جست‌وجوی استاد و پژوهشگر

Levi Waldronمشاهده پروفایل

استاد

• Data Science
• Bioinformatics
• Cancer Genomics
• +۴ مورد دیگر

Levi Waldron is a Professor and Department Chairperson in the Department of Epidemiology and Biostatistics at the CUNY Graduate School of Public Health & Health Policy. His research focuses on data science, R/Bioconductor software development, bioinformatics, cancer genomics, and human microbiome analysis. He holds a PhD in Wood Science from the University of Toronto and completed postdoctoral training at the Ontario Cancer Institute, Dana-Farber Cancer Institute, and Harvard School of Public Health. Education: PhD in Wood Science, University of Toronto MSc in Physics, University of Waterloo BSc in Physics, University of British Columbia Research Interests: Dr. Waldron’s work integrates computational methods with biomedical data to address challenges in public health. Key areas include microbiome analysis for disease biomarkers (e.g., colorectal cancer, autism spectrum disorders), development of open-source bioinformatics tools (e.g., CellMapper, AnVILWorkflow), and improving data-sharing infrastructure for genomic research. His recent studies explore longitudinal microbiome changes in cancer immunotherapy and ethical implications of metagenomic data privacy. Grants & Collaborations: As a leader in bioinformatics, he collaborates on projects like the CHASING COVID Cohort study and contributes to initiatives like the NHGRI Genomic Data Science Analysis Lab-space. His lab develops tools for multi-omics integration and cloud-based genomic analysis.

Leonardo Collado Torresمشاهده پروفایل

استادیار

• Bioinformatics
• Biostatistics
• Computational Genomics
• +۹ مورد دیگر

Leonardo Collado Torres is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and leads the R/Bioconductor-powered Team Data Science at the Lieber Institute for Brain Development (LIBD). His interdisciplinary research bridges biostatistics, bioinformatics, and neuroscience to study psychiatric disorders through high-throughput genomic technologies. PhD, Johns Hopkins Bloomberg School of Public Health, 2016 BS, National Autonomous University of Mexico (UNAM), 2009 Dr. Collado Torres specializes in the analysis of RNA-seq, single-cell/nucleus RNA-seq, and spatial transcriptomics data, with a focus on gene expression across multiple biological scales. His work emphasizes reproducibility, open science, and the development of accessible computational tools. He is a strong advocate for data science training and mentorship, organizing initiatives such as the LIBD rstats club and contributing to open-source R/Bioconductor packages like derfinder, recount, and spatialLIBD. His recent publications highlight advancements in spatial gene expression mapping in the human brain and tools for reproducible transcriptomic analysis. These works span disciplines including neuroscience, genomics, and software development, with subfields such as spatial transcriptomics, psychiatric disorder mechanisms, and open-source bioinformatics tooling. Scientific honors include the CONACyT scholarship and service on the Bioconductor Community Advisory Board and rOpenSci’s Statistical Software Peer Review advisory board. Principal Investigator, R/Bioconductor-powered Team Data Science, LIBD Co-founder, LIBD rstats club Co-founder, Community of Bioinformatics Software Developers (CDSB), Latin America Active contributor to R/Bioconductor open-source projects Dr. Collado Torres fosters a collaborative and inclusive research environment, offering data science guidance sessions and comprehensive onboarding resources for team members. His leadership promotes best practices in coding, reproducibility, and scientific communication.

• Statistical Bioinformatics
• Computational Biology
• Spatial Transcriptomics
• +۴ مورد دیگر

