Recent years have seen the increasing availability of big scholarly data, such as Microsoft Academic Graph and Semantic Scholar, that record detailed publishing, granting, and patenting activities in the science and technology ecosystem. Science of science is an (re-)emerging field that uses those large data sets to study the workings underlying the practice of science and technology. In this talk, I will present some of my recent works along this line. I will particularly focus on how combining notions from translational medicine and network representation learning algorithms allows me to propose a method that quantifies the “basicness” of biomedical research, which further paves a way to study knowledge flows from science to technology. I will conclude my talk by discussing ongoing work and future research directions.
Speaker: Dr Qing KE
Date: 10 February 2021 (Wed)
Time: 11:00am – 12:00pm
Poster: Click here
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Biography
Dr. Qing Ke is a Postdoctoral Associate at the Science of Science & Computational Discovery Lab at Syracuse University School of Information Studies. Previously he was a postdoc at the Center for Complex Network Research at Northeastern University, after completing his PhD in Informatics at Indiana University, Bloomington in 2018. His research leverages big data to explore a wide range of topics related to science, technology, innovation, and entrepreneurship. His research takes an interdisciplinary approach, by developing and integrating quantitative methods with social science theories. He has published in diverse venues including Nature, PNAS, Research Policy, Journal of the American Medical Informatics Association, and the CSCW conference. His work was awarded Best Paper Honorable Mentions at CSCW and received world-wide media coverages such as Nature News, the New York Times, Scientific American, etc. He is also interested in data visualizations and produced one that was featured as Nature cover.