Our Research

The SMOOTH (Skin & Mucosal Microbiome Omics for Total Health) laboratory employs a comprehensive, AI-powered research pipeline that integrates large-scale clinical sampling with cutting-edge computational analysis to advance personalized skin medicine. Our research framework encompasses six core components that work synergistically to decode the complex relationship between skin microbiome and human health.

Research Pipeline

Research Pipeline
Global Human Skin Microbiome
Immunofluorescence

Skin bacteria influence the development of Langerhans cells.

Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Skin bacteria influence the development of Langerhans cells.
Current Projects (Active Data Collection)

"The largest long-term study of brain development and child health in the United States."
Current Projects (Active Data Analysis)

Neural correlates of financial decision making.
How do cannabis use and HIV infection impact the brain?
Better understanding the neurobiology of addiction through meta-analysis.
How does electronic cigarette use affect youths during development?
What can we learn from dense sampling of individuals?
Software

Can we automatically annotate the cognitive neuroscience literature?
A new frontend for Brainspell and a resource to facilitate neuroimaging meta-analysis.
A Python package for neuroimaging meta-analysis that provides a shared syntax for a range for algorithms.
A Python library for mixed-effects meta-regression (including meta-analysis).
An interconnected set of open source tools for large-scale meta-analysis.
Completed Projects

How do adolescents perform effort-based decision making?
How do individuals with high functioning autism spectrum disorder perform social processing?