2018 Anita Borg Systers Pass It On Award Winner
Project Title: MedHere: Medication Adherence System for Low Income Women
Diane is a mother of two children and a second-year assistant professor at the Data Science Master’s Program at the University of San Francisco (USF). She received her master’s and doctorate degrees in Computer Science from the University of California, Los Angeles (UCLA), and her bachelor’s degree in Computer Science from Sogang University in South Korea.
While at UCLA, Diane worked on various wireless health monitoring projects to collect and analyze patients’ data in collaboration with the school of medicine, nursing, biomedical engineering, and electrical engineering at UCLA, UCI, UCSF, and Cedars Sinai. Many of the studies were focused on underrepresented populations such as low literacy Latino patients, former convicts, and women. Her research project, WANDA, became a commercialized solution (Wanda Inc).
Prior to joining University of San Francisco, Diane was a senior member of technical staff in the scalable analysis and visualization department at Sandia National Laboratories. She worked on various national mission domains including seismicity, remote sensing image analysis, power grid, medical data analysis, etc.
Diane has 18 peer-reviewed publications and four WO patents. She also has been mentoring students from Girl Scout – Northern California, a high school and college in New Mexico, and graduate students at UCLA and USF. She has been a member of Jesuit honor society, Alpha Sigma Nu since 2006. She is a recipient of NIH National Library of Medicine and best paper awards from Mobicase and ACM BigSpatial Workshop.
When Diane first became a parent, she and her husband were both in school, and did not have enough money to pay medical bills or prepare nutritious meals. They also could only visit their newborn daughter in the neonatal intensive care unit (NICU) during the designated meeting time, a task that is difficult for many parents who are working, have other children to take care of, or live far from the hospital. Luckily, several government programs were available to Diane and her husband. The Women, Infants, and Children (WIC) program gave vouchers to buy food, provided education about what, when, and how much to feed the baby, and gave monthly feedback. WIC also helped answer their questions and checked in with them to make sure they and their newborn were doing well.
Diane was inspired to help other parents going through similar problems, and thought of how her research could reduce communication gaps between healthcare providers and patients, and help hospitals monitor their patients’ health statuses after discharge with wireless devices.
A study by Bloomberg showed that only 18% of all U.S. undergraduate computer science students in 2018 were women. While many technologies are intended to be gender agnostic, they are mainly developed by male developers, resulting in products that are male-focused. In addition, technologies focused on females, especially low income pregnant women, receive little to no attention, and are largely absent from the marketplace.
Diane’s project, MedHere: Medication Adherence System for Low Income Women, would allow Diane to work with a group of students to design and develop a project where students can learn about social good and needs for data scientists and software engineers. Diane will mentor the students and show them how to use technology to help low income pregnant women. The project is designed to research and develop a medication adherence system that collects a subject’s motion data.
By using data-driven analysis, the students will develop a system that provides personalized feedback and aids the underserved population. The students will learn about technical skills of programming sensor devices and applying machine learning. More importantly, they will read articles and interview healthcare providers and patients to learn about issues of low medication adherence rates for low income pregnant women, and think about how technologies can impact a baby, his/her mother, a family, and the society. These students will be the start of reducing social inequality.