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Tamara Limber, Lurie Children’s fellow, is developing a pediatric sepsis-detection device
Summary
Tamara Limber, a Kobe Curie Fellow at Anne & Robert H. Lurie Children’s Hospital of Chicago, described work to build a medical device that uses machine learning to detect sepsis in children and said her research and background aim to reduce health-care disparities in underserved communities.
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Tamara Limber, a Kobe Curie Fellow at Anne & Robert H. Lurie Children’s Hospital of Chicago, said she is working on a medical device to detect sepsis in children. Limber described a research background in biomedical engineering and public health that she said informs the project.
Limber said her PhD research used machine learning to detect when a person with opioid use disorder or low blood volume is at risk of a medical crisis. She listed degrees from Cornell University (bachelor’s in biological engineering), Emory University (master’s in public health), the University of Illinois (master’s in bioengineering) and a joint PhD in biomedical engineering from the Georgia Institute of Technology and Emory University.
Limber said her motivation includes personal experience with income inequality and the loss of her parents to preventable diseases, and she cited exposure to poor health-care access while in Nicaragua and India. She said those experiences shaped her goal to create medical technologies that can reach underserved communities.
Limber said she plans to start a health-care company to make medical technology more widely available and cited prior recognition in research and startup circles, including selection as a New England computing and data science rising stars fellow, a finalist in the NIA startup challenge and a wearable-tech-ventures ambassador. She also said she has published peer-reviewed articles related to her work.
In closing, Limber noted hobbies including music, outdoor activities, travel and exercise. She framed the device work as part of a broader aim to reduce disparities in access to quality medical technology for marginalized groups.

