Wei Liu, a Ph.D. student in computer science, was attracted to Virginia Tech’s computer science program and the Sanghani Center for two reasons: her strong interest in visualization and artificial intelligence and the opportunity to work with Chris North. 

“Dr. North is a model advisor,” she said, “not only in guiding my research but also in his kindness and understanding toward students. He is approachable and open-minded.”

Liu’s research focuses on explainable artificial intelligence and visualization, particularly for dimensionally reduced text embeddings. 

A real-world example of this, she said, is helping researchers and analysts understand and explore relationships in text datasets -- such as research or news articles -- within the projection space of these embeddings. 

She presented her work with North and postdoc Rebecca Faust, "Visualizing Spatial Semantics of Dimensionally Reduced Text Embeddings," at the IEEE VIS 2024 NLVIZ Workshop, Exploring Research Opportunities for Natural Language, Text, and Data Visualization, in October. 

Their paper designed a visualization system that incorporates spatial word clouds into the document projection space to illustrate the impactful text features and gave three usage scenarios that demonstrate the practical applications of the system to facilitate the discovery and interpretation of underlying semantics in text projections.

"What I like best about being a student at the Sanghani Center is the interdisciplinary and collaborative environment it fosters,” Liu said. 

She is grateful for North’s and Faust’s invaluable guidance and support and the insightful discussions with her fellow students at InfoVis Lab @Virginia Tech. Additionally, Liu said, she thanks Yali Bian, Huimin Han, Brian Keith Norambuena, and Mandar Sharma, all Ph.D. graduates from the Sanghani Center, for their encouragement and help, especially during the early stages of her study.

“The Sanghani Center is a community that not only supports my academic growth, but also inspires me to develop practical solutions to real-world challenges in AI and data analytics,” Liu said. 

Projected to graduate in 2026, Liu is interested in pursuing a career in industry, while remaining open to exploring potential roles in academia in the future. 

“Wherever my journey takes me, I hope to continue to learn and strive to make contributions along the way,” Liu said.