Invited presentations

  • University of Southern California, Division of Biostatistics, Seminar Series. (2023). nnSVG for the identification of spatially variable genes and unsupervised analyses of spatial transcriptomics data.

  • Columbia University, Department of Biostatistics, Statistical Genomics and Genetics Seminar Series. (Virtual). (2023). nnSVG for the identification of spatially variable genes and unsupervised analyses of spatial transcriptomics data.

  • Boston University, Chobanian and Avedisian School of Medicine, Section on Computational Biomedicine, Spatial Biology Seminar Series. (Virtual). (2023). nnSVG for the identification of spatially variable genes and unsupervised analyses of spatial transcriptomics data.

  • Joint Statistical Meetings (JSM) 2023, Toronto, Canada. (2023). nnSVG for preprocessing, feature selection, and quality control in multi-sample spot-based spatially-resolved transcriptomics data.

  • Statistical Methods in Imaging Conference 2023, Annual Meeting of the ASA Statistics in Imaging Section, Minneapolis, MN, United States. (2023). nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.

  • Emerging Leaders in Computational Oncology 2023, Memorial Sloan Kettering Cancer Center, New York, NY, United States. (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • European Conference on Computational Biology (ECCB), Workshop NTB-W04. (Virtual). (2022). Scalable identification of spatially variable genes with nnSVG and Bioconductor.

  • R/Medicine. (Virtual). (2022). Unsupervised analyses of spatially-resolved transcriptomics data with nnSVG and R/Bioconductor.

  • Sydney Bioinformatics Seminar Series, Sydney Precision Bioinformatics Alliance, University of Sydney, Australia. (Virtual). (2022). nnSVG: scalable identification of spatially variable genes in spatially-resolved transcriptomics data.

  • ENAR (Eastern North American Region of the International Biometric Society) Spring Meeting, Houston, TX, United States. (2022). nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.

  • ICCABS 2018 (IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences): 1st Workshop on Computational Advances for Single-Cell Omics Data Analysis (CASCODA), Las Vegas, NV, United States. (2018). Methods, tools, and resources for differential discovery in high-dimensional cytometry data.

  • 1st Swiss Cytometry Meeting, Lausanne, Switzerland. (2018). Statistical methods for differential discovery in high-dimensional cytometry data.

  • European Bioconductor Meeting 2017, Cambridge, United Kingdom. (2017). Statistical methods for differential discovery in high-dimensional cytometry data.


Other presentations

  • Boston University, School of Public Health, Department of Biostatistics, Boston, MA, United States. (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • Ohio State University, College of Medicine, Department of Biomedical Informatics, Columbus, OH, United States. (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • University of Colorado, School of Medicine, Department of Biomedical Informatics. (Virtual). (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • Emory University, Rollins School of Public Health, Department of Biostatistics and Bioinformatics, Atlanta, GA, United States. (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • University of Utah, School of Medicine, Department of Human Genetics, Salt Lake City, UT, United States. (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • University of Minnesota, School of Public Health, Division of Biostatistics, Minneapolis, MN, United States. (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • Northwestern University, Feinberg School of Medicine, Department of Cell and Developmental Biology. (Virtual). (2023). Unsupervised statistical methods and data-driven analysis workflows for spatially-resolved transcriptomics.

  • Bioc2022 Bioconductor annual conference, Seattle, WA, United States. (2022). nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.

  • BLAST Working Group seminar, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States. (2022). nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.

  • Bioc2021 Bioconductor annual conference. (Virtual). (2021). Workshop on ‘Orchestrating Spatially-Resolved Transcriptomics Analysis with Bioconductor (OSTA)’.

  • Bioc2021 Bioconductor annual conference. (Virtual). (2021). Workshop on ‘SpatialExperiment’. (Joint presentation with Dario Righelli and Helena L. Crowell.)

  • European Bioconductor Meeting 2020. (Virtual). (2020). Workshop on ‘SpatialExperiment’. (Joint presentation with Dario Righelli and Helena L. Crowell.)

  • Bioc2020 Bioconductor annual conference. (Virtual). (2020). Unsupervised analysis of transcriptome-scale spatial gene expression data in the human prefrontal cortex.

  • Johns Hopkins University 13th Annual Genomics and Bioinformatics Symposium and Poster Session, Baltimore, MD, United States. (2019). Comparison of dimension reduction algorithms for visualization of single-cell data.

  • European Bioconductor Meeting 2018, Munich, Germany. (2018). HDCytoData package: High-dimensional cytometry benchmark datasets in Bioconductor formats.

  • CYTO 2017: 32nd Congress of the International Society for Advancement of Cytometry, Boston, MA, United States. (2017). Statistical methods for differential discovery in high-dimensional cytometry data.

  • IMLS Scientific Retreat 2017 (Institute of Molecular Life Sciences, University of Zurich), Emmetten, Switzerland. (2017). Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry (CyTOF) data.

  • European Bioconductor Developers’ Meeting 2015, Cambridge, United Kingdom. (2015). regsplice: Lasso-based model selection for improved detection of differential exon usage.

  • C1omics 2015: Single-Cell Omics Methods and Applications, Manchester, United Kingdom. (2015). Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.


Posters