Spotlight on Alum Ke Yang ('21)

When Assistant Professor Julia Stoyanovich left her post at Drexel University to come to NYU Tandon in 2018, she brought with her deep expertise in data and knowledge management. She also brought along doctoral student Ke Yang, who decided to move from Philadelphia to New York in order to continue working with her mentor and advisor.

“I realized that switching to a new school in the middle of my doctoral studies could mean that it would take extra time to earn my Ph.D.,” Ke says. “But there was no question in my mind that I wanted to pursue the research I had begun under her direction.”

Like Stoyanovich, who heads Tandon’s Center for Responsible AI and is a member of the Visualization and Data Analytics (VIDA) Research Center, Ke is particularly concerned about the fairness, transparency, and explainability of algorithmic systems that leverage large data sets–such as those used by HR departments to aid in hiring decisions or by courts to predict recidivism.

“If an AI-based system is trained using biased data, the system itself will exhibit bias,” she explains. “That can have negative social impacts on the day-to-day lives of people who might not even realize that automated decision-making tools are being used by the banks determining whether or not they get credit, the landlord deciding if they would be good tenants or even the medical practitioner diagnosing their condition.”

Ke, who earned her bachelor’s and master’s degrees at Beijing Technology and Business University, collaborated with Stoyanovich on several major projects while at Tandon, including developing an open-source library that helps data scientists incorporate fairness-enhancing interventions into complex pipelines and explaining the methodology and potential issues of ranked outcomes to the general audience. She is now a Postdoctoral Research Associate at the University of Massachusetts College of Information and Computer Sciences, in Amherst, where she works with Associate Professor Alexandra Meliou on interpretable machine learning. 

“Having strong female role models like Professors Stoyanovich and Meliou has had a major impact on my life,” Ke says. “I hope that when my postdoctoral studies are complete and I embark upon my own career in academia, I can be a similar inspiration to other women students in STEM.”