Research

ML/AI
Machine learning (ML) techniques offer our best bet at interpreting audio signals in other species: they provide rigorous strategies for identifying salient acoustic features, facilitate transfer of pre-trained models between data sets (thereby hugely enhancing efficiency and accuracy), and allow for analyses of enormous datasets, which is necessary for identifying and understanding large-scale syntactical patterns used across populations and species. Combined with ethological techniques for interpreting animal social behavior and community science approaches for data collection, ML/AI tools can help us to decipher the information embedded with animal signals and reveal underlying similarities with human language.

​During my PhD, I developed an unsupervised ML approach to identify patterns and underlying structure within songs used by British birds (Parus major), revealing that song sharing amongst individuals was inversely correlated with geographic distance between nests and that birds that had recently immigrated into the populated used more complex songs that were rarely passed on to birds in the next generation. This work suggests that social environment shapes bird communication on multiple levels; although nearby birds are often "tutors" for young birds learning songs, additional social and ecological factors may ultimately determine which songs remain in the population during generational turnover. In my current research, I continue to use both supervised and unsupervised ML techniques to study animal communication.

​Cultural evolution of vocal signals
The vocal signals used within a population often change over time due to random drift, copying errors, innovation of new signals, and/or introduction of new signals. I'm particularly interested in exploring the relationship between patterns of migration and the change in song usage over time, and understanding to what degree the movement of individuals drives cultural evolution and the formation of dialects.

​Social learning
Patterns of social learning within a population can determine which vocal signals are passed onto subsequent generations and thus strongly influence which signals are most commonly used and which are lost. Among song birds, selection may act upon individuals to enable rapid recognition and acquisition of vocal signals from others, often resulting in birds from the same populations singing the same songs. In mixed species communities, social learning can also enable rapid acquisition of information about food and predators from heterospecifics that occupy a similar ecological niche. My research aims to investigate how social environment shapes vocal communication and how social learning both within and between species can facilitate coordinated behaviors and the establishment of new traditions.

Cooperation ​and recognition
Group or individual "signatures" can allow for recognition and facilitate cooperation in different species, particularly among cooperative breeders as well as socially monogamous species which forage together during the non-breeding season. Through my work I hope to increase our understanding the behavioral and physiological processes which govern vocal recognition and convergence on similar vocalizations.