جست‌وجوی استاد و پژوهشگر

• Neurobiology
• Reproductive Biology
• Mechanobiology
• +۳ مورد دیگر

Prof. Fabian Ille is a Professor and Head of the Competence Center for Bioscience and Medical Engineering at Lucerne School of Engineering and Architecture (HSLU). He leads interdisciplinary research in biomedical engineering, space biology, and regenerative medicine. His academic background includes a Master's in Cell Biology from Heidelberg University and a PhD in Neuronal Stem Cell Biology from ETH Zurich, followed by postdoctoral work in the Space Biology Group. He combines clinical, computational, and engineering approaches to address challenges in musculoskeletal disorders and fertility treatments. Education: PhD in Neuronal Stem Cell Biology, ETH Zurich (Neuroscience Center Zurich) Master's in Cell Biology, University of Heidelberg Research Focus: Intervertebral disc and joint repair mechanisms under microgravity conditions Development of medical software solutions (CDSS, SaMD) for musculoskeletal and reproductive disorders Biomedical applications of microgravity research including space bioreactors and tissue engineering Data-driven approaches in fertility treatments and clinical decision support systems Key Projects: Space Bioreactor Development SORTHOdisc: Stem cell sorting technology for disc therapy PAEON Virtual Hospital System COSMOS Data Cockpit for personalized medicine Technical Contributions: Medical device testing methodologies (e.g., phacoemulsification instruments) Multi-omics analysis frameworks for space biology experiments AI-driven ERP data mining for service shop optimization Laboratory Infrastructure: Leads the Institute of Medical Engineering with specialized facilities for cell culture under microgravity simulation, electrophysiology labs, and biomedical software development hubs.

• Bioinformatics
• Computational Biology
• Genetics
• +۴ مورد دیگر

Magnus Fontes is an Adjunct Professor at the Department of Automatic Control within the Faculty of Engineering at Lund University , Sweden. He also contributes to the ELLIIT: The Linköping-Lund initiative on IT and mobile communication . His work bridges Computational Biology , Immunology , and Mathematical Modeling , focusing on high-dimensional biological data analysis and immune system dynamics. His research spans Genetics , Systems Biology , and Machine Learning , with recent work on: Sex-based differences in immune responses to infections Statistical methods for single-cell data analysis Viral evolution modeling Genome-wide association studies Dimensionality reduction algorithms Computational immunology frameworks Notable trends include Integration of SDGs in health research, particularly SDG3 (Good Health) and SDG10 (Reduced Inequalities). He has contributed to 36+ publications and participates in cross-disciplinary initiatives like the Engineering Health Crossroads workshop (2023). Key projects include Bioinformatics research (2013-2014) and collaborative work with the Milieu Intérieur Consortium .

Daniel Förnvikمشاهده پروفایل

دانشیار

• Breast Cancer
• Digital Breast Tomosynthesis
• Mammography
• +۷ مورد دیگر

Dr. Daniel Förnvik is an Associate Professor at the Medical Radiation Physics department of Lund University, affiliated with the Lund University Cancer Centre (LUCC). His research focuses on breast cancer imaging, particularly through digital breast tomosynthesis (DBT), mammography, and artificial intelligence applications in radiology. He actively contributes to clinical research projects and collaborates internationally on cancer detection and screening optimization. Key Research Areas: Breast cancer diagnostics, AI-driven imaging analysis, radiation physics, tumor growth modeling Projects: Malmö Breast Tomosynthesis Screening Trial, NeoDense study, AI integration in mammography His recent work explores deep learning for chemotherapy response monitoring, abbreviated MRI integration with DBT, and breast density impacts on screening accuracy. Articles show expertise in overdiagnosis prevention, image quality assessment, and multi-omics approaches combining ctDNA analysis with radiomics. As a Principal Investigator, he supervises doctoral students Johannes Olinder and Andreas Bjerkén in projects examining DBT implementation and AI applications in breast imaging. Though no explicit awards are listed, his 51+ publications and sustained 2010-2025 research output demonstrate significant contributions to medical radiation physics and oncology imaging.

