News

New course: SML 301 - Data Intelligence: Modern Data Science Methods
Nov. 23, 2022

This spring 2023 course aims to foster the ability to plan and perform rigorous data analyses. Students are expected to learn the conceptual underpinnings behind advanced modern methods and to use this knowledge to program appropriate analyses.

Danqi Chen, CSML participating faculty, named a 2022 Samsung AI Researcher of the Year
Nov. 21, 2022

Danqi Chen was honored for her work on natural language processing and machine learning. Chen, an assistant professor of computer science, traveled to South Korea to receive the award and deliver a talk at the 2022 Samsung AI Forum. 

Adji Bousso Dieng, CSML participating faculty, receives AI2050 Early Career Fellowship
Nov. 14, 2022

Schmidt Futures has awarded Adji Bousso Dieng an AI2050 Early Career Fellowship for her work at the intersection of artificial intelligence and the natural sciences. The fellowship recognizes scholars doing interdisciplinary research on AI across fields in engineering, the social sciences and the humanities. 

Princeton students use data science to map the best routes to class
Nov. 9, 2022

To map pedestrian traffic on campus and get students involved with data science tools, Princeton Data Science (PDS), a club sponsored by CSML, is organizing a group project that is focused on finding out where people go every day on campus and elucidating efficient routes to buildings.

Oluwatamilore “Tamilore” Ajeigbe: harnessing data science to look at impact of race on STEM retention rates
Sept. 30, 2022

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.

CSML hosts "Welcome Back" event
Sept. 26, 2022

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. 

Hannah To: using data science to look at impact of gangs in El Salvador
Sept. 2, 2022

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.

CSML Alumni Profile: Ujjwal Dahuja uses data science to make sense of complex, big data in finance
Aug. 24, 2022

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. 

Scientists use artificial intelligence to model the formation of ice
Aug. 18, 2022

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…

CSML Alumni Profile: Sid Gupta uses data science to analyze the financial markets
Aug. 10, 2022
Author
Written by Sharon Adarlo

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.