In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Dinesh Majeti

will defend his dissertation

Cross-disciplinary Dynamics and Scalable Visualization of Academic Careers

Abstract

Borne out of the Human Genome Project (HGP), the field of genomics evolved into a dominant scientific and business force. While other efforts were intent on estimating the economic impact of the genomics revolution, we shift focus to the social and cultural capital generated by bridging together biology and computing – two of genomics’ constitutional disciplines. We measured the impact of cross-disciplinarity from three interlocking perspectives: interpersonal collaboration over time, mixed authorship in scholarly products, and mixed methods in premium articles. Our results show: First, research featuring cross-disciplinary (XD) collaborations has higher citation impact than other disciplinary research – both at the career and individual article level. Second, genomics articles featuring XD methods tend to have higher citation impact than epistemically pure articles. Third, XD researchers of computing pedigree are drawn to the biology culture. This statistical evidence acquires deeper meaning when viewed against the organizational and knowledge transfer mechanisms revealed by the data models. With cross-disciplinary initiatives set to dominate the agenda of funding agencies, our case study provides a framework for appreciating their long-term effects on science and its standard-bearers. While these curated datasets are valuable for advanced analytics, they also provide an opportunity to create a unique front-end interface for academic careers. In this context, we made an effort to develop a compact visualization for academics. The ability to fairly assess academic performance is critical to rewarding academic merit, charting academic policy, and promoting science. Quintessential to performing these functions is the ability to collect valid and current data through increasingly automated online interfaces. Moreover, it is crucial to remove disciplinary and other biases from these data, presenting them in ways that support insightful analysis at various levels. Existing systems fail in some of these respects. Here we present Scholar Plot (SP), an interface that harvests bibliographic and research funding data from online sources, unbiases the collected data, and combines them synergistically in a plot form for expert appraisal, and a pictorial form for broader consumption.

Date: Friday, July 20, 2018
Time: 2:30 PM
Place: HBS 315
Advisors: Dr. Ioannis Pavlidis

Faculty, students, and the general public are invited.