AMIA 2025: Clinical AI & Data Science
Latest note on clinical AI and data science at AMIA 2025.
View on LinkedIn
About
Advancing data-informed care
I design and deploy AI systems that are grounded in clinical workflows and co-developed with care teams. My Ph.D. in Computational Medicine centered on early warning systems for critical events in intensive care units. Today, I apply those lessons as a Data Scientist and Postdoctoral Fellow at Duke, focusing on perioperative medicine and Generative AI for Medicine.
My work combines statistical rigor, prospective clinical evaluation, and multidisciplinary collaboration. I enjoy partnering with clinicians, informatics teams, and industry collaborators to translate AI research into sustainable delivery models that improve outcomes and equity.
Featured
Latest LinkedIn Post
Focus Areas
Where clinical expertise meets machine intelligence
I build systems that are interpretable, safe, and ready for deployment.
Trustworthy Clinical AI
LLM agents and RAG pipelines that surface context-rich insights for perioperative teams with clear guardrails.
Predictive Monitoring
Time-series and multimodal models that flag hemodynamic shock, hypothermia, and respiratory risk hours in advance.
Clinical Impact
Prospective studies and trials that prove measurable gains in value-based care and clinical decision-making.
Recent Highlights
Selected milestones and collaborations
-
2025
Randomized trial of a transthoracic echocardiography decision tool in the Journal of the American Society of Echocardiography.
-
2025
Contextual phenotyping of pediatric sepsis cohorts using LLMs selected for oral presentation at AMIA 2025.
-
2024
Launched the Pediatric Sepsis Data Challenge leaderboard with open-sourced evaluation tooling for global teams.
-
2024
Open-sourced code and benchmarks for the Pediatric Sepsis Challenge to accelerate reproducible clinical AI.
Research Spotlights
Impactful collaborations across the translational pipeline
Agentic Hybrid RAG
Multi-agent RAG for scientific review; next step is combining structured EHR data with LLM reasoning for perioperative care.
Prospective Shock Prediction
Prospectively validated prediction of hemodynamic shock in resource-limited pediatric ICUs.
Thermal Imaging for Shock
Early detection of hemodynamic shock using thermal videos and deep computer vision models.
Digital Health Equity
How electronic health literacy intersects with social determinants of health.
Randomized Trial of a TTE Value Tool
Decision support improved value-based imaging RCT. Journal of the American Society of Echocardiography, 2025.
Contextual Phenotyping with LLMs
LLM-driven phenotyping of pediatric sepsis cohorts using chart and registry context. AMIA Annual Symposium, 2025.
Early Warning for Hemodynamic Shock
Thermal imaging and machine learning to predict hemodynamic shock. Scientific Reports, 2019.
Digital Health Literacy & SDOH
Social determinants shaping digital health literacy. Preventive Medicine Reports, 2024.
Hypothermia Prediction with Physiological Vitals Time-Series Data
Physiologic time-series with machine learning to predict hypothermia. Frontiers in Physiology, 2022.
Predicting Childhood Asthma Exacerbations
Longitudinal EHR and lung impulse oscillometry to forecast pediatric asthma exacerbations. European Respiratory Journal suppl_64, 2020.
Environmental Drivers of Respiratory Events
Impact of ambient air pollution on pediatric emergency visits for acute respiratory illness. Environmental Science and Pollution Research, 2021.
Prospective Validation of Shock Prediction
Real-world validation of an early warning system for hemodynamic shock. SSRN Working Paper, 2023.
Media & Outreach
Sharing work across clinical and public audiences
Connect
Let’s build the next generation of clinical AI
Collaborations & Speaking
Open to clinical deployments, translational research, and mentoring data scientists.