WPI granted $2M to develop detection tools against illegal wildlife trade
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Worcester Polytechnic Institute's Unity Hall
Researchers at Worcester Polytechnic Institute have received a $2-million grant from the U.S. National Science Foundation to develop tools for research and law enforcement in an effort to combat the multi-billion-dollar illegal wildlife trade.
WPI’s research is one of 10 projects receiving a total of $16 million administered by the NSF through the Partnership to Advance Conservation Science and Practice program, a collaborative initiative with the Seattle-based Paul G. Allen Family Foundation funding conservation science and science-informed conservation practice throughout the U.S., according to the NSF’s website.
Used to obtain exotic pets, traditional medicines, and luxury goods, the illegal wildlife trade has been challenging to control as global law enforcement lacks the tools needed to identify and intercept illegal wildlife products, an effort made more difficult when only fragments of animals are involved, according to a Tuesday press release from WPI.
Over four years, WPI will collaborate with researchers at Florida International University and the University of Maryland to develop a test kit for officials to identify if an animal is a protected species. Costing less than $1 and designed similarly to a COVID-19 test, the kits will input molecular markers into an AI-powered database of 20,000 samples from 185 protected species through a process called high-resolution melting. Offering results in fewer than three hours, the kits will be able to be used in both national and international ports and airports, allowing officials to quickly identify illegally trafficked wildlife.
“Without the right tools, it’s nearly impossible to determine if a species is protected or not,” Kyumin Lee, WPI professor of computer science, said in the release. “This project hopes to change that.”
In addition to test kit development, the awarded funds go toward creating an online infrastructure used to analyze social networks often utilized to discuss wildlife trade. The system will use machine learning to detect illegal wildlife trafficking conversations, track keywords, and leverage data analytics to gain insights into real-time results and long-term trends. Resulting datasets will subsequently be incorporated into a singular open-source repository to be accessed by researchers and law enforcement.
Mica Kanner-Mascolo is a staff writer at Worcester Business Journal, who primarily covers the healthcare and diversity, equity, and inclusion industries.
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