Meenu Ravi earned a bachelor’s degree in computer science at Rensselaer Polytechnic Institute and a master’s degree in statistics and analytics from the University of Chicago. 

“When it came time to deciding on a Ph.D. program in computer science, Virginia Tech stood out to me in particular because it offered a research environment where we are inspired to investigate, experiment, and learn for ourselves,” Ravi said. “It focused a lot on taking ownership of our own research journey. After talking with Professor Lu, I felt empowered and supported to find what interested me specifically, explore independently, take initiative in designing projects, and assemble a team in such a way that fit my vision of the project.”

Chang-Tien Lu is now her advisor at the Sanghani Center.

Ravi’s research lies at the intersection of natural language processing and machine learning to help the general public and public agencies plan for and respond to environmental crises. She aims to build trustworthy and fairness-aware systems that support decision-making in environmental disaster response and long-term resilience planning. 

With a past project, she looked to developing a method of predicting vegetation loss for ecological management and informing environ- mental and land-use policy decisions. 

“Currently,” she said, “I am working on developing a question-answering system that aims to help the general public understand and navigate the aftermath of a wildfire, from financial assistance to health and safety questions. I am also working on developing a personalized flood evacuation rerouting tool.”

She finds this area especially fascinating, she said, because of its interdisciplinary nature. 

“It draws on ecology, physics, economics, anthropology, and statistics. No matter where we are in the world, we are all susceptible to natural hazards. With the rapid growth of information, increasing socioeconomic gaps, and rapidly changing climate, it has become more important than ever to understand these hazards and be well-informed on effective policy decisions,” said Ravi.

Earlier this month, she presented “MVeLMA: Multimodal Vegetation Loss Modeling Architecture for Predicting Post-fire Vegetation Loss” in main proceedings at the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS) in Minneapolis, Minnesota. The paper predicted vegetation loss; mapped risk; and provided a guide to ecological recovery.

The Sanghani Center has been a motivating and supportive research community for her work. “Both the faculty and my peers have been incredibly helpful in my learning journey. They have always been open to teach me new skills, provide insights, and collaborate,” she said.

Projected to graduate in May of 2029, she would like to pursue a career at a research center dedicated to advancing solutions for social impact.