Talks and Other Events

2019

• Teaching data science through case studies in Public Health. Joint Statistical Meeings. 2019 Jul 27-Aug 1. Denver, CO. USA.
• mbkmeans: fast clustering for single cell data using mini-batch $k$-means. Bioconductor Conference. 2019 Jul 24-27. New York City, NY, USA.
• Useful Tools for Teaching And Outreach In Data Science: Workflows, Case Studies, Github Classroom, and Slack. Symposium on Data Science and Statistics. 2019. May 29-Jun 1. Bellevue, WA. USA.
• Addressing Open Challenges Data Science Education. Department of Statistics and Data Sciences, Smith College. 2019 Apr 18. Northampton, MA. USA.
• Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments. ENAR Conference. 2019 Mar 24-27. Philadelphia, PA. USA.
• Making data science accessible world-wide in the Johns Hopkins Data Science Lab. Department of Statistics, University of Connecticut. 2019 Feb 27. Storrs, CT. USA.
• Applications of Latent Variables in Identifying Systematic Errors in Genomics. Department of Statistics, Rice University. 2019 Feb 11. Houston, TX. USA.
• National Human Genome Research Institute’s Genome to Phenotype Strategic Planning Meeting. 2019 Jan 22-24. Rockville, MD, USA
• Estimating cell type composition in whole blood using differentially methylated regions. Department of Statistics, Oregon State University. 2019 Jan 14. Corvallis, OR. USA.

2018

• Orchestrating Single-Cell RNA-sequencing Analysis with Bioconductor.
  • Keynote presentation at Bioconductor Asia on 2018 Nov 29 at the University of Melbourne, Melbourne, Australia.
  • Invited presentation at Australian Mathematical Sciences Institute (AMSI) Bioinfosummer on 2018 Dec 3-7 at the University of Western Australia, Perth, Australia.
• Analyzing Genomics Data in R with Bioconductor at DC R Conference. 2018 Nov 8. Washington D.C., USA.
• Estimating cell type composition in whole blood using differentially methylated regions. Department of Biostatistics, The University of Alabama at Birmingham. 2018 Nov 2. Birmingham, AL. USA.
• Estimating cell type composition in whole blood using differentially methylated regions at the 12th Annual Symposium and Poster Session on Genomics and Bioinformatics at Johns Hopkins University. 2018 Oct 25. Baltimore, MD. USA.
• Women in Statistics and Data Science Conference. Lead a project on building a children’s book to highlight women in Statistics and Data Science.
• Data Science Innovation Lab: Mathematical Challenges of Single Cell Dynamics. Invited to participate in a five-day workshop with early-career biomedical and quantitative investigators to create collaborations, answering important questions in single cell data.
• Open challenges in Single-Cell RNA-Sequencing. Joint Statistical Meetings. 2018 Jul 29-Aug 2. Vancouver, CA.
• Open Challenges in Single-Cell RNA-Sequencing. Single Cell Data Science: Making Sense of Data from Billions of Single Cells. 2018 Jun 4-8. Lorentz Center, Leiden University, Netherlands.
• - Technology-independent estimation of cell type composition using differentially methylated regions. Office of Biostatistics Research at NHLBI/NIH. 2018 Apr 24. Bethesda, MD. USA.

2017

• Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments. Joint Statistical Meetings. 2017 Jul 29-Aug 3. Baltimore, MD, USA.
• Estimating cell type composition in whole blood using differentially methylated regions. Bioconductor conference. 2017 Jul 26-28. Boston, MA, USA.
• Missing Data and Technical Variability in Single Cell RNA-Sequencing Experiments. Ascona Workshop 2017: Statistical Challenges in Single Cell Biology. 2017 Apr 30-May 5. Ascona, Switzerland.
• Setting the Stage for Reproducibility and Replicability in Science. Brandeis University. 2017 Mar 22. Waltham, MA, USA.

2016

• On the widespread and critical impact of systemic bias and batch effects in single-cell RNA-seq data. Presented at the Boston Single-Cell Network Meeting in March 2016 (Boston, MA, USA), presented at the Joint Statistical Meetings Aug 2016 (Chicago, IL, USA), and presented at the Single-Cell Genomics Conference Sept 2016 (Hinxton, UK).
• Batch effects and technical biases in scRNA-Seq data. HSCI Single-Cell Workshop. 2016 Nov 29-30. Harvard Medical School, Boston, MA, USA.
• Transforming the Classroom to Teach Statistics and Data Science with Active Learning. Women in Statistics and Data Science Conference. 2016 Oct 20-22. Charlotte, NC, USA.
• Towards progress in batch effects and biases in single-cell RNA-seq data. 2016 Sept 14-16. Wellcome Genome Campus, Hinxton, Cambridge, UK.
• On the widespread and critical impact of systemic bias and batch effects in single-cell RNA-seq data. Joint Statistical Meetings. 2016 Jul 31-Aug 4. Chicago, IL, USA.
• On the widespread and critical impact of systemic bias and batch effects in single-cell RNA-seq data. Boston Single-Cell Network Meeting. 2016 Mar 15. Boston, MA, USA.

2015

• On the widespread and critical impact of batch effects and systematic bias in single-cell RNA-Seq data (Selected for a Stellar Abstract Award). PQG Conference: Single-cell Genomics: Technology, Analysis, and Applications. 2015 Nov 5-6. Boston, MA, USA.
• Mozilla Open Science Leadership Summit. Invited to work with community leaders furthering open practice and open science through creating community events, tools for collaboration and learning resources.
• Why Statistics Matters in the Analysis of Genomics Data (Youtube video). Presented at the LSU Computational Biology seminar and the LSUConnect event. 2015 Feb 11. Baton Rouge, LA, USA.

2014

• Normalization of DNA methylation and Gene Expression Data in the Context of Global Variation. Bioinformatics Meeting, Division of Immunology, Harvard Medical School. 2014 Sept 18. Boston, MA, USA.
• quantro: When should you use quantile normalization?. Flashlight talk at the Bioconductor Conference. 2014 Jul 30-Aug 1. Boston, MA, USA.
• ROpenSci Unconference. Invited to work with over 40 R enthusiasts from industry, academia, non-profits and government on projects supporting open data, open science and data visualization in R. Contributed to R-packages including explainr and catsplainr.

2011

• Modeling Discovery Of Functional SNPs From Genome Scale Data. JSM 2011. Miami, FL, USA.

2010

• Prediction of Missense Mutation Functionality Depends on both the Algorithm and Sequence Alignment Employed. Human Genome Variation Society’s Exploring the Functional Consequences of Genomic Variation Meeting. Washington, D.C., USA.