Dr. Shila Ghazanfar is an Australian Research Council DECRA Fellow at the University of Sydney, Faculty of Science. She is an expert in statistical and computational analysis of spatial transcriptomics and single-cell RNA-seq data, with a focus on developing bioinformatic approaches for integrating complex biological datasets across various omics modalities. Her educational background includes: Undergraduate studies in statistics and statistical bioinformatics at The University of Sydney PhD in statistics and statistical bioinformatics at The University of Sydney Royal Society Newton International Fellowship at The University of Cambridge under Dr. John Marioni in computational biology Dr. Ghazanfar's research interests center on developing statistical bioinformatic and biomedical data science approaches for meaningful integration of complex and high-dimensional biological datasets. Her multidisciplinary expertise spans statistics, statistical bioinformatics, and computational biology, enabling her to devise strategies to jointly model processes generating diverse data sources. Her work aligns with the University of Sydney Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, and Data and Decisions. Analysis of her recent publications (2021-2025) reveals strong focus areas including spatial transcriptomics infrastructure development, single-cell data integration methodologies, and applications in cancer research and cardiovascular biology. Key trends show progression from foundational method development to increasingly sophisticated multi-modal integration approaches, with significant emphasis on creating open-source computational tools for the research community. Her scientific recognition includes: Australian Research Council DECRA Fellowship Royal Society Newton International Fellowship Dr. Ghazanfar actively mentors research students, including Angel GUAN working on melanoma immunotherapy research. Her grant portfolio demonstrates substantial research support: 2023: Statistical Bioinformatics for Single Cell, Spatial and Multiomic Biotechnologies (Faculty of Science) 2022: Defining spatiotemporal mechanisms for peripheral nerve regeneration (Partnership Collaboration Award) 2022: Multiscale data integration for single cell spatial genomics (Chan Zuckerberg Initiative) Australian Research Council DECRA for statistical approaches in spatial genomics As a member of the Charles Perkins Centre, Dr. Ghazanfar participates in interdisciplinary research addressing complex health challenges through computational biology approaches, with collaborations spanning multiple domains from basic science to clinical applications.

Pedro Baldoniمشاهده پروفایل

استادیار

• Statistical genomics
• RNA-seq analysis
• Epigenomic data analysis
• +۳ مورد دیگر

Pedro Baldoni serves as an Assistant Professor in the Department of Biostatistics and Health Data Science at the University of Pittsburgh's School of Public Health. His academic journey includes a BSc and MSc in Statistics from the University of Campinas (Brazil), followed by a PhD in Biostatistics from the University of North Carolina at Chapel Hill, and postdoctoral training at the Walter and Eliza Hall Institute of Medical Research under Professor Gordon Smyth. Dr. Baldoni specializes in developing statistical methodologies for high-throughput genomic data analysis. His primary research focuses on RNA-seq transcript-level analysis , where he addresses quantification uncertainty through innovative methods like divided counts and Gibbs sampling implementations in edgeR v4. He also develops epigenomic analysis tools (epigraHMM, ZIMHMM) for detecting protein-DNA binding sites across conditions using mixed-effects hidden Markov models. Additional work includes measurement error correction in complex survey designs for epidemiological studies like the Hispanic Community Health Study. His publication trends reveal consistent contributions to Bioconductor software development, with recent articles (2019-2024) emphasizing uncertainty-aware differential expression analysis, epigenomic peak calling, and statistical modeling for genomic technologies. These works demonstrate strong integration between theoretical statistics and practical bioinformatics applications. As an educator, he teaches BIOST 2160: Data Visualization for Health Data Science , emphasizing accessibility and effective communication of health data. His methodological developments primarily serve the Bioconductor Project , with tools designed to identify molecular features changing between experimental conditions across transcriptomic, epigenomic, and proteomic datasets.

Lun-Ching Changمشاهده پروفایل

دانشیار

• Statistical Genetics
• Bioinformatics
• Computational Biology
• +۶ مورد دیگر

Dr. Lun-Ching Chang is an Associate Professor and Statistics Advisor in the Department of Mathematics and Statistics at Florida Atlantic University. He holds a Ph.D. in Biostatistics from the University of Pittsburgh (2014). His research focuses on statistical genetics, bioinformatics, and computational biology with particular emphasis on developing novel methodologies for analyzing high-throughput genomic data. Key contributions include copy number variant detection algorithms (HCMMCNVs), microbiome analysis frameworks (wiSDOM), and integrative omics approaches for cancer research. Research interests span genotype-phenotype correlations in diseases like cancer, chronic kidney disease, and neurodegenerative disorders. Collaborative work includes psychiatric studies (e.g., DA3 scale development), clinical trial methodologies, and precision medicine applications. Recent studies highlight microbiome-gut-brain interactions and molecular aging biomarkers. Publications emphasize both methodological advancements (e.g., meta-analysis pipelines) and translational research (e.g., biomarker discovery in chronic diseases). Active in software development for bioinformatics, including R packages for genomic data integration. Advises interdisciplinary projects at the intersection of statistics, computational biology, and clinical research.

