In healthcare and biotech, enhanced predictions and insights can improve patient outcomes, identify trends in population health, and reduce costs. Achieving these goals requires access to larger datasets. However, collecting additional data can be impractical or prohibitively expensive. Data sharing in healthcare offers a potential solution to this challenge, but it is constrained by several factors:

·         Stringent data privacy regulations often hinder the sharing of sensitive data.

·         Technical issues, such as some datasets being too large to transfer or having inherent heterogeneity among different local datasets.

·         An inherent unwillingness to share, often due to competition between collaborators or concerns related to intellectual property rights.

To address these constraints, we will develop statistical machine learning techniques and computational tools to optimize data utilization and tackle key data sharing challenges. This includes anonymization techniques that comply with privacy regulations for sharing personal data and reduce data sizes, making transfers more feasible and lowering computational demands. The statistical machine learning techniques should address the heterogeneity and size differences across various local datasets while adapting to diverse practical data-sharing scenarios. Algorithms will be designed to manage diverse datasets, accommodate various data complexities, and provide techniques for variable selection and regularization.

Our aim is to enable collaborators from both academia and industry to share insights without giving away their data. Current challenges in data sharing prevent the realization of the full potential inherent in combining sensitive data, leaving essential insights and discoveries untapped. Our statistical machine learning algorithms will address these challenges, enabling organizations and collaborators to maximize the value of their data, thereby facilitating the discovery of new biomarkers and improving disease risk stratification.