Doctoral Thesis Oral Defense - Ananya Ashish Joshi

— 5:30pm

Location:
In Person and Virtual - ET - Newell-Simon 3305 and Zoom

Speaker:
ANANYA ASHISH JOSHI, Ph.D. Candidate, Computer Science Department, Carnegie Mellon University
https://ananyajoshi.com/

Growing volumes of public health-related data render standard techniques for syndromic surveillance (designed for smaller data volumes) obsolete. My thesis presents a practical approach for experts to monitor large-scale aggregate public health data. These novel big data monitoring methods identify data corresponding to quality issues or changes in disease dynamics and are simple, scalable, generalizable, and shown to be accurate in real-world settings based on human-labeled data. When paired with custom user interfaces, these methods have led to a 53-fold increase in monitoring efficiency for data experts at the Delphi Group at Carnegie Mellon University. Experts can now detect over 200 noteworthy data issues from 15 million new data points each week. The output of this thesis' monitoring approach can directly support public health surveillance, especially at the state or national level, and increase the utility of public health data modernization efforts for data-driven decision-making.  

Thesis Committee

Roni Rosenfeld (Co-Chair)
Bryan Wilder (Co-Chair)
Rayid Ghani
Matthew Biggerstaff (Centers for Disease Control and Prevention)

In Person and Zoom Participation.  See announcement.

Add event to Google
Add event to iCal