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For her CSML independent work project, Ajeigbe decided to compare data from Predominantly White Institutions (PWIs) versus Historically Black College and Universities (HBCUs). She used pruned decision trees and OLS regression to uncover factors that would make a student more likely to stay in a STEM major and whether these factors changed depending on the institution, PWI or HBCU.
The Center for Statistics and Machine Learning’s (CSML) "Welcome Back" reception for undergraduate and graduate certificate students, researchers and faculty was a chance to mark the beginning of the academic year, catch up with colleagues, and strengthen ties within the campus data science community. The event was held on September 19th. Check the post for a slideshow of pictures.
The Center for Statistics and Machine Learning (CSML) invites applications for DataX Postdoctoral Fellowships. The DataX Postdoctoral Fellowships are intended for early-career scientists with a research interest in data science, statistics, and machine learning.
For her CSML independent work project, Hannah To worked on a study to see how gangs in El Salvador impacted labor, and was advised by Thomas Fujiwara, associate professor of economics and international affairs. This project also fulfilled her senior thesis requirement.
Ujjwal Dahuja is currently an associate at Blackstone Alternative Asset Management (BAAM), a leading alternative investment firm. At BAAM, Dahuja is in a team responsible for making allocation recommendations on quantitative hedge funds, private hedge funds that use systematic processes for asset selection.
A team based at Princeton University has accurately simulated the initial steps of ice formation by applying artificial intelligence (AI) to solving equations that govern the quantum behavior of individual atoms and molecules.
The resulting simulation describes how water molecules transition into solid ice with quantum accuracy…
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.