.jpg "Brianna M. Goodale, PhD photo")
Head of Data Science
Julius Clinical Research
As Head of Data Science for Julius Clinical Research, Brianna Goodale, PhD leverages more than fifteen years of experience conducting clinical research to bring modern machine learning and advanced statistical methods to healthcare, vaccine, and drug research. She earned her PhD in quantitative and social psychology from the University of California, Los Angeles in 2018 after graduating with a BA from Harvard University in 2009. Drawn to complex datasets, Dr. Goodale pursued a graduate-level Data Science internship with a wearable device company in 2017 before joining their team full time in 2018. As a Clinical Data Scientist, she authored the clinical documentation required by European and American regulatory authorities to demonstrate the validity of Software as a Medical Device (SaMD) algorithms predicting women’s real-time fertility.
Dr. Goodale has also built a substantial clinical portfolio. She has led several retrospective data mining initiatives of Real World Evidence, conducted multiple systematic reviews, and contributed to internally and externally sponsored research projects. Serving on the Management Team for the EU-funded COVID-RED project, Dr. Goodale oversaw the development of a novel machine learning algorithm and complementary app that could detect and alert the user in real-time to physiological changes associated with COVID-19 prior to symptom onset. The COVID-RED study, recruiting more than 17,000 subjects, demonstrated the algorithm’s superior performance and decreased time to testing compared to symptom-based standard of care.
Since joining Julius Clinical’s Department of Digital Innovation and Clinical Development in November 2022, Dr. Goodale has helped launch a multi-country post-market surveillance study for COVID-19 vaccine safety through VAC4EU. She and her team are also involved in initiatives to bring machine learning and artificial intelligence to risk-based monitoring and project management. Dr. Goodale joins machine learning with traditional epidemiological approaches to investigate research questions related to infectious disease, cardiometabolics, women’s health, and CNS.
