Invited presentations

• 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

• National Human Genome Research Institute (NHGRI) Research Training and Career Development Annual Meeting 2023, Salt Lake City, UT, United States. (2023). nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.

• American College of Neuropsychopharmacology (ACNP) 2022, Phoenix, AZ, United States. (2022). (Travel awardee.) The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics.

• ASHG Institute for Genomic Medicine Symposium on Spatial Omics. (Virtual). (2020). Unsupervised analysis of transcriptome-scale spatial gene expression in human dorsolateral prefrontal cortex using spatial transcriptomics data.

• Genome Informatics. (Poster and lightning talk.) (Virtual). (2020). Unsupervised analysis of transcriptome-scale spatial gene expression in human dorsolateral prefrontal cortex using spatial transcriptomics data.

• CYTO 2018: 33rd Congress of the International Society for Advancement of Cytometry, Prague, Czechia. (2018). diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.

• Ascona Workshop 2017: Statistical Challenges in Single-Cell Biology, Ascona, Switzerland. (2017) Statistical methods for differential discovery in high-dimensional cytometry data.

• ICML 2016 (International Conference on Machine Learning): Workshop on Computational Biology, New York, NY, United States. (2016). Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry (CyTOF) data.

• SIB Days: Swiss Institute of Bioinformatics annual conference, Biel/Bienne, Switzerland. (2016). Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry (CyTOF) data.

• IMLS Scientific Retreat 2015 (Institute of Molecular Life Sciences, University of Zurich), Morschach, Switzerland. (2015). Improving power to detect differential exon usage by L1-regularization (lasso) model selection.