Leo Lahtiمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۱۱ مورد دیگر

Leo Lahti is a Professor in Data Science at the University of Turku, Finland. His research focuses on computational analysis and modeling of complex natural and social systems, with applications in microbiome science, ecological modeling, and computational humanities. He is actively involved in open science initiatives and leads a dynamic research team. University: University of Turku Role: Professor, Data Science Contact: leo.lahti@utu.fi Lahti holds a Doctor of Science (DSc) from Aalto University (2010), where he developed probabilistic machine learning methods for life science data. He conducted postdoctoral research at EBI/Hinxton (UK), Wageningen University (NL), and VIB/KU Leuven (BE), establishing a strong international research profile. His research interests span Data Science, Artificial Intelligence, Machine Learning, Applied Statistics, Probabilistic Models, Microbial Ecology, Computational Humanities, and Open Science . He applies computational and statistical methods to understand complex systems, particularly in microbiome dynamics, ecological resilience, and historical data analysis. His work integrates interdisciplinary approaches, combining data analytics with domain-specific knowledge in biology, medicine, and humanities. The recent publications (2021–2025) reflect a strong focus on gut microbiome analysis , including maternal-infant health, dietary impacts, disease risk (CKD), and methodological advances in differential abundance and phylogenetic diversity. Other themes include ecological modeling (resilience, early warning signals), infectious disease modeling (COVID-19), and open science education (Bioconductor). The keyword trends show deep engagement with microbiology, bioinformatics, and mathematical modeling, with subfields emphasizing reproducibility, host-microbe interactions, and computational methods. Lahti plays a significant role in open science and academic leadership: Vice Chair, Open Science Finland Member, International Science Council Committee on Data (2023–2025) Member, Bioconductor Community Advisory Board Founder, Open Science Working Group, Open Knowledge Finland He is also active in teaching computational and data science, statistical programming, AI, and open science. His research homepage is available at iki.fi/Leo.Lahti, and his full publication list is accessible via ORCID and bibtex.

Anna Plaksienkoمشاهده پروفایل

پژوهشگر ارشد

• High-dimensional statistics
• Omics data integration
• Multi-omics analysis
• +۴ مورد دیگر

Dr. Anna Plaksienko is a Research Fellow in High-dimensional Statistics at the University of Oslo's Institute of Basic Medical Sciences, Faculty of Medicine. Her work focuses on statistical methods for omics data integration, particularly in multi-source high-dimensional regression and graphical models. She develops computational tools like the methyLImp2 and jewel R packages for missing value imputation and joint network estimation. Education: PhD in Mathematics (2021) - Gran Sasso Science Institute, Italy BSc in Mathematics (2016) - Novosibirsk State University, Russia Research Interests: She explores differential network estimation, stability selection, and error control in omics data (RNA-Seq, methylation, lipidomics). Her methods aim to uncover biological insights from multi-omics integration and high-dimensional datasets. Labs/Teams: She is part of the High-dimensional Statistics research group at the Institute of Basic Medical Sciences, collaborating on projects involving statistical methodology and computational biology.