Simon Magesمشاهده پروفایل

استاد پژوهشی

• Spatial Omics
• Bioinformatics
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Dr. Simon Mages serves as Group Leader at the Gene Center and Department of Biochemistry, Ludwig Maximilians University Munich (LMU), within the Faculty of Medicine. His research bridges bioinformatics, high-performance computing, and theoretical physics to develop computational frameworks for spatial omics data analysis. Previously, he held positions as Scientist at LMU (2021-2022), Visiting Scientist at the Broad Institute of MIT and Harvard (2020-present), and Research Scientist at Siemens Corporation (2019). His research focuses on the physics of high-dimensional biological data , specifically developing methods to analyze cellular dynamics in joint position-internal state spaces using spatial omics. Key areas include spatial transcriptomics, single-cell data integration, and physics-inspired algorithm development. The Mages Lab collaborates extensively with clinical researchers to translate computational insights into biological understanding, particularly in cancer progression and tissue organization. Analysis of his publication record reveals a strong trajectory from theoretical physics ( 2015-2017 lattice QCD work ) to computational biology ( 2022-present spatial omics leadership ). His recent work demonstrates expertise in algorithm development (TACCO, SlideCNA), multi-omics integration, and clinical applications in oncology. The publications consistently emphasize scalable computational frameworks and physical modeling approaches. Selected scientific awards: German Research Foundation (DFG) Research Fellowship (2020-2022) Studienstiftung des Deutschen Volkes PhD Fellowship (2012-2015) Studienstiftung des Deutschen Volkes Scholarship (2008-2011) Mages advises doctoral researchers including Antonia Eicher and collaborates with major institutions like the Broad Institute. His lab develops open-source tools (BoReMi) and participates in high-impact consortia such as the Regev Lab collaborations. Current research integrates physics-based modeling with cutting-edge spatial technologies to decode multicellular functional units in cancer and tissue organization. The Mages Lab operates within LMU's BioSysM infrastructure at Butenandtstraße 1, leveraging high-performance computing resources for large-scale biological data analysis. The group maintains strong ties with both computational physics (through prior Jülich Supercomputing Centre work) and clinical research communities.

John B. O. Mitchellمشاهده پروفایل

مدرس ارشد

• Computational Chemistry
• Machine Learning
• Quantum Chemistry
• +۴ مورد دیگر

Dr. John B. O. Mitchell is a Senior Lecturer in the School of Chemistry at the University of St Andrews , UK. His research bridges computational chemistry, bioinformatics, and toxicology, with a focus on machine learning applications in solubility prediction, enzyme catalysis, and protein-ligand interactions. PhD in Theoretical Chemistry (University of Cambridge, 1991) Readership at St Andrews since 2009 Active in Mitchell Group His research spans: Machine Learning for regression/classification in solubility, bioactivity, and toxicity Molecular Simulation of enzymes and substrates via Molecular Dynamics Quantum Chemistry (Hartree-Fock, DFT) for reaction energetics Bioinformatics of enzyme evolutionary histories and MACiE database Computational Toxicology (e.g., phospholipidosis prediction) Recent publications highlight applications in plastic-degrading enzymes , drug design , and Bayesian network modeling . Scientific awards include multiple iGEM Gold Medals (2010-2019) and a Bronze Medal (2018). Teaching roles: Electronic Structure Calculations, Scientific Writing, Statistical Mechanics Collaborations with industry in pharmaceuticals , food science , and biofuels

Laura Carrollمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Computational Biology
• Genomics
• +۶ مورد دیگر

Laura Carroll is a DDLS Fellow at Umeå University , Sweden, specializing in computational microbiology and genomic epidemiology. Her lab develops bioinformatic methods to combat bacterial pathogens through advanced data analysis. Current research focuses on phylodynamic modeling of zoonotic pathogens Development of machine learning tools for genomic phenotype prediction Creation of multi-omics methods to assess pathogen virulence Her work bridges computational and experimental microbiology, emphasizing data accessibility and public health applications through teaching and outreach. Recent publications highlight expertise in antimicrobial resistance , taxonomic classification , and outbreak detection using genomic data. Scientific Awards: DDLS Fellowship Her research combines genomic epidemiology , machine learning , and multi-omics approaches to enhance pathogen surveillance and risk evaluation systems.

Beate Vestadمشاهده پروفایل

پژوهشگر ارشد

• Gut Microbiome
• Cardiovascular Disease
• HIV
• +۶ مورد دیگر

Beate Vestad is a Postdoctoral Fellow at the Department of Transplantation Medicine , University of Oslo (UiO), with a focus on microbiome research and extracellular vesicle analysis in complex disease contexts. She actively contributes to the Genomics and Metagenomics in Inflammatory Disorders and Oslo HIV Research Network groups. Her research interests center on the intersection of gut microbiota alterations , systemic inflammation , and chronic disease progression in conditions like HIV , cardiovascular disorders , and post-COVID-19 complications . She explores how microbial metabolites (e.g., imidazole propionate, butyric acid) and vesicle-mediated signaling influence disease severity and patient outcomes. Her publications demonstrate expertise in multi-omics integration , EV isolation methodologies , and clinical microbiome studies , with collaborations across immunology , metabolic medicine , and cancer biology . Her work bridges fundamental microbiome science with translational clinical applications , particularly in high-risk patient populations .

