Improving Robust High-Dimensional Causal Inference and Prediction Modelling

Why Study Complex Data in Precision Medicine?

Recent advances in “-omics” technologies are allowing scientists to simultaneously collect large amounts of clinical data. This is revolutionizing the way we can measure pathogenic processes or responses to therapies. 

This high-dimensional data has the potential to improve disease prevention and diagnosis. However, it presents significant challenges due to measurement errors, outliers, multivariate responses, and complex correlation structures.

Advancements in this area are essential for building useful models in precision medicine.

Areas of Exploration

Advanced Regularized Regression

Includes reducing variance and decreasing sample errors in biometric data.

Regularized Instrumental Variables

Includes correcting for measurement errors in biometric data.

Matrix-Valued Causal Models

Includes the development of causal models in high-dimensional settings.

Solving Global Challenges

Research Team’s Goal

To develop an advanced analytical framework for managing complex data in biomedical sciences.