B.S. University of Southern California (2018)
M.S. University of Southern California (2018)
2nd Year MSTP
2nd Year Medical Student
I am most generally interested in computational and physical science approaches to studying diseases. My previous research involved translational work studying circulating tumor cells (CTCs) in cancer patients. I primarily focused on prostate cancer and examined the effects of anti-cancer drugs on genotypic and phenotypic diversity in the population of a patient’s CTCs.
My current interests lie in the application of machine learning to medical imaging data. Recent projects have included deep learning and radiomic analysis of chest x-rays in COVID-19 and the use of artificial intelligence to study radiotherapy in cancer. During my training I hope to develop computational techniques to make use of clinical and medical imaging data to improve patient outcomes.
Cowan, C.; Bae, J.; Singh, G.; Khullar, R.; Shah, S.; Madan, N.; Prasanna, P. Evolution of Chest Radiograph Radiomics and Association with Respiratory and Inflammatory Parameters in COVID-19 Patients Undergoing Prone Ventilation: Preliminary Findings; International Society for Optics and Photonics, 2021; Vol. 11597, p 1159709.
Bae, J.; Kapse, S.; Singh, G.; Phatak, T.; Green, J.; Madan, N.; Prasanna, P. Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study. In submission. arXiv:2007.08028 [cs, eess, q-bio] 2020.
Khullar, R.; Shah, S.; Singh, G.; Bae, J.; Gattu, R.; Jain, S.; Green, J.; Anandarangam, T.; Cohen, M.; Madan, N. Effects of Prone Ventilation on Oxygenation, Inflammation, and Lung Infiltrates in COVID-19 Related Acute Respiratory Distress Syndrome: A Retrospective Cohort Study. Journal of clinical medicine 2020, 9 (12), 4129.