The campus research magazine has an article on artificial intelligence in its latest issue. The CSML participating faculty featured in the article are Ryan Adams, Sanjeev Arora, Danqi Chen, Adji Bousso Dieng, Karthik Narasimhan and Olga Russakovsky.
The expansive whiteness of the ice and snow at the Antarctic seems the same as more than a century ago when explorers first started traversing the continent. But scientists monitoring the continent have noticed rapid changes in the mass and reach of ice sheets, whose collapse can impact sea levels all over the world. Climate change has raised…
Machine learning technologies are increasingly being used to analyze complex, ambiguous situations such as the spread of diseases or financial markets, but some of these algorithms falter when they encounter new data, an altered environment, or have hidden biases that come to the surface.
In machine learning, the term “robustness”…
In the last quarter of this year, news organizations have been reporting that the United Kingdom is headed toward a recession, with economists saying that Brexit, the economic decoupling of the United Kingdom and the European Union, is a major factor.
The economic downturn has not been a surprise to many economists. In fact, in 2017,…
In recent years, scientists have noticed that conclusions in some published research that heavily use machine learning cannot be reproduced.
To uncover why this is happening, Sayash Kapoor, a computer science doctoral student affiliated with the Center for Information Technology Policy (CITP), and Arvind Narayanan, professor of computer science, a participating faculty member of the Center for Statistics and Machine Learning (CSML) and CITP associated faculty, published the paper, “Leakage and the Reproducibility Crisis in ML-based Science.”
The project was conceived by Princeton professors Minjie Chen, Niraj Jha and Yuxin Chen, who were awarded DataX seed funding for the original proposal.
Magnetic components are typically the largest and least efficient components in power electronics. To address these issues, this project proposes the development of an open-source machine-learning based magnetics design platform in order to transform the modeling and design of power magnetics.
The research, by Karthik Narasimhan, assistant professor of computer science, and doctoral students Vishvak Murahari, Carlos E. Jimenez and Runzhe Yang, makes it possible to run a deep neural network at least 10 times more efficiently while sacrificing only 2 percent accuracy. This new approach, called DataMUX, was recently awarded second place in the 2022 Bell Labs Prize with a $50,000 award.
This spring 2023 course aims to foster the ability to plan and perform rigorous data analyses. Students are expected to learn the conceptual underpinnings behind advanced modern methods and to use this knowledge to program appropriate analyses.
Danqi Chen was honored for her work on natural language processing and machine learning. Chen, an assistant professor of computer science, traveled to South Korea to receive the award and deliver a talk at the 2022 Samsung AI Forum.
Schmidt Futures has awarded Adji Bousso Dieng an AI2050 Early Career Fellowship for her work at the intersection of artificial intelligence and the natural sciences. The fellowship recognizes scholars doing interdisciplinary research on AI across fields in engineering, the social sciences and the humanities.