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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An introduction to effective data visualization in Python. Attendees will be exposed to several plotting packages in Python, along with how to integrate them with NumPy and Pandas. Other packages discussed include Matplotlib, Seaborn, and Plotly. Examples will include static and interactive 1D plots, 2D contour maps, heat maps, violin plots, and box plots.
Knowledge prerequisites: Participants should have reasonable facility with the Python programming language, including a basic familiarity with NumPy arrays and Pandas data frames. This session is not appropriate for those with no prior Python experience. However, no previous experience with Python plotting tools is required.

An introduction to effective data visualization in Python. Attendees will be exposed to several plotting packages in Python, along with how to integrate them with NumPy and Pandas. Other packages discussed include Matplotlib, Seaborn, and Plotly. Examples will include static and interactive 1D plots, 2D contour maps, heat maps, violin plots, and box plots.
Knowledge prerequisites: Participants should have reasonable facility with the Python programming language, including a basic familiarity with NumPy arrays and Pandas data frames. This session is not appropriate for those with no prior Python experience. However, no previous experience with Python plotting tools is required.

Wintersession is for University affiliates only.

An introduction to data analysis using the R programming language aimed at people who have ever used R or RStudio before. It will briefly cover different facets of data analysis and their execution using basic R. The style is fairly hands-on, with participants executing the examples on their own laptops alongside the instructors.  This workshop is ideal for those who are at the initial stages of doing independent research requiring quantitative analysis.

Presented by

• Brian Arnold - DataX Data Scientist
• Vineet Bansal - Senior Research Scientist at Princeton Research Computing

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Presented by Boris Hanin, Assistant Professor at ORFE

Class Website

This 2-day virtual workshop explores social biases in machine learning and in human nature; what social scientists and computer scientists can learn from each other. We bring cutting-edge, innovative sociology, social psychology, cognitive science, and computer science perspectives on the interplay between stereotyping and human and artificial intelligence.

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Workshop videos can be found here.

Presented by Roberto Car, Chemistry and  Weinan E, Mathematics

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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.

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