This project will investigate the growth dynamic of bacterial biofilm with lattice light-sheet microscopy (LLSM), which is able to resolve individual cells within thick 3D biofilms in a non-invasive manner.

This project aims to explore how colonial policies of urban planning and racial segregation contribute to current-day urban air pollution in Dakar, Senegal.

This project will focus on predicting the interaction sites of host-host proteins using deep learning, so that scientists can better understand how these proteins interact for cell development.

Using data analytics to improve the way organizations identify, attract, develop, and retain talent

Using machine learning to develop personalized therapies

Presidential Fellows in Data Science Miranda Beltzer and Mawulolo Ameko are investigating technology-based models to address mental health by evaluating emotion regulation strategies.

Presidential Fellows in Data Science Erfan Pakdamanian and Nauder Namaky aim to develop a system that optimally alerts drivers when an autonomous vehicle algorithm fails. They plan to develop a deep neural learning algorithm that considers external hazards in conjunction with drivers’ cognitive states.

MSDS '18 students Jack Prominski and Pragati Shah, led by faculty advisor and DSI professor Rafael Alvarado, undertook a capstone project to improve an automated system that matches PLOS article manuscripts with appropriate editors.

A person walking quietly through a park might never be heard by others, but if a flock of birds flies away as the person passes it is revealed that someone is there. Technology capable of discovering quiet agents in real-time, using machines to analyze environments, could prove immensely useful.

MSDS students Boh Young Suh, JB Park and James Xie worked with Prof. Quanquan Gu to develop a spatial model to find the intersections across Virginia with the most deadly crashes.