• Neurodegeneration
• Oxidative Stress
• Molecular Biology
• +۳ مورد دیگر

Dr. Kasthuri Bai is a Lecturer at Monash University Malaysia's Malaysia Jeffrey Cheah School of Medical and Health Sciences, specializing in molecular neuroscience. She holds a PhD from International Medical University (2020), an MSc (2014), and a BSc in Biomedical Sciences (2000). Her research focuses on neurodegenerative diseases (e.g., Parkinson’s and Alzheimer’s), climate change impacts on disease mechanisms, and neuroprotective natural compounds like tocotrienols. She leads grants including an industry-funded study on skin health and an in vivo Parkinson’s project. She teaches courses in biomedical sciences, medical research, and physiology. Dr. Bai actively seeks postgraduate students for her climate change and neurodegeneration research. Research Interests: Neurodegeneration, oxidative stress modulation, natural phytonutrients (e.g., tocotrienols), DNA methylation, and protein expression in disease models. Her work employs bioinformatics tools (R Bioconductor, KEGG) to analyze genomic data. Key Projects: Untangling the Heat Paradox in Parkinson’s Disease (RM 30,000, Ongoing) STGAIA Skin Health Trial (RM 88,000, Ongoing) Cellular Models of Ethanol-Induced Psychosis (RM 30,000, Ongoing) Grants & Activities: Principal Investigator in multiple seed grants; collaborates with PPUKM and industry partners. Active in conferences (e.g., ASAD 2024) and peer review (Annals of Medicine). Engaged in public outreach and educational programs.

Aedin Culhaneمشاهده پروفایل

استاد

• Cancer Genomics
• Bioinformatics
• Single Cell Biology
• +۸ مورد دیگر

Aedin Culhane is a Professor of Biomedical Sciences (Cancer Genomics) at the University of Limerick School of Medicine. She leads the Limerick Digital Cancer Research Centre and co-leads the all-island eHealth-Hub for Cancer. Her expertise spans computational oncology, multi-omics data integration, and clinical bioinformatics, with over 20 years of experience in cancer genomics, including roles at the Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health in Boston, USA. Education: PhD in Cloning and Characterisation of Interleukin-18 in rat brain, University of Manchester Research Interests: Cancer computational genomics, digital health, single-cell genomics, spatial biology, tumor microenvironment, immune-oncology, and open science initiatives like Bioconductor and OHDSI. She focuses on algorithm development for integrative multi-omics data analysis and translational oncology, including tumor drug resistance mechanisms and CAR-T cell therapies. Recent Article Trends: Her work emphasizes spatial profiling of cancer microenvironments, CAR-T cell engineering, and genomic minimal datasets for precision oncology. Key themes include immune response modulation, Myc activity regulation in treatment persistence, and CRISPR-based functional genomics. Awards: Young European Research Universities Network (YERUN) Open Science Award (2023) Bioconductor Community Award (2021) Grants & Collaborations: Leads the €730,000 Chan Zuckerberg Initiative-funded eHealth-Hub for Cancer. Active in the 1+ Million Genomes Program and Genomic Data Infrastructure (GDI) projects. Collaborates internationally in spatial profiling and multi-omics integration through hackathons and open-source initiatives. Labs & Teams: Directs the Limerick Digital Cancer Research Centre, a hub for AI-driven cancer research and translational genomics. Engages with global networks like the Human Cell Atlas and National Spatial Profiling Platform.

Anestis Touloumisمشاهده پروفایل

مدرس ارشد

• Biostatistics
• Categorical Data Analysis
• High-Dimensional Statistics
• +۱۳ مورد دیگر

Anestis Touloumis is a Principal Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton, affiliated with the Computing and Mathematical Sciences Research Excellence Group. He holds a PhD in Statistics from the University of Florida and has held postdoctoral positions at the European Bioinformatics Institute and the University of Cambridge before joining Brighton in 2015. Education: PhD in Statistics, University of Florida (2006–2011) MSc in Applied Statistics, University of Piraeus (2003–2006) BSc in Mathematics, Aristotle University of Thessaloniki (1998–2003) His research centers on biostatistics, high-dimensional statistics, and statistical genetics, with a focus on developing methods for correlated responses, covariance matrix estimation, and hypothesis testing in high-dimensional settings. His work is applied in omics, health studies, and social sciences, and he actively disseminates methods through R packages on CRAN and Bioconductor. He teaches a range of statistical modules using R and SAS, emphasizing conceptual understanding and critical thinking. The recent research articles reflect strong trends in biostatistical methodology, particularly in high-dimensional inference, longitudinal and correlated data analysis, and statistical computing. There is a consistent emphasis on developing robust, open-source tools for real-world applications in healthcare and data science. Scientific Awards and Professional Service: Treasurer, British and Irish Region of the International Biometric Society (2020–present) Committee Member, British and Irish Region of the International Biometric Society (2019–present) External Examiner, MSc in Applied Statistics, Birkbeck University of London (2020–present) Member, International Biometric Society He has been active in research supervision and mentoring, with a stated interest in supervising postgraduate students in machine learning, categorical data, and high-dimensional statistics, often involving software development. He has contributed to research grants, notably as a Co-Investigator on the ERDF-funded Digital Research & Innovation Value Accelerator (DRIVA) project (2018–2021). He is involved in multiple research collaborations and has delivered invited talks and peer reviews for leading journals. His work bridges theoretical statistics and practical applications across disciplines.

