Parameter estimation for genetic time-series data: Theory and methods

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This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communications, it seeks to design scalable inference methods for resolving mutational fitness effects from genetic time-series measurements of complex evolving populations. This would enable new understanding of complex adaptive systems, such as pathogen evolution, host-immune dynamics, and acquisition of drug resistance.