For his master’s degree thesis, which also fulfilled requirements in the CSML graduate certificate, Sid Gupta compared the use of various machine learning methods to estimate probable volatility and prices for vanilla options, a type of financial derivative. In addition to neural networks, Gupta used decision tree-based ensemble models such as gradient boosted trees and random forests to estimate volatility.
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.
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 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'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.
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, 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.
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 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.
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.