Juan Ramón Gonzálezمشاهده پروفایل

استاد پژوهشی

• Bioinformatics
• Genetic Epidemiology
• Statistical Methods
• +۷ مورد دیگر

Juan Ramón González serves as a Research Professor at the Barcelona Institute for Global Health (ISGlobal), leading the Bioinformatic Group in Genetic Epidemiology (BRGE), and holds an Adjunct Professor position in the Department of Mathematics at the Autonomous University of Barcelona where he delivers graduate lectures on advanced statistical methods and bioinformatics. His academic foundation includes a Ph.D. in Biostatistics from the University of South Carolina Columbia (USA) under Prof. Edsel Peña's supervision, followed by a postdoctoral fellowship at the Center for Genomic Regulation (CRG). Dr. González pioneers statistical and computational methodologies for multi-omic data analysis (genomic, transcriptomic, proteomic) with emphasis on genomic inversions, chromosome Y regulation, and exposome integration. His research bridges bioinformatics with epidemiological applications to unravel mechanisms in complex diseases like asthma and obesity, while developing scalable tools for personalized medicine. Analysis of his recent publications reveals a dominant trend in open-source bioinformatics tool development—particularly for genomic inversion detection (scoreInvHap), exposome-wide association studies (rexposome), and toxicogenomics (CTDquerier)—showcasing methodological innovation in handling big data challenges across genetic epidemiology and environmental health domains. No specific scientific awards are documented in the source material, though his leadership in major international consortia (ATHLETE, HELIX, MEDIRAD, OPERRA) demonstrates significant recognition. He actively mentors through graduate teaching and authored the authoritative text 'Omic association analysis with R and Bioconductor'. The BRGE group he directs focuses on advancing computational frameworks for integrating genetic, environmental, and clinical data, with ongoing projects targeting exposome characterization and structural genomic variations in disease pathogenesis.

Stephanie C. Hicksمشاهده پروفایل

دانشیار

• statistics
• data science
• missing data
• +۹ مورد دیگر

Stephanie C. Hicks is an Associate Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. Her work bridges statistics, data science, and genomics, with a strong emphasis on developing computational tools for modern biological data. Research Interests: Statistical methods for genomics and single-cell data Missing data imputation and uncertainty quantification Spatial transcriptomics and multi-assay data integration Open-source software development for reproducible research Functional and epigenomic analysis Her recent research, as reflected in publications from 2025, centers on benchmarking cellular deconvolution methods, integrating spatial transcriptomics across individuals, and developing combinatorial approaches for marker gene selection. These works span high-impact journals in genomics, bioinformatics, and computational biology, indicating a strong and active research program in statistical genomics and computational biology. Scientific Contributions: Development of the HuBMAPR R package for accessing the HuBMAP data portal Leadership in benchmarking studies for cellular deconvolution Advancing methods for label-free marker gene identification Addressing reproducibility and integration challenges in spatial omics Dr. Hicks mentors students and contributes to graduate education in biostatistics, emphasizing statistical programming and workflows. She is actively involved in the development of open-source tools that enhance reproducibility and accessibility in genomic research.

