Bioconductor virtual conference, August 4-6 2021

Where Software and Biology Connect

Bioconductor virtual conference, August 4-6 2021

Current developments within and beyond the Bioconductor project

Bioconductor virtual conference, August 4-6 2021

Workshops, keynote presentations, and more

Post-conference information Tweets by @Bioconductor
BioC by numbers

Speakers

Daniela Witten, Keynote: "Selective inference on trees"

Professor, University of Washington

Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. Daniela is the recipient of an NIH Director’s Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, a Simons Investigator Award in Mathematical Modeling of Living Systems, a David Byar Award, a Gertrude Cox Scholarship, and an NDSEG Research Fellowship. She is also the recipient of the Spiegelman Award from the American Public Health Association for a statistician under age 40 who has made outstanding contributions to statistics for public health and the Leo Breiman Award for contributions to the field of statistical machine learning. She is a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. Daniela is a co-author of “Introduction to Statistical Learning”. Daniela completed a BS in Math and Biology with Honors and Distinction at Stanford University in 2005, and a Ph.D. in Statistics at Stanford University in 2010. https://www.danielawitten.com/ https://twitter.com/daniela_witten

Melissa Wilson, Keynote: "Sex-biased genomics and methodology"

Associate Professor, Arizona State University

Melissa Wilson is a computational evolutionary biologist whose main research interests include sex-biased biology. She studies the evolution of sex chromosomes (X and Y in mammals), why mutation rates differ between males and females, and how changes in population history affect the sex chromosomes differently than the non-sex chromosomes. Generally she studies mammals, but is also curious about the sex-biased biology of flies, worms and plants. Professor Wilson is also active in public science engagement and outreach. She routinely teaches in K-12 classrooms, and regularly engages the public in discussions about the difference between sex and gender, the importance (or not) of genetic inheritance, and understanding evolution. http://www.sexchrlab.org/ https://twitter.com/sexchrlab

Lucia Peixoto, Keynote: "Reproducible Neuroscience from "omics" data analysis: a tale of sleep and learning"

Assistant Professor, Washington State University

Lucia Peixoto received her bachelor’s degree in Biochemistry from the Universidad de la Republica in her native Uruguay in 2002 and earned her Ph.D. in 2009 at The University of Pennsylvania under the mentorship of Dr. David S. Roos. She completed her postdoctoral training with Dr. Ted Abel at The University of Pennsylvania in 2015. During her fellowship, she was also a trainee at the Training Program in Neurodevelopmental disabilities at the Children’s Hospital of Philadelphia https://neurotraining.research.chop.edu with support from NINDS/NIH. As a trainee at CHOP, she completed a clinical internship at the Center for Autism Research under the supervision of Dr. Robert Schultz. She became an Assistant Professor at Washington State University in 2015 and has since been recognized with a K01 Career Development award from NINDS/NIH. https://medicine.wsu.edu/peixoto-lab/ https://twitter.com/luciascience

Gabriela de Queiroz, Keynote: Building a Diverse Community: The R-Ladies Story

Sr. Engineering & Data Science Manager, IBM

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. . https://k-roz.com/ https://twitter.com/gdequeiroz

Amanda Haddock, Keynote: Redefining the Patient's Role in the Genomics Era

Liaison to the Scientific Committee at Childrens Brain Tumor Tissue Consortium (CBTTC)

Amanda Haddock is president and co-founder of Dragon Master Foundation, a nonprofit that strives to speed biomedical discovery by developing and implementing tools and technology to empower cancer researchers. Dragon Master Foundation’s pilot project is an initiative to build an open access, collaborative, multi-institute research infrastructure with innovative informatics & high quality data to empower the next wave of precision medicine approaches for cancer therapeutics. The database infrastructure will allow for unlimited future growth and has the capacity to impact all areas of biomedical research. Amanda’s son, David Pearson, died from brain cancer in 2012, a tragedy that brought the need for better research tools to her attention. https://www.dragonmaster.org/ https://twitter.com/AmandaHaddock

Matthew Stephens, Keynote: "An invitation to a multiple testing party!"

Professor in Statistics and Human Genetics at University of Chicago

Matthew Stephens is a Bayesian statistician and professor in the departments of Human Genetics and Statistics at the University of Chicago. His lab works on a wide variety of problems at the interface of Statistics and Genetics. Much of hisresearch involves developing new statistical methodology, many of which have a non-trivial computational component. He is known for the Li and Stephens model as an efficient model for linkage disequilibrium. https://stephenslab.uchicago.edu/ https://twitter.com/mstephens999

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