Staff Surgeon/Assistant Professor

The petitioner proposes to evaluate and treat surgical conditions through clinical research to enhance patient safety and optimize quality of care. His work focuses on addressing disparities in emergency and trauma surgery and improving access to safe surgical care in low-resource and high-risk environments, aiming to bridge public health research with clinical practice to shape global health policy.

The petitioner proposes to develop advanced artificial intelligence and machine learning models, specifically deep learning and neuroimaging techniques, to objectively assess surgical performance. This work aims to enhance surgical training efficiency and improve patient safety by reducing mortality rates in operating rooms, even in data-limited settings.

The petitioner proposes to design algorithm-based models that analyze electronic health records (EHRs) to detect patient risk early and recommend personalized treatment strategies for sepsis, trauma, and acute pancreatitis. She intends to contribute to open-source health data modeling at the Laboratory for Computational Physiology at MIT using advanced machine learning techniques, signal processing, and pattern recognition applied to ICU datasets.

The petitioner proposes to identify clinical and biological factors associated with poor outcomes in critically ill patients to develop advanced predictive models and evidence-based clinical strategies. The work focuses on optimizing risk stratification and treatment pathways for conditions such as acute respiratory distress syndrome (ARDS), septic shock, organ transplantation, and pulmonary embolism. The goal is to improve patient survival rates and enhance the efficiency of healthcare resource utilization.