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…
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.
Kate Daugherty is interested in utilizing machine learning tools to uncover hidden trends in social science, a track that she had focused on for her CSML independent project, which was done in her junior year. Daugherty looked at how bail is set in Seattle Municipal Court and used quantitative analysis to see if there were any biases impacting bail decisions, particularly regarding prosecutors’ influence.
Commercial airplanes can be controlled by autopilot. But what happens if a wing gets damaged or an engine malfunctions? Is it possible to design a software system with a feedback loop — a system that quickly tests how controls operate on the damaged vessel and makes adjustments on the fly to give it the best chance of landing safely?
An international team of astrophysicists and cosmologists have spent the past year teasing out the secrets of dark matter, using sophisticated computer simulations and the observations from the one of the most powerful astronomical cameras in the world. The team is led by astronomers from Princeton University, including Michael Strauss, chair of Princeton University’s Department of Astrophysical Sciences and participating faculty member at the Center for Statistics and Machine Learning.
Richard Zhu is a senior in the mechanical and aerospace engineering department. He is pursuing three undergraduate certificates, one from the Center for Statistics and Machine Learning (CSML), the second from the Program in Applied and Computational Mathematics, and the third from the Program in Applications of Computing. He is also a CSML undergraduate student ambassador.
Xuechunzi Bai’s main research focus has been on figuring out where stereotypes of people come from and how do we mitigate or eliminate them since they are inaccurate and biased.
“We know there are different kinds of stereotypes about different social groups such as immigrants,” said Bai. “For example, people typically portray Asian people as very competent but not friendly. The phenomenon of social stereotypes is very established in psychology and other social sciences. A question that is less addressed is where do these social stereotypes come from? My dissertation seeks to answer that origin story.”