SheSMAT Foundation · Established January 2026
Advancing the Dissemination and Impact of NeuroGraph Sparse Analytics.
SheSMAT Foundation is a community-facing platform supporting the dissemination, collaboration, and responsible scaling of NeuroGraph Sparse Analytics, a statistical methodology endeavor focused on complex biomedical data, with Alzheimer's disease as its principal initial application.
Why SheSMAT exists
Advancing statistical methodology requires more than developing analytical tools. Meaningful dissemination also depends on accessible research communication, technical education, interdisciplinary collaboration, and pathways for responsible application.
SheSMAT Foundation provides a community-oriented platform to support these connections and expand opportunities for engagement with NeuroGraph Sparse Analytics.
The research we support
A statistical research endeavor addressing high-dimensional, interconnected, and incompletely observed biomedical data. Its principal initial application is Alzheimer's disease research.
Principal initial application
Alzheimer's research involves structural MRI, PET imaging, and cerebrospinal fluid biomarkers. These sources provide complementary information but are often incompletely observed. The NeuroGraph endeavor investigates statistical approaches for analyzing such data; SheSMAT supports the communication, education, and collaboration needed to advance this direction.
Explore the Research FocusLearning Academy
Accessible statistical and computational learning resources that support research understanding and participation.
DoctorAI Academy · Foundational
Develop foundational mathematical and statistical knowledge relevant to artificial intelligence, machine learning, and advanced data analysis.
View courseDoctorAI Academy · Foundational / Introductory
Explore fundamental machine learning concepts and the major categories of learning approaches used in data analysis.
View courseOur approach
An intended research-to-community pathway, not a statement of completed outcomes.
Advance statistical methods for complex, high-dimensional, and incomplete data.
Evaluate methodological performance through theoretical, simulation-based, and empirical research.
Share findings, technical knowledge, educational resources, and reproducible materials.
Connect researchers, educational institutions, and community organizations to explore applications.
Support responsible adaptation, evaluation, and potential implementation in appropriate settings.
Dissemination & translation
SheSMAT aims to provide infrastructure for sharing statistical methodology research with broader research and learning communities.
Verified publications, manuscripts, posters, and conference presentations, with attribution and authorized supporting materials.
Methodological explanations, tutorials, reproducible examples, approved code repositories, and technical documentation.
Foundational mathematics, statistics, and computational learning connected to the skills needed to understand the research.
Pathways for methodological discussion, independent evaluation, and potential interdisciplinary collaboration.
Research, education & community
Featured educational resources
SheSMAT Foundation incorporates educational resources from DoctorAI Academy to support foundational learning in mathematics, statistics, and machine learning. These resources support broader understanding and dissemination of advanced statistical research.
Featured: Foundation of Math & Statistics for AI; Machine Learning & Types.
Explore learning resources
Proposed exploratory collaboration
SheSMAT Foundation seeks to explore the application of NeuroGraph Sparse Analytics in collaboration with Green Agritech Foundation to investigate complex relationships among psychological distress, social conditions, environmental pressures, and community well-being, including among agricultural populations.
Explore the proposed focusConnect around methodological discussion, technical learning, interdisciplinary evaluation, and responsible research engagement.