Dissertation Proposal - University of Houston
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Dissertation Proposal

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

Mohammed Emtiaz Ahmed

will defend his proposal

Predicting Brain Science Advances Through ML/Statistical Modeling of Cross-disciplinarity


Brain science lies at the confluence of several disciplines and is considered the next research frontier. The solution to mental health, neurodegenerative diseases, and other open problems of grave importance hinge on brain science advances. To this end, policy makers are scrambling to develop programs that will usher us to an era of brain science breakthroughs. Two well-known existing programs are the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative in the U.S. and the Human Brain Project (HBP) in Europe. What is the policy mix that would make such programs succeed? The underlying hypothesis of this investigation is that cross-disciplinarity plays a constitutional role in brain science advances. Accordingly, the key aim is to model the formative processes and impact of cross-disciplinarity in brain science, a development that will bring predictive capabilities to researchers and managing capabilities to policymakers. The study design views brain science research as a multi-stage process. It includes the policy stage where funding priorities are set; this spurs activity at the behavioral stage where academics move to form teams in pursuance of research programs; these teams create intellectual products, some of which are breakthroughs bound to affect scientific growth; and, a few of these breakthroughs end up spawning new economic sectors. Within each stage, linear models are used to assess the role of cross-disciplinarity and other factors in successful outcomes. These stage-centric models act as feature selectors for an inter-stage machine learning engine, which commands a holistic view of the problem and is capable of addressing non-linear interactions between stages. The said methods operate upon a cross-linked ensemble of datasets, including departmental affiliations of STEM academics in U.S., grant funding data from the federal RePORTER and the European Research Council, and bibliographic data from Scopus.

Date: Tuesday, November 12, 2019
Time: 11:00 AM - 1:00 PM
Place: HBS 1, Conference Room 302
Advisor: Dr. Ioannis Pavlidis

Faculty, students, and the general public are invited.