Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
The relationships between individual behavior and broader-scale societal structures have been central to a range of human phenomena, from job and migration decisions to political participation to the experience of a specific health-related event. Recent progress in predictive analytics along with big data offers a powerful way to explore those relationships but often gives rise to the "black box" problem with little insight into what goes on in the algorithmically learned relationships. In this talk, Dr. Lin will present a spatiotemporal learning approach that leverages a deep learning framework with relevant social theories to help examine the relationship between human-societal activity and their social and geographical contexts, with applications including predicting political protest and opioid overdose events. The approach is not only capable of forecasting the occurrence of future events, but also provides theory-relevant interpretations -- it allows for interpreting what features, from which places, have significant contributions on the forecasting model, as well as how they make those contributions.
Last Updated On Tuesday, April 02, 2019 by Crow, Sharon Weber
Created On Tuesday, April 02, 2019