Colloquium: Computational Framework for Learning from Complex Data

Title: A Computational Framework for Learning from Complex Data: Formulations, Algorithms, And Applications
Speaker: Wenlu Zhang
3:30pm, Monday, May 1, SH 107

Abstract: Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development.