Dissertation Defense - University of Houston
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Dissertation Defense

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

Mohammed Emtiaz Ahmed

will defend his dissertation

Modeling Convergence Undercurrents in Brain Science via Statistics and Machine Learning


Multi-disciplinary research calls for multi-disciplinary collaborations, an integration process known as convergence. There is broad consensus that convergence research is the best way to go about solving complex problems on the frontier of science. The brain question exemplifies such a problem, and relevant funding initiatives the world over were conceived as convergence incubators. Accordingly, by nature and by policy, brain research qualifies as a unique live experiment in convergence, holding clues to new forms of science. Hereby considering jointly the disciplinary pedigree of the authors with the subject areas of nearly one million brain science publications, we unearthed heretofore unknown convergence undercurrents. The results suggest that science integration does not only take place between researchers from different disciplines, but also within researchers. In fact, there is an increasing tendency for brain researchers to tackle subject areas beyond their core expertise, especially when these areas are epistemically close. This expansive behavioral trend appears to precipitate and compete with true convergence, although it is clearly less impactful. Disturbingly, such a shortcut to convergence is aided by the funding initiatives, in spite of their expressed objectives to the contrary. All together, these results highlight the importance of using new methods to analyze convergence, revealing the interplay between high-minded science policies and the creative human behaviors generated in response. In its ascending path to integration, 21st-century science appears to get entangled in a formative vector field with hidden variables that beg for nuanced modeling. Regarding the content of the research publications in our corpus, in addition to linear model analysis based on Medical Subject Headings (MeSH) keywords, we also applied Machine Learning (ML) analysis to the articles’ abstracts. The ML methods yielded results that either validated or complemented those of the linear models. Furthermore, ML furnished insights regarding the timing and source of transformative developments in brain science that elucidate the abstract conclusions of the linear models. Such insights include the role and effect of Magnetic Resonance (MR) imaging and data analytic methods in brain science advancements.

Date: Tuesday, July 14, 2020
Time: 10:00 AM - 12:00 PM
Place: Online Presentation - MS Teams
Advisor: Dr. Ioannis Pavlidis

Faculty, students, and the general public are invited.