• Computational Biology
• Bioinformatics
• Single-Cell Genomics
• +۳ مورد دیگر

Kevin Rue-Albrecht is a Research Fellow and Computational Biologist at the University of Oxford's MRC Weatherall Institute of Molecular Medicine (MRC WIMM) Centre for Computational Biology. He currently leads training for the Oxford Biomedical Data Science (OBDS) programme and contributes to the Sims Group's computational genomics research. His work focuses on developing bioinformatics tools for single-cell genomics and integrating multi-omics data. Previously, he held postdoctoral positions at Imperial College London and the University of Oxford's Ratcliffe group, where he analyzed genetic, proteomic, and metabolomic datasets related to renal cancer and oxygen sensing. Dr. Rue-Albrecht holds a PhD in Computational Infection Biology from University College Dublin and a Dipl. Ing. in Biological Engineering from Polytech Nice-Sophia Antipolis. His research spans functional genomics, host-pathogen interactions, and translational bioinformatics. Notable contributions include maintaining the Bioconductor package GOexpress for gene ontology analysis, developing the Panpipes multi-omics pipeline, and advancing Capture-C technology for chromosomal conformation studies. His scientific efforts emphasize computational methods for immunology, inflammatory disorders, and single-cell analysis. Collaborations include projects with the Powrie and Holländer groups exploring thymic epithelial cell biology and central tolerance mechanisms. Training initiatives at OBDS focus on equipping early-career researchers with skills in biomedical data science and open-source tool development.

Carlo Colantuoniمشاهده پروفایل

استادیار

• Developmental Neuroscience
• Systems Neuroscience
• Cognitive Neuroscience
• +۱۲ مورد دیگر

Carlo Colantuoni is an Assistant Professor in the Department of Neurology at the Johns Hopkins University School of Medicine. He is based at Johns Hopkins Hospital in Baltimore, MD, and leads a research lab focused on functional genomics and computational neuroscience. His work is affiliated with the Neuroscience Training Program and centers on understanding human brain development and disease through multi-omic data analysis. His research interests include Developmental Neuroscience , Systems and Computational Neuroscience , Neurobiology of Disease , and Functional Genomics . He develops and applies advanced computational methods such as Structured Joint Decomposition (SJD) and projectR to analyze large-scale transcriptomic datasets. Colantuoni's recent publications focus on neocortical development , stem cell variation , neuroinflammation , and host immune responses . His work integrates data from in vivo mammalian development and in vitro cerebral organoid models to identify conserved molecular dynamics and human-specific features. He has contributed to public resources like NeMO Analytics , enabling broad access to curated datasets and analytical tools. He collaborates with leading researchers such as Gabriel Santpere, Nenad Sestan, Pasko Rakic, Flora Vaccarino, and Dimitri Avramopoulos on projects involving neurodevelopmental risk genes and neuronal identity. His lab also works with applied mathematicians Don Geman and Laurent Younes on cell type marker identification using the CellCover method.

Karin Loréمشاهده پروفایل

استاد

• Vaccination Immunology
• Innate Immunity
• Vaccine Adjuvants
• +۴ مورد دیگر

Karin Loré is a Professor at Karolinska Institutet, leading the research group 'Understanding mechanisms of vaccination' within the Department of Medicine, Solna. Her work focuses on elucidating how the immune system responds to vaccines, particularly emphasizing adjuvants, mRNA technologies, and B cell responses. She holds a Doctor of Philosophy (2001) and a Licentiate degree (1999) from Karolinska Institutet, and became a Docent in 2010. Research Interests: Vaccination immunology and innate immunity Mechanisms of vaccine adjuvants and nanoparticle-based vaccines Broadening antibody responses for HIV, malaria, and respiratory viruses Immune responses in immunocompromised patients Her lab's work spans from basic immunology to translational studies, leveraging computational tools for analyzing high-dimensional immunological data. Collaborations include institutions like the NIH, CureVac AG, and Novavax. Current projects explore mRNA vaccine efficacy, adjuvant development, and immune correlates in chronic diseases. Funding sources include the Swedish Research Council, Bill & Melinda Gates Foundation, and the Knut and Alice Wallenberg Foundation. She advises numerous PhD students and postdocs, with alumni contributing to vaccine research globally. Laboratory activities include the 'Laboratory of Lymphocyte Biology' (led by Taras Kreslavskiy) and computational immunology pipelines. Her research has direct implications for developing vaccines against emerging pathogens like SARS-CoV-2 variants and neglected diseases such as malaria.