Boosting customer retention for software-as-a-service companies with an XGBoost model

Using an adversarial learning approach to predict a fraudster’s next move

Applying performative analysis to predict linguistic rigidity in political, social and religious groups

A model to fuse massive amounts of contextual data from the Department of Defense

MSDS 2018 Cohort students Kia Duan, Nitesh Prakash, Duncan Rule and Ni Zhuang worked to create a program that could more accurately assess threat levels from rallies and protests led by extremist groups.

Using machine learning to develop personalized therapies

Master of Science in Data Science capstone project researchers Vaibhav Sharma, Beni Shpringer, Sung Min Yang, and Martin Bolger, along with Ph.D. students Sodiq Adewole and Erfaneh Gharavi, sought to develop new methods for collecting, generating, and labeling data to aid in the creation of educational, free-input dialogue simulations.