Find Academic Supervisors

• Bioinformatics
• Computational Biology
• Genetics
• +4 more

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 FörnvikView profile

Associate Professor

• Breast Cancer
• Digital Breast Tomosynthesis
• Mammography
• +7 more

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 MagesView profile

Research Professor

• Spatial Omics
• Bioinformatics
• High-Dimensional Data Analysis
• +3 more

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. MitchellView profile

Senior Lecturer

• Computational Chemistry
• Machine Learning
• Quantum Chemistry
• +4 more

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 CarrollView profile

Research Fellow

• Bioinformatics
• Computational Biology
• Genomics
• +6 more

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 VestadView profile

Research Fellow

• Gut Microbiome
• Cardiovascular Disease
• HIV
• +6 more

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 .

Matt PadulaView profile

Associate Professor

• Proteomics
• Lipidomics
• Metabolomics
• +4 more

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 VingronView profile

Professor

• Computational Molecular Biology
• Functional Genomics
• Gene Regulation
• +3 more

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
• +3 more

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 SongView profile

Professor

• Computational Biology
• Genomics
• Epigenetics
• +3 more

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.

Mariola FerraroView profile

Associate Professor

• Bacterial Infections
• Extracellular Vesicles
• Ubiquitin Pathways
• +4 more

Mariola Ferraro is an Associate Professor in the Department of Microbiology & Cell Science at the University of Florida. Her research focuses on microbial pathogenesis, extracellular vesicles, and host defense mechanisms. She investigates how pathogens such as Salmonella , Staphylococcus aureus , and Yersinia interact with host cells, particularly through ubiquitin pathways and extracellular vesicle-mediated communication. Her work integrates proteomics, immunology, and space biology to study bacterial adaptation under stress conditions like microgravity. Recent studies include the impact of simulated microgravity on Enterobacter cloacae proteomes and the role of cannabinoid receptors in modulating immune responses during infections. Ferraro’s research also explores novel vaccine strategies using extracellular vesicles isolated from wastewater-derived Salmonella strains. Her interdisciplinary approach bridges fundamental biology with translational applications, including drug development targeting deubiquitinating enzymes (DUBs) and exosome-based therapies for neurological disorders. Publications highlight her expertise in multi-omics data analysis, lipidomic profiling, and the development of standardized protocols for extracellular vesicle research (MISEV2023). Collaborations span microbiology, immunology, and space science, reflecting her commitment to advancing understanding of pathogen-host interactions in diverse environments.

Li MaView profile

Professor

• Statistical Genetics
• Population Genetics
• Genomic Selection
• +5 more

Dr. Li Ma is a Professor in the Department of Animal & Avian Sciences at the University of Maryland, College Park. His research focuses on statistical genetics, population genetics, and genomic selection in livestock, particularly dairy cattle. He develops computational tools to enhance genetic studies using next-generation sequencing data, with applications in improving livestock productivity and disease resistance. His work includes analyzing recombination patterns and PRDM9 alleles in dairy cattle breeding, genomic selection strategies using large-scale datasets, and integrating multi-omic data for precision livestock breeding. He also explores big data analytics in agricultural genomics. Notable research areas include sequence-based genomic discovery, genetic architecture of complex traits, and applying machine learning to genetic studies. His recent articles (2024–2025) show interdisciplinary work in financial econometrics, addressing topics like risk management, stochastic volatility modeling, and option pricing strategies. This dual focus suggests expertise spanning both biological and quantitative finance domains. Dr. Ma collaborates on projects involving genomic databases, statistical method development, and computational biology tools. His work bridges genetic research and advanced financial modeling, though specific student advisement details are not listed here.

Tanja LaskeView profile

Researcher

• Systems Medicine
• Computational Biology
• Virology
• +3 more

Dr. Tanja Laske is a Group Leader at the Cosy.Bio lab within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. Her research focuses on computational systems biology, integrating multi-omics data with mechanistic models and machine learning to address challenges in systems medicine. She holds a PhD in Bioprocess Engineering from the Max Planck Institute for Dynamics of Complex Technical Systems and a Master’s in Biosystems Engineering from Otto von Guericke University Magdeburg. Her work includes experimental studies on adenoviral vector production and mathematical modeling of virus replication dynamics. Her research interests span systems biology, virology, and bioprocess optimization. Key projects include analyzing defective interfering particles in influenza viruses, modeling virus-host interactions, and developing algorithms for differential co-expression analysis (e.g., DRaCOon). She also explores metabolic dysregulation in diabetic bone regeneration and proteomic biomarker discovery for bone-related diseases. Recent work emphasizes computational methods like FedProt for federated proteomic analysis and ProHarMeD for drug repurposing. Her publications highlight contributions to normalization techniques in proteomics, patient stratification via UnPaSt, and mechanistic modeling of viral dynamics. No scientific awards or grants are explicitly listed. She advises no students, though her group contributes to collaborative research projects. Her lab, Cosy.Bio, focuses on systems medicine applications and interdisciplinary approaches to biomedical challenges.

Silke Van ElferenView profile

Researcher

• Biomarkers
• Cardiomyopathies
• DNA Methylation Analysis
• +3 more

Silke Van Elferen is a Research Associate at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, within the Computational Systems Biology department. She is concurrently completing a Master's degree in Drug Development and Neurohealth at Maastricht University. Her research focuses on biomarker identification in cardiomyopathies and federated DNA methylation data analysis, conducted under collaborations with CoSy.Bio. Education: BSc (Hons) in Forensic Science, Anglia Ruskin University MSc in Systems Biology, Maastricht University (thesis: federated DNA methylation workflow implementation) Research Interests: Silke specializes in translational bioinformatics, particularly in developing computational frameworks for multi-omics data integration. Her current work targets tissue-specific and blood-based biomarkers for cardiomyopathies, leveraging federated learning approaches to enhance data privacy in genomic studies. She also explores applications of systems biology in drug development pathways. Labs/Teams: Active contributor to CoSy.Bio, collaborating with Dr. Olga Tsoy and Lena Hackl on thesis projects. Previously mentored by Dr. Olga Zolotareva during her MSc thesis.

• Pain Management
• Sleep Disorders
• Machine Learning in Healthcare
• +5 more

Carl Saab is an Adjunct Professor of Engineering at Brown University's School of Engineering. His research focuses on neurophysiological mechanisms of pain and sleep, leveraging machine learning, quantum computing, and advanced neuroimaging techniques. He explores biomarker discovery for chronic conditions like neuropathic pain and migraine, and develops innovative treatments such as spinal cord stimulation protocols. Education details are not explicitly provided in the text, but his extensive publication record indicates expertise in neuroscience, biomedical engineering, and computational biology. His work bridges clinical applications and technological innovation, addressing challenges in pain management, sleep medicine, and neurological disorders. Research interests include: Neuropathic pain mechanisms and therapies Sleep-disordered breathing and cardiovascular risk stratification Machine learning in biomarker identification Quantum computing's role in multi-factorial disease analysis EEG/EMG-based pain assessment systems Recent publications emphasize foundational models for clinical decision-making, spinal cord stimulation efficacy, and migraine neurophysiology. Though awards are not listed, his prolific output suggests significant contributions to translational neuroscience. Advising and grant details are not specified, but his work likely involves interdisciplinary collaborations across engineering and medical fields.