Computer Science Ph.D. Thesis Proposal
— 3:30pm
Location:
In Person and Virtual - ET
-
Traffic21 Classroom, Gates Hillman 6501 and Zoom
Speaker:
ANANYA JOSHI
,
Ph.D. Student, Computer Science Department, Carnegie Mellon University
https://ananyajoshi.com/
Identifying Anomalous Events in Modern Public Health Data Streams
Public health data aggregators publish millions of data points across many data streams, like the daily number of influenza cases, hospitalizations, and deaths per county and state in the United States. So that their users, including public health experts, do not draw erroneous conclusions, these aggregators must identify noteworthy changes in their data, including those that result from data errors and outbreaks. However, given increasing data volumes and limited numbers of data reviewers, aggregators can only have some of their data inspected manually.
This thesis introduces a human-in-the-loop framework for public health data aggregators to inspect their data given their reviewer resources. Currently, an automated method ranks each new data point in context, with a correction for high-volume settings, so that reviewer attention is focused on the most noteworthy data. Still, reviewers need to investigate hundreds of data points from the ranked list before being able to understand the scope of the impacted data. Accordingly, we propose including an automated module that jointly identifies multiple data points that are noteworthy in context.
Thesis Committee:
Roni Rosenfeld (Co-Chair)
Bryan Wilder (Co-Chair)
Rayid Ghani
Matt Biggerstaff (Center for Disease Control and Prevention-CDC)
In-person and Zoom Participation. See announcement.