University of Tennessee

Faculty Spotlight: Wenjun Zhou

March 21, 2016

After earning her undergraduate degree in China, Wenjun Zhou traveled to the United States to pursue her master’s at the University of Michigan. She went on to earn a PhD in management from Rutgers University in 2011. “Then I received an offer from the University of Tennessee and had a good experience visiting the campus,” Wenjun says. “So I decided to come.”

Wenjun is currently an assistant professor in the Department of Business Analytics & Statistics. Her teaching and research interests focus on data mining. “I teach graduate level data mining and text mining courses,” Wenjun says. “This year I am also teaching an undergraduate senior level data mining class, and we are developing an advanced version of the course for PhD students starting next year.”

Correlation computing is a further focus of Wenjun’s research. “One application is to use the correlation to make recommendations for online purchases,” she says. “For example, when you’re buying something online, a website will make suggestions based on similar items other consumers have purchased.” Creating programs that can compute these correlating products quickly is a challenge for businesses, Wenjun says. “Computing efficiency is a critical goal we want to achieve,” she explains. “How we measure a correlation is another aspect of the challenge.”

Some of Wenjun’s recent research on peer-to-peer lending was published in the European Journal of Operational Research (EJOR) in 2016. “This type of lending is normally based on an internet web platform where users apply for small loans up to $20,000,” Wenjun explains. “They have to specify their background, how they plan to use the loan, the maximum interest they want to pay, and how they plan to pay it back.” Then, private investors browse the listings. “It’s a lot more efficient than traditional financial institutions,” says Wenjun. “Borrowers get lower interest and investors get better return, but they have to be really selective. We studied how investors can assess the risk for the individual loans out there based on similar loans in the past.”

In the overwhelming world of big data, Wenjun sees a need for practical focus. “A lot of new discoveries happen when you really understand the domain, so you need to know what’s meaningful in practice in order to better motivate your research and develop a solution that’s going to be used,” she says. “It’s an integration of theory and practice.”