Hey! Glad you stopped by.
A bit about me
I’m a biostatistician and data scientist specializing in statistical methods for neuroimaging and high-dimensional data. I’m currently an Assistant Professor of Clinical Biostatistics (in Psychiatry) in Columbia University’s Mental Health Data Science Division, and a Research Scientist at the New York State Psychiatric Institute. I previously received my PhD in Biostatistics from the University of Pennsylvania, and my BS in Psychology from Haverford College.
Then and now, I have always sought to produce cross-cutting and interdisciplinary work (so please reach out if you’d like to chat or brainstorm collaborations!). Some of my favorite recent team-ups have included developing statistical methods for multiple sclerosis research with Taki Shinohara, investigating inequities in scientific citation practices with Dani Bassett and Perry Zurn, and delving into job automation and skill networks with the folks at The Pudding. You can find a few examples of my most recent work below, and a more comprensive list on the research and projects pages.
Side Projects
Lesion quantification toolkit
The lesion quantification toolkit (LQT) is a publicly available software package for quantifying the probabilistic impacts of focal brain lesions on structural connectivity.
Read moreSelected Publications
Structural brain measures among children with and without ADHD: A cross-sectional US population-based study
{The Lancet Psychiatry, 2022}
Read moreMy Academic Research
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.
Read moreFeatured categories
Meta-science (5) Neuroimaging methods (4) Applied neuroimaging (3)
