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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. He used the S&P 500 option price data from 2016 to 2018 to train and test his models.
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.
Garip was one of eight candidates chosen for the committee, and she will serve a three-year term. Her research portfolio is deeply rooted in migration patterns, economic sociology and inequality. She uses machine learning as part of her research. Her book, “On the Move: Changing Mechanisms of Mexico-U.S. Migration,” reveals the diversity of migrants from Mexico and their evolving migration patterns over time.
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.
Mona Singh, along with four other members of the Princeton engineering faculty, has been named to an endowed professorship, effective July 1. Singh, a CSML participating faculty member, focuses on developing algorithmic and machine learning approaches to decode genomes, and she is especially interested in developing data-driven methods for predicting and characterizing protein sequences, functions, interactions and networks, both in healthy contexts and in diseases such as cancer.
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.