|Doctoral student, 2021-
|Jiangen He (2019 - 2020)
|Doctoral Candidate graduated in 2020, now Assistant Professor at University of Tennessee Knoxville
|Kai Li (2014 - 2020)
|Doctoral Candidate graduated in 22020, now Assistant Professor at University of Tennessee Knoxville
|Yongjun Zhu (2013 - 2017)
|Doctoral Candidate graduated in 2017, now Assistant Professor at Yonsei University, South Korea
|Aimee Turner (2017 - 2018)
|Undergraduate Student graduated in 2018
|Kathleen Padova (2014 - present)
|Doctoral Candidate graduated in 2021
|Palash Pandey (2018 - present)
|Mengnan Zhao (2015 - 2016)
|Gao Li (2015 - 2016)
|Visiting Scholar, Associate Professor, Central China Normal University, China
|Ruimin Ma (2015 - 2016)
|Visiting Scholar, Associate Professor, Shanxi University, China
|Xuelian Pan (2015 - 2017)
|Visiting Student, now Assistant Professor at Nanjing University
Informetrics and scientometrics. This area of research uses bibliographic and full-text data to understand scholarly communication, help make effective science policy, and sustain scientific research and workforce. In science, dynamics and interactivity are increasingly salient geographic, disciplinary, and social boundaries that once isolated scholars are becoming more permeable. Investigating patterns of scholarly communication in such a dynamic and interactive environment is crucial if we are to understand collaboration, innovation, impact, and scientific activity in general. Our main goal in this area is to study the dynamic and interactive nature of scholarly communication, and to investigate impact assessment, scientific collaboration patterns, and transformative innovation in an effort to advance scholarly communication and scientific communities.
Scholarly data mining and analysis. Publication and patent data embody the very essence of scientific and technological advancement. These data have been continuously examined in an ongoing multidisciplinary effort. The effort however, has largely been driven by the analysis of existing publication or patent metadata. Consequently, we have a limited understanding of the ways to analyze the content of individual papers or patents. As the variety of sources available for textual analysis is increasing, there is the need to incorporate content-aware analytics into the methodological repertoire. Mindful of these opportunities, our lab is engaged in the design of new methodologies to transform the metadata-driven mode of inquiry into a content-driven one for studies of scholarly data.
Knowledge diffusion studies. While work at the document level has delivered rich analyses of the science of science, knowledge is more effectively expressed through contents and texts. Thus, researchers and practitioners are demanding more fine-grained methods and tools to contextualize their findings and make sense of bibliometric indicators and numbers. It is thus our goal to use these textual data to create new knowledge and gain insights into knowledge production and diffusion. To this end, our research seeks to tackle the complexity of textual data and examine content-rich knowledge entities. Analysis of these entities-which constitute the essential cells of scientific literature and the building blocks of knowledge-will drastically accelerate our understanding of the process of knowledge production and diffusion. More specifically, the examination of content-rich entities will reveal the workings of the scientific enterprise at a new, finer-grained level, allowing us to pose and answer ever-deeper questions about the provenance, diffusion, coevolution, and impact of knowledge. This area of research will push the boundary of knowledge studies to the entity level and examine the creation and dissemination patterns of knowledge as codified by content-rich entities.