My research focuses on integrating approaches from evolutionary genomics, mathematical statistics, machine learning and computational biology to understand various evolutionary processes. I am particularly interested in investigating the relative contributions of stochastic and deterministic forces to evolutionary phenomena across three major research directions:

1. Adaptive evolution of viruses. I explore viruses such as SARS-CoV-2, IBV, and PEDV to understand their adaptive evolution, including factors such as origin, cross-species transmission, intra- and inter-host evolution, and spread dynamics of emerging variants. This research direction contributes to my understanding of viral evolution and its implications for public health.

2. Theoretical aspects of traditional evolutionary processes. I delve into theoretical aspects of traditional evolutionary processes by combining evolutionary approaches with mathematical modeling, machine learning, and computational simulation. By analyzing publicly available data, I aim to understand various aspects of evolution, including mutation rate, individual-based genetic drift, epistasis, fitness landscapes, and Hill-Robertson effect. This interdisciplinary approach helps me uncover fundamental principles underlying evolutionary dynamics.

3. Genotype-phenotype prediction using AI. I utilize advanced machine learning techniques to predict viral phenotypes based on genome sequences. My research focuses on constructing models to accurately predict characteristics such as transmissibility, tissue tropism, and receptor binding capabilities of different viral strains. This approach aims to provide crucial data support for vaccine and drug development, enhancing public health preparedness.

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