Data Science Research Intern
-Developed a predictive model to assess mortality risk for pediatric patients on Extracorporeal Life Support (ECLS) in the Pediatric Intensive Care Unit (PICU). Addressed limitations of prior general models by conducting in-depth research to identify unique mortality risk factors specific to ECLS patients.-Created a practical tool that hospitals can utilize to benchmark unit performance, enabling targeted improvements and better outcomes for critically ill children.-Invited to present research findings at the 2025 Critical Care Congress in Orlando, Florida, one of the largest international conferences for clinicians, attended by over 5,000 healthcare professionals.