Joseph Bae

Image: Joseph BaeJoseph Bae


B.S. University of Southern California (2018)

M.S. University of Southern California (2018)

Current Position:

4th Year MSTP

2nd Year Graduate Student


Prateek Prasanna

Graduate Program:


Research Interest:

I am most generally interested in computational and physical science approaches to studying cancer. My previous research involved translational work studying circulating tumor cells (CTCs) for solid tumor 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 ongoing projects focus on the application of cutting-edge computer vision approaches in multiple cancers including those of the lung, brain, and breast. The development of clinically impactful machine learning tools in addition to robust methods for evaluating those tools are key priorities in my research training.  You can read more about my work at


Journal Papers

Bae, J.; Kapse, S.; Singh, G.; Gattu, R.; Ali, S.; Shah, N.; Marshall, C.; Pierce, J.; Phatak, T.; Gupta, A.; Green, J.; Madan, N.; Prasanna, P. Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study. Diagnostics 2021.

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.

Conference Papers

Bae, J.; Cattell, R.; Zabrocka, E.; Roberson, J.; Payne, D.; Mani, K.; Prasanna, P. Pre-Treatment Radiomics from Radiotherapy Dose Regions Predict Distant Brain Metastases in Stereotactic Radiosurgery. In Medical Imaging 2022: Physics of Medical Imaging; SPIE, 2022.

Konwer, A.; Xu, X.; Bae, J.; Chen, C.; Prasanna, P. Temporal Context Matters: Enhancing Single Image Prediction With Disease Progression Representations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.

Konwer, A.; Bae, J.; Singh, G.; Gattu, R.; Ali, S.; Green, J.; Phatak, T.; Gupta, A.; Chen, C.; Saltz, J.; Prasanna, P. Predicting COVID-19 Lung Infiltrate Progression on Chest Radiographs Using Spatio-Temporal LSTM Based Encoder-Decoder Network; Medical Vision with Deep Learning 2021.

Zhou, L.; Bae, J.; Liu, H.; Singh, G.; Green, J.; Samaras, D.; Prasanna, P. Chest Radiograph Disentanglement for COVID-19 Outcome Prediction. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.

Konwer, A.; Bae, J.; Singh, G.; Gattu, R.; Ali, S.; Green, J.; Phatak, T.; Prasanna, P. Attention-Based Multi-Scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.

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.