Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts.

Dr. Jana Schaich Borg uses neuroscience, computational modeling, and emerging technologies to study how we make social decisions that influence, or that are influenced by, other people.  As a neuroscientist, she employs neuroimaging, ECOG, simultaneous electrophysiological recordings in rats, and computational analysis of video interactions to gain insight into how we make social decisions.  As a data scientist, she works on interdisciplinary teams to develop new statistical approaches to analyze these high-dimensional multi-modal data to uncover principles of how the brain integrates complex social information with internal representations of value to motivate social actions.

Dr. Schaich Borg’s most current research projects focus on developing Moral Artificial Intelligences and understanding the role of “social synchrony” in empathy, social connection, psychiatric disease, and mental health.  Issues related to these research projects have led her become involved in efforts to develop practical strategies for ethical AI development, and passionate about initiatives to use storytelling and data visualization to communicate the impact of complex analytical problems to diverse audiences.

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).