DataX - researchers use machine learning to model power magnetic material characteristics in advanced power electronics
March 22, 2023

To power homes and other buildings across large land areas, electrical energy is carried on high-voltage transmission lines as alternating current (AC), in which electrons periodically switch direction. A transformer steps down the high voltage in power lines to allow electricity to be safely used in people’s homes. The resulting power from…

CSML participating faculty Jaime Fernández Fisac receives accelerator fund
March 22, 2023

Jaime Fernández Fisac, assistant professor of electrical and computer engineering, along with Robert Shi, who earned his master’s in electrical and computer engineering in 2022, and their team are developing a new robotic system to deliver packages from delivery vehicles to customers’ doorsteps, aiming to double the package-delivery efficiency of human drivers by 2030.

Machine learning course reveals its utility in different disciplines
Feb. 28, 2023

Before 2000, many social scientists avoided quantitative studies of text because it was a time-consuming job, it was difficult to search content for relevant information, and the task didn’t lend itself to being generalizable since each new data set represented unique challenges.

“There was a lot of social interaction occurring in…

Wintersession mini-course gives a taste of machine learning to attendees
Feb. 10, 2023

With machine learning making headways into a variety of research fields and industries and garnering media headlines, a five-day Wintersession mini-course offering an introduction to machine learning became a popular draw, with more than 200 people signing up for at least one of the five days.

The mini-course, Introduction to…

Several CSML participating faculty featured in artificial intelligence article in Princeton's Discovery Magazine
Jan. 30, 2023

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. 

Faculty Profile: Ching-Yao Lai yields machine learning to explore ice sheet physics
Jan. 25, 2023

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…

Visitor Profile: Mert Gurbuzbalaban is advancing robustness in machine learning
Jan. 24, 2023

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”…

Synthetic control emerges as a useful data science tool to test policy interventions in economics and social sciences
Jan. 9, 2023

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,…

Princeton researchers tackle reproducibility in machine learning
Dec. 21, 2022

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.”

DataX seed project MagNet-AI is revamped and online
Dec. 19, 2022

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.