Data Science Research in CS@W&M
To Prospective Undergraduate and Graduate Students
Our research focuses on developing theory and technologies for scaling up machine learning and data-mining systems. This includes building distributed systems for machine learning algorithms and developing new statistical techniques to effectively extract actionable knowledge from massive datasets.
In the past, we have built models and data mining algorithms for analyzing social networks (yelp data challenges, community detections, viral marketing), making sense of news and forum datasets (our ICDM paper and ICWSM paper), and improving efficiencies of massive open online course platforms (learning about social learnings).
Sound Interesting?
Graduate students: Take my fall course (link) and we will find some interesting projects to work on together.
Undergraduate students: After graduation, will you apply for a job and make the world a better place, or pursue a PhD? Come and have a chat! We may find projects that help you better understand your choices.
One project we are specifically interested in is to use machine learning techniques to understand images posted in twitter related to this year’s election.
Math majors: Some of our projects heavily use probability theory, graph theory, and optimization. For example, we study random walks, build statistical models for community detections, and use combinatorial optimization to understand technology diffusion.