“My career path has been shaped by a growing curiosity about improving the technologies and their applications in digital platforms,” said Jingwen Zhang, a new assistant professor in the Department of Business administration who joins Gies Business after completing a PhD in information systems at the University of Washington.

After receiving her undergraduate degree from the School of Economics, Zhejiang University, Zhang saw that most of her peers were drawn to the corporate world – consulting and like – following their education; Zhang, however, was fascinated by the impact that emerging technologies were having on business, and she wanted to pursue that interest.

“That interest led me to explore more interdisciplinary questions, and ultimately to pivot into the field of information systems,” she said. “Also, I am very fascinated by how to improve those technologies to make a better life for all the people. I found that information systems is a perfect interdisciplinary area where I can explore the technology in the border in a broader sense.”

Pioneering research on combatting estimation bias

Zhang’s research interests lie at the intersection of causal inference, machine learning, and digital platforms. “I use a combination of methodologies – including econometrics and machine learning techniques – to study how to advance technology which can improve business decisions,” she said.

“What excites me most is the dual opportunity to understand how emerging technologies impact real-world business outcomes and to contribute to improving those technologies through rigorous research. My interest stems from a desire to bridge the gap between technical innovation and societal impact, which naturally led me to this interdisciplinary area.”

Recent work by Zhang and her colleagues focuses on how to correct estimation bias (which could result in either over- or underestimation) when utilizing machine learning or AI technologies. In the study titled "Debiasing ML- or AI-Generated Regressors in Partially Linear Models" Zhang and her coauthors Wendao Xue and Yifan Yu (University of Texas Austin), and Yong Tan (University of Washington) developed new estimators to overcome estimation bias when utilizing machine learning or AI technologies for their econometric analysis.

Through their work, Zhang and her coauthors developed an estimation tool that surpasses those methods currently in use. “By utilizing our estimator to correct that bias, the estimation would be more accurate, and the decision-making would be better,” Zhang said. “Our estimator accommodates the machine learning or AI-generated variables in metric analysis. We found that our estimator can outperform the other existing estimators.”

Teaching business analytics

In addition to continuing her research, Zhang is enthusiastic about her teaching opportunities. This fall she will be teaching BADM 211: Business Analytics II and she looks forward to sharing her expertise with Gies Business students. "I love teaching," she said. "There's nothing more rewarding than seeing students make progress, and I genuinely enjoy the interactions and discussions we have."

In the course, students will learn and utilize the Python programming language to develop basic techniques for business data analysis.

Attracted by the Gies Business culture

Zhang said that the culture of Gies Business is something that she is particularly excited to be part of. “I’m drawn to Gies College of Business because of its strong academic reputation and collaborative, collegial environment,” she said. “The faculty comprises leading scholars who are conducting pioneering work across diverse areas of business. I deeply respect their contributions to the field and am eager to learn from such accomplished colleagues.”