May 13-14, 2022
Friend Center 101
Many scientific experiments generate large, multi-modal datasets, often in the form of time-series of different dimensionality. A particular challenge that scientists face in their workflows is comparing experiments to model and simulation, determining how close experiments match expected theory. The various analyses that scientists perform on these datasets can greatly be enhanced and accelerated by machine learning techniques, including recent deep learning and Bayesian inference techniques. The main objective of the workshop is to distill current machine learning techniques to a broad scientific audience at Princeton, and provide much needed research tools based on machine learning to advance their science. This should benefit mostly the Princeton research community but also the broader nearby research institutions.
The use of machine learning (ML) methods for prediction and forecasting has become widespread across the quantitative sciences. However, there's a reproducibility crisis brewing. Indeed, we found 20 reviews across 17 scientific fields that find errors in a total of 329 papers that use ML-based science. Hosted by the Center for Statistics and Machine Learning at Princeton University, our online workshop provides an interdisciplinary venue for diagnosing and addressing reproducibility failures in ML-based science.
We especially welcome researchers outside traditional ML fields who are interested in applying ML methods in their own fields. Participants will learn to identify reproducibility failures in their own fields and ensure that their research is reproducible. Through our interdisciplinary workshop, we will:
Highlight the scale and scope of the crisis in ML-based science.
Identify root causes of the observed reproducibility failures and explain why they have occurred in dozens of fields that adopted ML methods.
Make progress towards solutions by outlining a concrete research agenda for reproducibility in ML-based science.
More information and registration details can be found at this link.