Statistical methods for diagnosis and screening using neuroimaging data

The use of magnetic resonance imaging (MRI) for detecting disease-related pathologies can be hampered by the infeasibility of manual inspection. For visible structural pathologies, rigid criteria can lead to high time burdens on already over-burdened clinicians. For diffuse, unobservable, or multi-modal pathologies, it may be difficult or impossible for a clinician to obtain accurate visual assessments. To enable faster and more powerful detection of pathologies in brain tissue, my colleagues and I work on developing data-driven statistical methods that can make probabilistic and inferential conclusions about the occurrence of tissue abnormalities, and can reveal links to disease status or patient characteristics.

Structures of innovation and inequity in scientific research

As the academic community becomes more complex and interconnected, the generation of innovative ideas will increasingly rely on researchers forming new connections to historically marginalized voices and historically distant fields. To gain a better understanding of the scientific process, my colleagues and I have investigated the relationships between scientists and scientific papers. Using novel network scientific approaches, this work has revealed the importance of interdisciplinary links and uncovered pervasive gender- and race-based inequity in citation practices. Ongoing projects seek to expand this line of research into new scientific disciplines and dimensions of bias, and to explore potential mechanisms for mitigating such inequities.

Social and environmental determinants of health

Disentangling complex social, environmental, and biological processes is critical for improving health. Working with collaborators across Columbia’s Department of Psychiatry, I have sought to apply rigorous statistical approaches to studies investigating the social underpinnings of mental and physical health. These projects have covered a range of social and environmental factors, included air pollution, prenatal exposures, childhood socioeconomic status, COVID-19, and interpersonal stigma and discrimination. Across contexts, they have found social and environmental factors to be relevant for myriad outcomes, including sleep, neurological development, and mental health.