My research focus mainly concentrates around interdisciplinary fields of theoretical artificial intelligence analysis and real-world applications, including graph model representing non-structure data, robust training algorithms with imperfect datasets and reinforcement learning algorithms for sequential decision making.
I am recently interested in AI-aided drug discovery, whose typical applications include high content phenotype analysis, drug analysis with molecular graph representation, retrosynthesis analysis with reinforcement learning, virus variation, etc, all with the aim to promote wider AI-powered technologies in the modern pharmaceutical pipeline.