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Joseph Romano, PhD, MPhil, MA is an Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics, Senior Fellow in the Institute for Biomedical Informatics (IBI) and an investigator in the Center of Excellence in Environmental Toxicology (CEET).

Joseph D. Romano is an Assistant Professor of Informatics and Pharmacology in the University of Pennsylvania’s Perelman School of Medicine. He joined the faculty in 2023 after completing postdoctoral and […]

Dr. Yong Chen is Professor of Biostatistics at Department of Biostatistics, Epidemiology, and Informatics (DBEI), and a Senior Scholar at Center for Clinical Epidemiology & Biostatistics (CCEB) at the University […]

In 1991, the National Academies of Sciences, Engineering and Medicine declared that computer-based patient records were an essential technology for health care. The new records would not only support patient […]

The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence […]

Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research […]

Total brain white matter lesion volume is the most widely established MRI outcome measure in studies of multiple sclerosis. To estimate white matter lesion volume, there are a number of […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the […]

Sensitivity analysis and power for instrumental variable studies Wang X, Jiang Y, Zhang NR, Small DS. Link In observational studies to estimate treatment effects, unmeasured confounding is often a concern. […]

This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data, and demonstrates its ability to recover obscured longitudinal information. The proposed count works by incorporating information […]

PREVAIL: In this work, our lab developed a model for predicting the degree and spatial pattern of MS lesion tissue recovery. The model is based solely on lesion presentation at […]