My research develops and applies statistical, computational, and AI-enabled methods to address clinically important questions in perioperative medicine, trauma recovery, and complex health data analysis. Across projects, I work closely with clinicians, trainees, and interdisciplinary collaborators to build research programs that are methodologically rigorous, practically useful, and increasingly supported by reproducible data science and AI workflows.

I view Research and Portfolio as complementary pages: this page summarizes the main scientific directions and representative work, while the Portfolio page highlights current systems-building efforts such as AI-assisted clinical summarization and prediction pipelines.

Research Areas

Perioperative Medicine

Patient-centered and data-informed perioperative care

I study perioperative risk factors, treatment effects, gastric ultrasound, fluid status, and AI-assisted decision support tools designed to improve preoperative and intraoperative care.

Trauma Recovery

Posttraumatic pain, neuropsychiatric outcomes, and biomarkers

I develop prediction tools, identify clinically meaningful subgroups, and investigate wearable, language, and behavioral biomarkers for heterogeneous posttraumatic outcomes.

Methodology

Statistical modeling for high-dimensional and longitudinal data

My methodological work spans latent variable models, heterogeneous treatment effects, hidden Markov models, and computational approaches for complex longitudinal health data.

Selected Work By Area

Perioperative care and clinical decision support

Posttraumatic outcomes and recovery

Statistical methodology and high-dimensional data analysis

Collaborators

Song Rui, Heng Cai, and Wenbin Lu.

For project-level systems work, demos, and current AI-enabled pipelines, see the Portfolio page. For a broader academic record and external publication profiles, see CV.