The second annual R/Pharma conference will take place August 21, 22, and 23, 2019 at Harvard University, Cambridge, Massachusetts, USA.
Please note that R/Pharma 2019 is now full. Thank you for the interest to attend R/Pharma 2019. R/Pharma is deliberately a smaller event in terms of attendance in order to foster a more personal experience for attendees. Moreover, R/Pharma is a participant-driven gathering where the attendees are involved in running and organizing the meeting - a Contributor Attended Gathering. Our goal is to bring together the pharma community around open source R and attendance is driven by participation. Please use the form below to let us know if you would like to be added to the wait-list.
Based on feedback, we have devoted a 3rd day to the conference agenda focused completely on R/Pharma workshops for attendees. More info will be available soon.
By design, R / Pharma is deliberately a smaller conference in terms of attendance in order to encourage maximum opportunities for direct interaction with speakers. The location will be the Center for Government and International Studies in the Tsai Auditorium at Harvard University and capped at 150 attendees. As of now, we are planning to make the gathering a free event. Invitations are firstly based on committee membership and advisory support, speaker acceptance, academic/student and diversity attendance.
Our entire event is a community-lead effort and 100% volunteer run. The event is vendor neutral and very much an academic conference. Harvard has been very helpful in hosting the event.
R/Pharma is an ISC working group (www.r-consortium.org/projects/isc-working-groups) under the R Consortium. The conference is envisioned as a relatively small, scientifically & industry oriented, collegial event focused on the use of R in the development of pharmaceuticals. The conference will cover topics including reproducible research, regulatory compliance and validation, safety monitoring, clinical trials, drug discovery, research & development, PK/PD/pharmacometrics, genomics, diagnostics, immunogenicity and more. All will be discussed within the context of using R as a primary tool within the drug development process. The conference will showcase the current use of R that is helping to drive biomedical research, drug discovery & development, and clinical initiatives. (Note that topics related to the use of R in hospitals/clinics for patient care by clinicians, doctors, and researchers will likely be the focus of the upcoming R/Medicine conference.)
The conference will be a single track conference consisting of keynotes from renowned industry practitioners to key R developers to leading academics, pre-conference workshops and full-length presentations as well as a number of shorter, highly-energetic lightning talks.
R/Pharma is dedicated to providing a harassment-free conference experience for everyone regardless of gender, sexual orientation, disability or any feature that distinguishes human beings. For more information, please see the R Consortium code of conduct.
2019 Organising Committee
The organising committee focuses on the logistics of running this conference. The content of this conference is shaped by dozens of Pharma industry colleagues in a community effort, led by John Sims and Bella Feng.
|Eric Nantz||Eli Lilly|
|Elizabeth Hess||IQSS Harvard University|
|John Sims||Pfizer (& Program Committee link)|
|Bella Fang||Amgen (& Program Committee link)|
|Michael Lawrence||Roche/Genentech (& R Foundation link)|
Garrett Grolemund Reproducibility and the role of code in reproducible data science
Garrett Grolemund is the co-author of R for Data Science and R Markdown: The Definitive Guide, as well as the author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as both an educator and advocate of data science with R.
Paul Schuette Simulations, and Complex Innovative Trial Designs
Scientific Computing Coordinator for the Office of Biostatistics. Paul serves as subject matter expert for Scientific and Statistical Computing, manages scientific workstations, acquisition and deployment of software, and with additional projects dealing with statistical methodology appropriate for pregnancy registries and clinical trials.
Marianna Foos Breaking the Speed Limit: How R Gets Faster
Scientific developer specializing in removing roadblocks to data access, with diverse experience independently moving projects forward with a focus on implementation details. Enthusiastic about empowering downstream analysis consumers with training, support and documentation.
Aedin Culhane Title TBD
Harvard Lab website
Computational Biologist at Dana-Farber Cancer Institue, Harvard School of Public Health. Her lab maintains ~6 Bioconductor packages.
Joe Cheng Shiny Reproducibility
Joe is the Chief Technology Officer and Shiny team leader at RStudio.
Max Kuhn Machine Learning
Max is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, AppliedPredictiveModeling, Cubist, C50 and SparseLDA. He routinely teaches classes in predictive modeling at Predictive Analytics World and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology.
Carson Sievert Plotly
Sievert Consulting LLC Website
Carson is a freelance data scientist, well known as the maintainer of the plotly R package on CRAN.
Leon Eyrich Jessen Artificial neural networks in R with Keras and TensorFlow
Technical University of Denmark Github
Leon is an Assistant Professor in the Immunoinformatics and Machine Learning Group (Morten Nielsen Lab) at the Section for Bioinformatics at Technical University of Denmark. I apply advanced machine learning methods to model molecular interactions in the human immune system.
Andy Nicholls R Validation Hub (past, current and future state)
The R-Consortium in June 2018 awarded funding to create an online repository for R package validation in accordance with regulatory standards. Since the main hurdle for widespread use of R in late phase trials is ensuring adequate validation documentation, we are now focused on designing a framework which will specify a set of requirements, including metadata and examples of tests, which together would form evidence of the quality of an R package.
Will Landau Pipeline toolkit for reproducibility and high-performance computing
Eli Lilly Drake website
Data analysis can be slow. A round of scientific computation can take several minutes, hours, or even days to complete. After it finishes, if you update your code or data, your hard-earned results may no longer be valid. How much of that valuable output can you keep, and how much do you need to update? How much runtime must you endure all over again?
Please click an image below to view the program for R/Pharma conferences.