Reproducibility standards for machine learning in the life sciences with Stephanie Hicks, Associate Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health
Stephanie will be talking about her paper on reproducibility standards and her work at John Hopkins.
| An Institute for Data Science and Artificial Intelligence seminar | |
|---|---|
| Date | 1 February 2023 |
| Time | 14:00 to 15:00 |
| Place | To be delivered by Zoom Details will be confirmed on registration. |
Event details
Abstract: To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.
Biography: Stephanie is an Associate Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health working at the intersection of data science and health. She is also a faculty member of the Johns Hopkins Data Science Lab and the Center for Computational Biology, co-host of The Corresponding Author podcast and co-founder of R-Ladies Baltimore.
Delivery and Registration: The seminar will be delivered by Zoom. To register, please click here. Registration closes: Wednesday 1 February 2023 at 09:00 (GMT). If you miss the registration, please contact IDSAI.
If you have any queries, please contact IDSAI.
This forms part of the IDSAI Research Seminar Series for 2022-2023.
