Recently, Talha Oz successfully defended his doctoral dissertation proposal entitled “Collective Stress in the Digital Age.”
Talha Oz strives to understand various social and behavioral phenomena, especially by applying new kinds of methods to new kinds of data. Given that most social problems are complex, Talha believes that integration of a variety of theoretical tools in developing new social theories (or for testing existing ones) make them stronger. In this regard, he has educated himself in how to use new computational methods to address social scientific inquiries in the most appropriate ways. His work can mainly be grouped into four computational means: (1) social information retrieval and data mining, (2) social complexity and network analysis, (3) social simulation and agent-based modeling, and (4) online crowdsourcing and experimentation.
Talha is studying towards a Ph.D. in computational social science (CSS) in the Department of Computational & Data Sciences (CDS) at George Mason University (GMU).
Talha’s background is in computer science (CS); he holds two master’s degrees in CS, one with a focus on data mining and the other on internet measurements. Until Fall 2016, Talha worked in the Machine Learning and Inference Lab (MLI) within the Center for Discovery Science and Health Informatics (DSHI) for about five years.
For more information about Talha, check out his website: http://mason.gmu.edu/~toz/