Interview with Dr. Yi Zhang

Faculty of Engineering and Information Technology, University of Technology Sydney, Australia

Collaborations are highly valued in academia, government and industry. But how do researchers and organizations know if they’re selecting the best collaborators? Traditional bibliometrics generates co-authorship networks that enable visualizations of groups who might work well together. But Yi Zhang is taking bibliometrics to the next level, using advanced technologies to not only identify, but to actually predict, potential collaborators as well as competitors. "This is just one example of how we use ‘intelligent biometrics,’ defined as the development and application of intelligent models for recognizing patterns in bibliometrics,’ to support innovation," Yi explains.

"In the larger picture, we map the collaborative and competitive space around a researcher," Yi says. "We use link prediction to find missing links among nodes in a network, sometimes illuminating darker corners of the network that researchers didn’t know about. For example, we might find two researchers who haven’t coauthored any papers, but have very similar research interests and partnerships. We can predict that they would make good collaborators going forward."

"We can also uncover collaboration patterns in a specific field, and relationships among different disciplines – i.e., multidisciplinary interactions – that might lead to successful team efforts."

Intelligent bibliometrics projects are requested by governments interested in data to inform science technology and innovation policy (STIP) and decision making, Yi says. "By contrast, companies may request a project if they’re thinking about mergers and acquisitions – i.e., they want to identify other companies that hold similar research and technical interests."

Yi’s interest in bibliometrics, scientometrics, and informetrics began when, as a PhD candidate in management science and engineering at Beijing Institute of Technology, he worked as a visiting scholar at Georgia Institute of Technology in the U.S. Under the mentorship of Alan Porter, PhD, Co-director of the Program in Science, Technology & Innovation Policy, he realized that bibliometrics enables insights from scientific publications and patents that might somehow be used to forecast innovation. Several years later, Yi was encouraged to enroll in a second PhD program, in software engineering.

"Part of the motivation for me to enroll in the program was the realization that while we use bibliometrics to analyze scientific publications, we often don’t have expertise in IT,” Yi recalls. “This means we don’t know how to handle large-scale data analytics or how to develop new algorithms to further analyze certain topics of interest. So, while earning my second PhD, I tried to use those information technologies, as well as artificial intelligence, to enhance existing bibliometric tools and gain more insights."

As a member of the ICSR Advisory Board, Yi looks forward to sharing his IT expertise with other members who are working on moving beyond traditional bibliometrics to help solve STIP issues, and to confront emerging bibliometrics challenges involving big data, social media and artificial intelligence.

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Yi Zhang is a Lecturer at the UTS Centre for Artificial Intelligence. He holds dual PhD degrees in Management Science and Engineering (Beijing Institute of Technology) and in Software Engineering (University of Technology Sydney). He is the recipient of the 2019 Discovery Early Career Researcher Award from the Australian Research Council, and an investigator for six research programs that received grants from the Natural Science Foundation of China, and several industry programs in Australia and China. He was a visiting scholar at the School of Public Policy, Georgia Institute of Technology from 2011 to 2012.