Some current research opportunities in my lab include predictive modeling and causal effect estimation from ultra high dimensional, sparsely and irregularly sampled longitudinal data; representation learning and predictive modeling from functional data; causal inference from relational data; multi-modal representation learning; causal fairness criteria; explainable machine learning; and applications in biomedical sciences, e.g., predictive and causal modeling of health risks and health outcomes from clinical, environmental, socio-demographic and behavioral data; characterization and prediction of protein-protein, protein-DNA and protein-RNA interactions, interfaces and complexes; and in material sciences and engineering, e.g., data-driven development of potential functions, inverse design of materials, and optimizing experimental measurements in material science; and data and computational infrastructure for collaborative discovery.

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