves as a hub for lipidomics methodology development and application.
Søren Hauberg is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the Cognitive Systems group. His research lies at the intersection of machine learning, geometry, and probabilistic modeling, with applications in life sciences and computer vision. Department: Applied Mathematics and Computer Science Research Group: Cognitive Systems Institution: Technical University of Denmark (DTU) His research interests center on geometric deep learning , Bayesian inference , and manifold-based modeling . He explores how stochastic and geometric structures can improve machine learning models, particularly in modeling complex data such as biological sequences and medical images. His work integrates Riemannian geometry, Gaussian processes, and energy-based models to build more robust and interpretable AI systems. The recent publications reveal a strong trend toward geometric representation learning , fairness in generative models , and computational methods for biological data . His team investigates latent space geometries, develops efficient GPU-based numerical algorithms, and applies foundational models to protein sequences. There is a clear emphasis on both theoretical rigor and practical implementation. Søren Hauberg actively supervises multiple PhD students and leads key projects in machine learning for life sciences. He is a project manager in the Center for Basic Machine Learning Research in Life Science , indicating leadership in interdisciplinary research. He also contributes to high-performance computing applications in statistical modeling. He is involved in several active research projects, including: Geometric Bayesian Deep Learning Stochastic Riemannian Geometry with Applications to Data Modelling The geometry of protein representations AI-driven Electron Tracking for High-Energy Radiation Detection
Simon Shaw is a Researcher at the Novo Nordisk Foundation Center for Biosustainability , Technical University of Denmark. His work focuses on genome mining and bioinformatics tool development for microbial genetics. Research Interests : Computational analysis of gene clusters , antiSMASH database expansion, phylogenetic tree generation , and robotic automation in Streptomyces conjugation. Key Contributions : Co-author on 11 peer-reviewed publications including major updates to the antiSMASH database (versions 6.0–7.0), ActinoMation automation framework, and getphylo phylogenetic tool. Collaborative Activities : Organized the 2018 antiSMASH Hackathon and participated in the 4th antiSMASH Hackathon (2017) .
Henrik Nielsen is an Associate Professor at the Department of Bioinformatics, Technical University of Denmark, specializing in protein sorting prediction and signal peptide analysis. His research leverages deep learning and protein language models to advance subcellular localization and secretion pathway studies. Department of Bioinformatics, DTU Focus on signal peptides, protein targeting, and computational methods His recent work includes SignalP 6.0 , DeepLoc 2.1 , and SpanSeq , highlighting the integration of protein language models for multi-label localization prediction and sequence data splitting. Collaborations span projects in pathogenic eukaryotes, vaccine development, and structural bioinformatics. Notable contributions include: Development of SignalP: a cornerstone tool for signal peptide cleavage prediction Creation of DeepLoc for membrane protein type classification Advancing SpanSeq to optimize deep learning model assessment He has supervised PhD candidates in protein sorting, bioinformatics, and sequence analysis, with a focus on improving vaccine design and pathogen characterization through computational approaches.
Nadja Møbjerg is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in animal anatomy, physiology, and evolutionary biology. Her work focuses on tardigrades (water bears) and their ability to survive extreme environments through cryptobiosis, emphasizing osmolyte and water regulation. She leads a research group established in 2008 and has secured ~2.15 million EUR in funding. Education: PhD in Animal Physiology (2002, August Krogh Institute) M.Sc. in Cell Biology and Anatomy (1997, University of Copenhagen) Research Interests: Nadja explores tardigrade adaptations to desiccation, freezing, and salinity fluctuations using advanced microscopy, genomics, and bioinformatics. Her work intersects with molecular physiology, evolutionary morphology, and stress tolerance mechanisms. Article Trends: Recent publications highlight tardigrade thermotolerance limits, quantum entanglement studies, and environmental DNA techniques for biodiversity assessment. Themes span molecular physiology, cryobiology, and interdisciplinary physics-biology interfaces. Scientific Awards: WoRMS Top-Ten Marine Species Nomination (2021) Schibbye'ske Præmie (2001) Carlsberg Foundation Fellowship (2003-2004) Danish Natural Science Research Council Fellowship (2002-2003) Academic Leadership: She has mentored over 90 students at all levels, coordinated courses like Zoophysiology and Comparative Anatomy, and served on international teaching committees. She evaluates PhD theses across European universities and contributes to editorial boards and ethics committees.
Henrik Bulskov is an Associate Professor in the Department of People and Technology (Programming, Logic and Intelligent Systems) at Roskilde University. He holds an MSc and PhD. His work focuses on natural logic systems, database querying, and ontology-based information retrieval. He has contributed to projects such as SEAFACTS (digital maritime history platform) and NorDigHealth (regional digital health solutions). Education: MSc and PhD (specific disciplines not explicitly stated) Research interests include formal logic systems integration with databases, machine learning applications in bioinformatics, and semantic summarization through ontologies. Recent work explores query optimization in natural logic knowledge bases and disparity analysis in neural networks. His publications highlight advancements in computational logic for knowledge management and biomedical text analysis. Bulskov has participated in international conferences and contributed to media discussions on big data applications. Advising: No explicit student advisees listed Grants: Principal/Co-investigator in multiple projects including SIABO (2007–2012) and Duuoo Analysis (2021) Labs/Teams: Involved in interdisciplinary teams focusing on bioinformatics, health tech, and digital humanities through collaborative projects.
Pablo Cruz-Morales serves as a Senior Researcher at the Novo Nordisk Foundation Center for Biosustainability, part of the Technical University of Denmark (DTU), where he leads the Yeast Natural Products research group within the DTU Microbes Initiative. His work contributes to multiple UN Sustainable Development Goals through sustainable bioproduction research. His research expertise spans natural product discovery, biosynthetic gene cluster analysis, and Streptomyces genomics. Cruz-Morales specializes in evolutionary analysis of biosynthetic pathways, synthetic biology applications for natural product enhancement, and computational methods for genome mining. His fingerprint reveals strong focus areas in Gene Clusters (100%), Streptomyces (62%), Natural Products (60%), and Biosynthetic Gene Clusters (32%). Cruz-Morales' publication record demonstrates significant contributions to microbial biotechnology, with recent work spanning phylogenetics, mycoparasitism mechanisms, edible mycelium engineering, and co-evolutionary analysis of biosynthetic pathways. His research bridges computational genomics with practical applications in sustainable production systems. Cruz-Morales actively supervises five PhD students across diverse projects including yeast-based biosensors, structural variant calling in centromeres, computational graph methods for natural product discovery, Streptomyces genome mining, and synthetic biology applications for smart farming. His collaborative network spans multiple institutions with significant international engagement. As part of the Novo Nordisk Foundation Center for Biosustainability, Cruz-Morales contributes to cutting-edge research in sustainable bioproduction through the integration of computational genomics, synthetic biology, and microbial engineering approaches.
