Jenna Young, Ph.D. Data Analyst - Brain-Gene Development Lab
Jenna Young (previously Schabdach) is a data analyst working with Dr. Aaron Alexander-Bloch in the Brain-Gene-Development Lab.
She graduated from Drexel University in 2016 with B.S. and M.S. degrees in Electrical Engineering. A desire to learn more about medical imaging and computer vision drove her to pursue a Ph. D. in Biomedical Informatics at the University of Pittsburgh. During her Ph. D., she focused on the effects of motion and motion correction in functional MRIs. She graduated from Pitt in April 2020 and joined LiBI in November 2020.
While Jenna's main focus is on different MRI processing and analysis techniques, her interests also include other modalities of medical imaging, machine learning, natural language processing, and data management.
Schabdach J., Wells W.M., Cho M., Batmanghelich K.N. (2017) A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies. In: Niethammer M. et al. (eds) Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science, vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9\_14
Schabdach, J. M., Ceschin, R., Lee, V. K., Schmithorst, V., & Panigrahy, A. (2020). A Series Registration Framework to Recover Resting-State Functional Magnetic Resonance Data Degraded By Motion. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020, 569–578.