• Computational Biology
• Biomedical Informatics
• Cancer Research
• +۴ مورد دیگر

Mitro Samuli Miihkinen is a Postdoctoral Researcher and Supervisor in the Doctoral Programme in Integrative Life Science at the University of Helsinki, affiliated with the Finnish Institute of Molecular Medicine. His research spans computational biology, biomedical informatics, and cancer research with a focus on drug repurposing, spatial transcriptomics, and statistical modeling. Institution: University of Helsinki Collaborations: Active in iCAN - Individualized Cancer Medicine Finland flagship program His recent publications highlight interdisciplinary approaches combining mathematical modeling , bioinformatics , and molecular medicine . Key contributions include developing tools for drug repurposing ( RepurposeDrugs ) and spatial cell type identification ( ScType ). Research activities from 2022-2025 show engagement in computational oncology, with 6 publications and participation in 2 projects. He presented at the iCAN retreat 2024 conference as a speaker.

Anna Mae Diehlمشاهده پروفایل

استاد

• Metabolic Liver Disease
• Hepatology
• Liver Pathogenesis
• +۲ مورد دیگر

Anna Mae Diehl, MD, is the Florence McAlister Distinguished Professor of Medicine at Duke University School of Medicine. She specializes in metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), and alcoholic liver disease. Her research employs a bedside-to-bench-to-bedside approach to understand pathogenesis mechanisms such as innate immune dysregulation, microbiome abnormalities, and cell death mediators. Key Achievements: Identified Hedgehog signaling's role in liver fibrogenesis and NASH/MASH progression Leveraged 'omics' technologies to subclassify MASLD patient cohorts Contributed to multi-center clinical trials for MASLD therapies Scientific Awards: Florence McAlister Distinguished Professor of Medicine

Qing Luمشاهده پروفایل

استاد مدعو

• Statistical Genetics
• Machine Learning
• U-Statistics
• +۳ مورد دیگر

Dr. Qing Lu is an Adjunct Professor at the BioMolecular Science Gateway Faculty of Michigan State University, affiliated with the Genetics & Genome Sciences Program. Their methodological research focuses on statistical genetics and machine learning innovations for high-dimensional data analysis, including tree-based methods, U-statistics, and deep learning frameworks. Key research trends from publications include: Statistical genetics methodology (U-statistics, kernel neural networks, mixed-effects models) Machine learning applications in genomic data analysis (deep learning, transfer learning, functional networks) Environmental health investigations (bisphenols, metals, parabens) Public health methodologies (network scale-up, population estimation) Dr. Lu's work bridges computational methods with biomedical applications, particularly in: Genetic interaction analysis Multi-omics data integration Exposure-genotype-phenotype relationships Development of open-source bioinformatics tools

• Computational Biology
• Systems Biology
• Mathematical Modeling
• +۲ مورد دیگر

Prof. Dr. Jan Hasenauer holds the Hertz Professorship at the University of Bonn since August 2024, with dual affiliations to the Life & Medical Sciences Institute (LIMES) and the Hausdorff Center for Mathematics (HCM) . His work bridges computational biology , systems biology , and mathematical modeling , focusing on methods for integrating heterogeneous biological data , model-based hypothesis testing , and experimental design optimization . Develops computational frameworks for analyzing complex biological systems Active in multi-scale modeling of cellular and organismal processes Key contributor to universal differential equations and Bayesian inference tools Research Trends from 2024-2025 publications show expertise in: Integrating clinical data with multi-omics for disease modeling Designing privacy-preserving analysis tools for clinical trials Developing software standards like PEtab for reproducible modeling Applying machine learning to epidemiological forecasting The group maintains strong connections with the Transdisciplinary Research Area 'Modelling' and collaborates across mathematics , life sciences , and public health . Recent ERC Starting Grant funding (€1.5M) supports their innovative computational approaches.

