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.

Presented by Matias Cattaneo, Professor of Operations Research and Financial Engineering

Synthetic controls are widely applied to estimate the effects of policy interventions and other treatments of interests. The DataX Workshop on synthetic control methods seeks to provide an introduction to synthetic control methods for non-experts as well as an opportunity for researchers working on synthetic control methods to communicate new results, reach audiences outside their primary disciplinary fields, and seek potential collaborations.

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Presented by

• Michael Churchill - Staff Research Physicist, PPPL
• Hantao Ji - Professor, Astrophysical Sciences
• William Tang - Principal Research Physicist, PPPL

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Presented by:

• Jose Garrido Torres, Data Scientist
• Vineet Bansal, Senior Research Scientist at Princeton Research Computing

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