Nova F. Smedley

PhD Candidate
  • UCLA Bioengineering

Office: 924 Westwood Blvd, Suite 420, Room P

E-Mail: novasmedley@ucla.edu

Biography

Nova is a PhD candidate in the Department of Bioengineering at the University of California, Los Angeles. She became a lab member in the Medical Imaging Informatics (MII) Group in 2014 and has been working on glioblastoma prognosis prediction and identifying radiogenomic associations. Her work has involved image processing, pattern mining, risk modeling, and machine learning. Nova has also worked with the Athena Breast Health Network to estimate breast cancer risk in screening populations at UCLA and fellow MII members on lung nodule diagnosis.

Nova is a recipient of the F31 fellowship from the National Cancer Institute (NCI) under the National Institutes of Health (NIH). Her dissertation on radiogenomics and neural networks is currently funded by the F31. She was previously funded by MII via two NIH grants: a T32 training grant from the National Institute of Biomedical Imaging and Bioengineering and a R01 research grant from NCI on glioblastoma.

Previously, Nova received a Bachelor of Science in biomedical engineering and a Minor in biology in 2013 from Rensselaer Polytechnic Institute. Her undergraduate degree focused on biomaterials, but she also studied cancer biology and has experience in cell culture. She spent two undergraduate summers doing stem cell research at a start-up and pediatric cancer research at a children's hospital. Nova was a graduate data science intern at Chegg in 2018.

Publications

2018
  1. Garcia-Gathright JI, Matiasz NJ, Adame C, Sarma KV, Sauer L, Smedley NF, Spiegel ML, Strunck J, Garon EB, Taira RK, Aberle D, Bui AAT. Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies. Computers in Biology and Medicine. 2018;92. DOI: 10.1016/j.compbiomed.2017.10.034. PDF
  2. Shiwen S, Smedley NF, Piedra EAR, Hsu W. Hybrid Hierarchical Model for Lung Cancer Prediction. International Symposium on Biomedical Imaging (ISBI). 2018.
  3. Smedley NF, Hsu W. Using deep neural networks for radiogenomic analysis. Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on [Internet]. Washington, D.C.: IEEE; 2018. p. 1529–1533. Retrieved from: https://ieeexplore.ieee.org/document/8363864 DOI: 10.1109/ISBI.2018.8363864. PDF
  4. Nova F Smedley TFC Benjamin M Ellingson, Hsu W. Longitudinal Patterns in Clinical and Imaging Measurements Predict Residual Survival in Glioblastoma Patients. Scientific Reports [Internet]. Nature Publishing Group; 2018;8(1):14429. Retrieved from: https://www.nature.com/articles/s41598-018-32397-z DOI: 10.1038/s41598-018-32397-z. PDF
2015
  1. Smedley NF, Chau N, Petruse A, Bui AAT, Naeim A, Hsu W. A platform for generating and validating breast risk models from clinical data: Towards patient-centered risk stratified screening. AMIA Annu Symp Proc. San Francisco, CA, USA; 2015.