Matt Padulaمشاهده پروفایل

دانشیار

• Proteomics
• Lipidomics
• Metabolomics
• +۴ مورد دیگر

Associate Professor Matt Padula is a faculty member in the School of Life Sciences at the University of Technology Sydney (UTS), where he also directs the Proteomics Core Facility. His research focuses on advanced proteomic, lipidomic, and metabolomic methodologies, with particular emphasis on developing sample preparation workflows and analyzing complex biological systems. Padula has 25 years of experience in protein chemistry, mass spectrometry, and the application of these techniques to diverse organisms including parasites, bacteria, and human cells. Research Interests : Padula's work spans proteomics innovation, platelet biology, and the molecular mechanisms of disease. Recent studies include investigating RPS4Y1's role in asthma inflammation, optimizing multi-omics workflows, and understanding lipid changes in stored platelets. His team is also advancing forensic proteomics for body fluid identification from post-DNA extraction waste. Key Projects : The Proteomics Core Facility provides cutting-edge instrumentation and expertise for researchers. Padula collaborates on projects ranging from coral restoration lipidomics to the development of therapeutic peptides for cardiovascular diseases. His lab's work bridges basic science with translational applications in healthcare and environmental conservation. Awards & Recognition : No specific awards are listed, though his contributions to methodological advancements have been highlighted in over 179 publications. Padula is actively involved in supervising postgraduate students and mentoring researchers in omics technologies.

Martin Vingronمشاهده پروفایل

استاد

• Computational Molecular Biology
• Functional Genomics
• Gene Regulation
• +۳ مورد دیگر

Prof. Martin Vingron is a Scientific Member and Director at the Max Planck Institute for Molecular Genetics. He holds an adjunct professorship at Freie Universität Berlin. His research focuses on computational molecular biology, integrating algorithmic and statistical methods to study gene regulation, epigenetics, and functional genomics. Education: Studied mathematics in Vienna, PhD (1991, Heidelberg University) under EMBL supervision. Postdocs at UCLA and Bonn University. Research: Current work emphasizes the interplay between epigenetic modifications and gene regulation. Develops methods for analyzing genomic sequences and functional genomics data. Leads the Vingron Lab, collaborating on transcriptional regulation and epigenetic studies. Awards: Max Planck Research Prize (2004), Leopoldina Membership, ISCB Fellowship. Labs/Teams: Heads the Transcriptional Regulation Group at MPIMG, focusing on computational methods for gene regulation analysis and data integration.

• Epidemiology
• Genetics
• Medical Biochemistry
• +۳ مورد دیگر

Dr. Olga Chervova is a Senior Research Fellow at University College London's Department of Epidemiology & Public Health, specializing in epigenetics and its applications to human health. Her work bridges pure mathematics and clinical sciences, with a focus on cardiovascular medicine, genetics, and oncology. Education: PhD in Pure Mathematics (University College London, 2012); MSc in Clinical Sciences (Saratov State University, 2006). Research interests include epigenetic mechanisms in disease, particularly DNA methylation's role in cancer evolution, cardiovascular conditions, and age-related pathologies. She has pioneered studies on epigenetic clocks, linking accelerated aging metrics to diseases like sarcoma and myocardial infarction. Her work also addresses global health challenges, such as studying war exposure effects in Syrian refugees through epigenetic biomarkers. Key research trends in her publications include integrating multi-omics data (genomic, transcriptomic, epigenetic) for precision medicine, developing diagnostic classifiers using epigenetic signatures, and applying machine learning to interpret clinical time-series data. Her studies often emphasize longitudinal analyses and population-based cohorts. Labs/Teams: Co-founder of the Personal Genome Project-UK (PGP-UK), an open-access multi-omics resource supporting personalized medicine research. She collaborates with international teams on lung cancer evolution and epigenetic biomarker development.

Jun Songمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Epigenetics
• +۳ مورد دیگر

Jun Song is a Professor affiliated with multiple departments at the University of Illinois at Urbana-Champaign, including the Department of Statistics, Department of Physics, Carl R. Woese Institute for Genomic Biology, and holds the title of Founder Professor in Physics. His research focuses on computational biology, statistical genetics, and systems biology, with emphasis on understanding regulatory genomics and epigenetic mechanisms. Song leads a research group developing innovative tools for analyzing genomic data, such as CHANCE, NSeq, and ClusterEnG. His work includes studies on chromatin structure, CRISPR prime editing efficiency, cancer-associated genetic variants, and the molecular basis of schwannoma development. He has contributed to high-impact journals in computational biology and genetics. Song’s lab actively investigates barriers to induced pluripotent stem cell (iPSC) generation through genome-wide screens and functional genomics approaches. Key research themes involve integrating multi-omics data, modeling epigenetic evolution in cancer, and developing statistical methods for interpreting regulatory variation. His collaborative projects span epigenetics, cancer biology, and computational tool development for genomics research.