Junior builds algorithm to aid disaster recovery

When Esha Singaraju first arrived on campus in 2023, she wasn’t sure what path she wanted to follow. One class unexpectedly shaped her academic journey: Environmental Law and Policy, taught by Carolina law professor Donald Hornstein.

She learned about climate legislation and the Federal Emergency Management Agency, which supports citizens and emergency personnel in preparing for, responding to and recovering from a wide range of hazards.

“Natural disasters are so volatile,” Singaraju says. “You don’t know how much they are going to affect you, but to be able to use data from past disasters to help prepare for future ones is a really cool idea.”

Now a junior studying computer science and environmental studies in the UNC College of Arts and Sciences, she is collaborating with professor Gregory Characklis and the UNC Institute for Risk Management and Insurance Innovation. This multidisciplinary hub brings together researchers and industry professionals to develop solutions for managing the financial risks faced by communities worldwide, ranging from extreme weather events to cybersecurity threats.

Singaraju is working on a project to collect financial aid data from FEMA and the U.S. Small Business Administration, a federal agency that provides financial assistance and counseling to help Americans start, build and grow businesses. Her goal is to develop machine learning models that predict the amount of aid North Carolina homeowners may receive after future natural disasters.

“What excites me about this project is the blending of technology like AI with important topics like climate policy,” she says. “It’s interesting to see how powerful data can be in addressing a real-world problem.”

Seeking aid

In North Carolina, approximately 97% of homeowners lack flood insurance, according to the state’s insurance commissioner. This becomes a critical problem after disasters like hurricanes or floods, when homeowners are left searching for financial solutions.

“In the past 20 years, you’ve seen all these hurricanes and flooding, and that has had a big impact on homes in the state,” Singaraju says.  “And then it opens up to some disparities in how this federal aid is distributed.”

There are two federal aid sources designed to help homeowners recover after a disaster has been declared by the federal government. Run through FEMA, the Individuals and Households Program provides grants to homeowners to help them pay for temporary housing, repair or replace their damaged homes, and cover other disaster-caused expenses.

The SBA leads a second program that offers low-interest, long-term disaster loans to homeowners to help with repairs or replacement of damaged homes.

“These programs are essential for North Carolina homeowners, but the application and approval process can be complex and vague,” Singaraju says. “There’s so much uncertainty in how or why someone gets approved and how much they’re even likely to get approved for.”

She saw an opportunity: What if she could use artificial intelligence to identify key factors that affect these approvals?

Building the algorithm

During the summer after her freshman year, Singaraju joined Characklis’s lab and began developing her algorithm — an incredibly ambitious goal considering she’d only taken one computer science course.

“I had no idea how to do any of it,” she recalls with a laugh. “It was a lot of learning how to code, how to think about designing research and asking good questions.”

While the project is a work in progress, she hopes that it will be a useful tool for homeowners, researchers and policymakers.

“I’m someone who likes to think ahead and then think logically and critically,” she said. “And to be able to do that on a computer using code and then seeing tangible results of how it’s impacted somebody is exciting.”

Read more about Esha Singaraju’s research.

Read more research stories from UNC-Chapel Hill