When: July 29 - 31, 2020
What: Community/Developer Day, Main Conference
Where: Simmons University and Harvard Medical School, Boston, USA
Slack: Bioconductor Team (
BioC2020 highlights current developments within and beyond the Bioconductor project.
Gabriela de Queiroz is a Sr. Engineering & Data Science Manager at IBM where she leads the CODAIT Machine Learning Team. She works in different open source projects and is actively involved with several organizations to foster an inclusive community. She is the founder of AI Inclusive, a global organization that is helping increase the representation and participation of gender minorities in Artificial Intelligence. She is also the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 180 chapters in 45+ countries. She has worked in several startups where she built teams, developed statistical models and employed a variety of techniques to derive insights and drive data-centric decisions.
Corrie Painter is the Associate Director of Count Me In and is a research scientist at the Broad Institute of MIT and Harvard. A trained cancer researcher with a Ph.D. in biochemistry, she completed her postdoctoral work in cancer immunology, focused on melanoma. In 2010, Painter was diagnosed with angiosarcoma. She has combined her cancer advocacy and scientific background to engage with patients in order to build and carry out patient-partnered genomics studies. She is also the co-founder of Angiosarcoma Awareness Inc.
Rafael Irizarry is Professor and Chair of the Department of Data Sciences at the Dana-Farber Cancer Institute and a Professor of Biostatistics at Harvard School of Public Health, and one of the original founders of the Bioconductor Project. Professor Irizarry’s work has focused on problems related to microarray, next-generation sequencing, and genomic data. Currently, he is interested in leveraging his knowledge in translational work, e.g. developing diagnostic tools and discovering biomarkers. During his career, he has co-authored papers on a variety of topics including musical sound signals, infectious diseases, circadian patterns in health, fetal health monitoring, and estimating the effects of Hurricane María in Puerto Rico.
Caroline Uhler, formerly Associate Professor at MIT in Cambridge, USA, recently joined the ETH Zurich, Switzerland, as Professor of Machine Learning, Statistics and Genomics. Her research focuses on statistics, machine learning and computational biology. In particular, graphical models, causal inference, algebraic statistics and applications to genomics, for example linking the spatial organization of the DNA with gene regulation.
Kylie Bemis is a faculty in the Khoury College of Computer Sciences at Northeastern University where she teaches data science and develops curriculum for the MS in Data Science program. Her research interests include machine learning and large-scale statistical computing for bioinformatics. She is active in outreach to the Native American and LGBTQ communities, an enrolled member of the Zuni tribe, and a writer of fiction and poetry.
Fei Chen is currently a Fellow at the Broad Institute. During the course of his doctoral studies in Biological Engineering at MIT, Fei co-invented expansion microscopy (ExM): A breakthrough technique that allows for super-resolution imaging of biological samples with conventional light microscopes. Chen’s lab utilizes ExM as a platform for in situ transcriptomics and epigenomics, while continuing to pioneer novel molecular and microscopy tools to uniquely illuminate biological pathways and function.
Aaron Lun is a Scientist at Genentech, and previously was a research associate in John Marioni’s group at the CRUK Cambridge Institute and completed a PhD with Gordon Smyth at the Walter and Eliza Hall Institute for Medical Research in Melbourne. Aaron is a prolific contributor to the Bioconductor project, currently especially in the area of single-cell RNA-seq.
Shirley Liu is a Professor with the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute and Harvard School of Public Health. Her research focuses on algorithm development and integrative mining from big data generated on microarrays, massively parallel sequencing, and other high throughput techniques to model the specificity and function of transcription factors, chromatin regulators and lncRNAs in tumor development, progression, drug response and resistance.
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