Sebastian Medina is a graduate student involved in several projects:
- Predictive Biomarkers for Prostate Cancer: Development of prostate cancer predictive biomarkers to evaluate chemotherapy benefit from core needle biopsies.
- Biochemical Recurrence Risk Prediction: Risk assessment of biochemical recurrence in patients post-radical prostatectomy using RP specimens.
- Patient Risk Stratification: Use of 3D open-top light sheet microscopy on core needle biopsies for enhanced patient risk stratification.
- Grading Upgrades in Prostate Cancer: Transition from core needle Gleason grading to RP grading in prostate cancer evaluation.
- Digital Biomarkers in Genitourinary Cancers: Research of prognostic and predictive digital biomarkers across genitourinary cancers.
Sebastian is focused on the integration of artificial intelligence with medicine and aiming to create effective and interpretable diagnostic tools from digital pathology. His work is particularly directed towards improving cancer diagnostics in low- to middle-income countries. His primary research interest lies in genitourinary cancers, especially prostate cancer, where he seeks to enhance diagnostic accuracy, treatment outcomes and improve patient selection for specific therapies.
Prior to joining the lab, Sebastian was a data scientist at UAV Latam, where he specialized in processing UAV-acquired aerial images for tasks such as semantic segmentation, object counting, detection, and classification. He also worked as a software consultant at a cybersecurity company.
Sebastian currently is pursuing a PhD in biomedical engineering in the Coulter Department of Biomedical Engineering of Emory and Georgia Tech. He earned an MS in systems and computing engineering from Universidad Nacional de Colombia, where he worked in interpretable deep learning for prostate cancer grading from histology images. He earned his undergraduate degree in systems and computing engineering from Universidad Nacional de Colombia.