Shuxing Ren is a PhD candidate specialising in AI for Science (AI4S) applied to epigenetics. He leverages advanced computational methods—including deep learning, statistical modeling, and large-scale data integration—to decode the complex regulatory logic embedded within epigenomic landscapes. His research focuses on developing novel AI-driven approaches to elucidate how dynamic epigenetic modifications influence gene expression, cellular identity, and disease pathogenesis, particularly in [specific area, e.g., cancer, neurodevelopment]. Driven by the potential of AI to transform biological discovery, he aims to build predictive models that uncover new mechanistic insights and accelerate therapeutic target identification. He is proficient in Python, key ML/DL frameworks (PyTorch/TensorFlow), and high-performance computing, and is actively seeking interdisciplinary collaborations at the forefront of computational epigenomics.