Date: Tuesday, December 12, 2023

Time: 1:30 - 3:00 pm EST

Location: (in-person) Midtown - Coda 12th floor; (virtual) https://gatech.zoom.us/j/99690477837

Qihang Yao

PhD Student in Computer Science

College of Computing

Georgia Institute of Technology

Committee

Dr. Constantine Dovrolis (Advisor, School of Computer Science, Georgia Institute of Technology)

Dr. Kelly F. Ethun (Emory National Primate Research Center / School of Medicine, Emory University)

Dr. B. Aditya Prakash (School of Computational Science and Engineering, Georgia Institute of Technology)

Dr. Srijan Kumar (School of Computational Science and Engineering, Georgia Institute of Technology)

Dr. Nabil Imam (School of Computational Science and Engineering, Georgia Institute of Technology)

Abstract

This thesis proposes an innovative method to monitor social dynamics in rhesus macaque breeding colonies, leveraging automated feeder-generated feeding records. Addressing the limitations of traditional, labor-intensive in-person behavioral observations, the study aims to use these records to detect community structures and dominance hierarchies, thereby predicting social instability and aggression. First, we introduce a methodology to define and validate feeding interactions, which form the basis of a Feeding Interaction Network (FIN). Second, a two-step approach is proposed to detect overlapping communities within the FIN, starting with benchmarking non-overlapping community detection algorithms and then applying these to a link graph version of the FIN. Third, we utilize dominance-related feeding interactions in hierarchy inference.

These feeding interactions, characterized by their higher temporal frequency and coverage for all animal pairs, will enhance traditional behavioral data, providing a more comprehensive and timely inference of the hierarchical structure. Finally, we identify dynamic patterns in the feeding interaction, community structure, and dominance hierarchy that are associated with social instability and aggression, aiming to proactively manage social dynamics, reduce aggression, and improve group cohesion. In summary, this thesis aims to deliver a comprehensive, data-driven framework for monitoring and understanding the intricate social dynamics of rhesus macaque breeding groups.