Male mice song syntax depends on social contexts and influences female preferences

My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more.  We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers.  We are also interested in new inference framework and in studying theoretical properties of methods we develop.  

Some highlight application areas: 
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc.  Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.

(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.

(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease.  This includes an emphasis on infectious disease modeling, such as COVID-19.

Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).

Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. They use an integrative approach that combines behavioral, anatomical, electrophysiological and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The generality of the discoveries is tested in other vocal learning orders, such as parrots and hummingbirds, as well as non-vocal learners, such as pigeons and non-human primates. Some of the questions require performing behavior/molecular biology experiments in freely ranging animals, such as hummingbirds in tropical forest of Brazil. Recent results show that in songbirds, parrots and hummingbirds, perception and production of song are accompanied by anatomically distinct patterns of gene expression. All three groups were found to exhibit vocally-activated gene expression in exactly 7 forebrain nuclei that are very similar to each other. These structures for vocal learning and production are thought to have evolved independently within the past 70 million years, since they are absent from interrelated non-vocal learning orders. One structure, Area X of the basal ganglia's striatum in songbirds, shows large differential gene activation depending on the social context in which the bird sings. These differences may reflect a semantic content of song, perhaps similar to human language.

The overall goal of the research is to advance knowledge of the neural mechanisms for vocal learning and basic mechanisms of brain function. These goals are further achieved by combined collaborative efforts with the laboratories of Drs. Mooney and Nowicki at Duke University, who study respectively behavior and electrophysiological aspects of songbird vocal communication.