Jafar Howe: applying machine learning to analyze and detect different language accents
Aug. 1, 2022

For his independent project for CSML, Jafar Howe decided to develop an English language accent classifier, basically a tool to accurately detect different English accents. Howe did this by converting speech samples into a time-frequency spectrogram, a visual representation of audio. 

Linear dynamical systems research by Yanxi Chen and H. Vincent Poor wins outstanding paper at machine learning conference
July 27, 2022

Research on linear dynamical systems by Yanxi Chen, a doctoral student in Princeton University’s Department of Electrical and Computer Engineering, and H. Vincent Poor, the Michael Henry Strater University Professor, won the outstanding paper award at this year’s International Conference on Machine Learning, which was held in Baltimore, Maryland from July 17 to 23.

Caio Costa: advancing computer vision into new horizons
July 25, 2022

Caio Costa tackled computer vision for his CSML independent project, specifically how computers may perceive objects that are partially occluded from view. He set out to put together a machine learning process that uses light bounced off a surface to determine an object’s shape, a technique known as non-line-of-sight (NLOS) imaging, a burgeoning area of study in the computer vision field.

Vineet Bansal provides software engineering expertise for database on magnetic materials and advanced computational software program
July 20, 2022

Vineet Bansal's most recent projects include contributions to MagNet, a large-scale dataset that allows researchers to model how materials react to electromagnetic excitation, and OSQP, a software program that solves quadratic systems with linear constraints. 

Machine Learning Theory Summer School fosters research community in a fast-growing field
July 12, 2022
Written by Molly Sharlach

This June, 60 graduate students came to Princeton from more than 20 institutions in six countries to learn from academic and industry experts in machine learning theory. The event was the second year that the Princeton Machine Learning Theory Summer School was held.

Olga Russakovsky receives NSF award to address bias in computer vision
July 11, 2022
Written by Sharon Adarlo

Olga Russakovsky, assistant professor of computer science and participating faculty member of the Center for Statistics and Machine Learning (CSML), recently received a National Science Foundation CAREER award to mitigate bias in computer vision through various strategies and to enact educational opportunities for under-represented groups.

Videos: Three DataX Data Scientists Discuss their Role and Impact in Research
July 6, 2022

Data scientists Pranay Anchuri, Amy Winecoff and Andrzej Zuranski, supported by the Schmidt DataX Initiative, are featured in a new series of videos talking about their role and impact in research with Princeton University scholars.

Abigail Drummond: using data science to understand dengue fever and other viral outbreaks
June 29, 2022

Abigail Drummond advanced a novel machine-learning driven technique to map the outbreak risk for dengue, as a case study example. She started by collecting climate and anthropogenic data from 2000 to 2019. She used this data to model current dengue outbreak risk using various machine-learning based species distribution models. She then compared the outbreak risk to the distribution of the mosquito species, Aedes aegypti, the main vector for dengue.

DataX workshop held for researchers who want to incorporate data science and machine learning into their work
May 25, 2022
Written by Sharon Adarlo

A two-day DataX workshop that covered a wide range of scientific topics, from Bayesian inference techniques to looking at machine learning in the context of the larger world, was held from May 13th to the 14th at Princeton University’s Friends Center. According to its organizers, the event, “Tutorial Workshop on Machine Learning for Experimental Science,” was meant to disseminate current topics and techniques in the field so that scholars may advance their research.

Peter Ramadge, CSML director, recognized for outstanding teaching along with three faculty members
May 24, 2022

The detailed lecture notes that Ramadge distributes every week are legendary. They have proved to be indispensable to the many students who have taken his courses over the years since Ramadge, whose scholarship focuses on signal processing and machine learning, joined the faculty in 1984.