Hey! Glad you stopped by.
A bit about me
I am a biostatistician and computational scientist helping to lead the Federation of American Scientists’ new Impetus Institute for Metascience. My work focuses on building and strengthening ties between metascience practitioners and policymakers, with the ultimate goal of improving scientific research, funding, institutions, and incentive structures through experimentation.
Before joining FAS, I was an Assistant Professor of Clinical Biostatistics at Columbia University and a research scientist at the New York State Psychiatric Institute; my academic research touched on statistical methodology, medical imaging, and mental health. Throughout my career, I have aimed to produce cross-cutting and interdisciplinary work, and am always excited to connect about potential collaborations (so feel free to send me a note at jdworkin@fas.org!)
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 comprehensive 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
Summary metrics of memory subnetwork functional connectivity alterations in multiple sclerosis
{Multiple Sclerosis Journal, 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 (5) Applied neuroimaging (4)
