Dr. Elizabeth Sweeney is an assistant professor of healthcare policy and research in the Division of Biostatistics and Epidemiology. Dr. Sweeney received her Ph.D. in biostatistics from the Johns Hopkins Bloomberg School of Public Health. During her PhD, she completed an internship in image analysis in multiple sclerosis at the National Institute of Neurological Disorders and Stroke. She did her postdoctoral fellowship at Rice University. Dr. Sweeney previously worked in the healthcare tech industry as a senior quantitative scientist at Flatiron Health and a senior data scientist at Covera Health.
What got you interested in biostatistics?
After completing my undergraduate studies in math, I wanted to work on problems that would impact people and health. I decided to go into biostatistics and did a Master’s and Ph.D. at Johns Hopkins. While I was there, I also interned at the NIH and started working on neuroimaging data – that’s where my research interests got ignited. I love getting a fresh data set and exploring the ins and outs – how the variables are related and what secrets the data holds. Then I took a turn in my career. Having been in academia for a number of years, I was curious about what it would be like to work in industry. I came to New York and started at Flatiron Health, a healthcare tech company that does electronic medical records research. I also worked at Covera Health, a company that focused on routing patients to high-quality radiologists. But after a few years, I started missing academia and working on neuroimaging data and decided to come back to academia.
Tell us about your research
I work on the analysis of structural magnetic resonance image (MRI) in multiple sclerosis (MS). Patients with MS have lesions in their brains which MRI is sensitive to. In order to meet the diagnostic criteria for MS, patients must have different lesions observed at different time points, requiring multiple MRI scans or MRI scans with an invasive intravenous contrast agent called gadolinium. One project that I am really excited about is an automated algorithm to determine the age of the lesions from a single scan, eliminating the need for patients to get multiple scans or to use gadolinium. I also work on automated algorithms to determine the location of lesions in the brain, helping to monitor progression of the disease and to aid in research applications.In addition to my work with MRI in MS, I also work with the Radiology Department on collaborative projects. These projects vary across many different areas. For example, in one project we worked on the diagnosis of chronic fatigue syndrome and differentiating it from depression using a marker measured through brain imaging. In another project we modeled the relationship between weight loss and imaging markers in a group of patients who had a new experimental weight loss procedure.
What brings you to Weill Cornell Medicine?
One of the many reasons I chose HPR at Weill Cornell was the opportunity for amazing collaborations with the Radiology Department. My goal is to establish a large line of imaging research, which I feel confident I can do with the collaborations here at Weill Cornell. I am excited to work alongside my division chief, Dr. Karla Ballman and the other great faculty in the department. I am also very excited about teaching a new data science course in the Master’s program this semester. In the future, I’d like to get more opportunities to mentor post-docs and students. I want to share my passion for working with imaging data. HPR is a great fit for me.