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Featured News

CSML leadership is pleased to announce the very popular certificate program in statistics and machine learning has been converted to a Princeton University minor. The change is effective for the Fall 2023 semester. More details to come.
For program requirements, please contact Susan Johansen…
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Open Positions
The Princeton Center for Statistics and Machine Learning (CSML) invites applications for DataX Postdoctoral Fellowships.
The DataX Postdoctoral Fellowships are intended for early-career scientists with a research interest in data science, statistics, and machine learning. As an associate, you will join the research group of a current…
Latest News

All intelligent organisms have a nervous system, a way for communication to flow between the brain and the motor system and vice versa. Researchers at Princeton have taken a first step in developing this type of coordination for mechanical AI systems using the tools of machine learning.
The researchers created a neural network that…

Claire Dennis, a graduate student in the Princeton School of Public and International Affairs, is steeping herself in math and computer code this spring. While she plans to enter the world of policy — and not that of algorithms and computer programming — she felt it was important to familiarize herself with how technology is transforming the…

Amidst the whirl of yet another busy spring semester at Princeton University, students enrolled in the undergraduate certificate program at the Center for Statistics and Machine Learning Center (CSML) stepped out of the classroom last month for a special meal at Prospect House. Between bites of salad and various entrees, students had the opportunity to connect with faculty in an informal setting as part of CSML’s “Dinner with a Professor” on April 11th.

A new project led by Brandon Stewart, associate professor of sociology and a researcher in the Office of Population Research, aims to learn what words, phrases and arguments successfully persuade people. The team will apply textual analysis tools and modern causal-inference designs to discover what features make a document persuasive. Using new large-language models, the team will create new machine-generated texts that possess these features, allowing the researchers to study systematically how specific attributes of the texts convince their audience.
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