I am a Ph.D. candidate in the Program in Atmospheric and Oceanic Sciences at Princeton University, with a strong passion for climate science and the power of numerical simulations to unlock new insights. My current research, under the guidance of Prof.Gabriel Vecchi focuses on cloud feedback and climate sensitivity—key factors in understanding the future of Earth’s climate. Previously, I investigated the dynamics of the Hadley circulation and its expansion in collaboration with Prof. Yongyun Hu and Xinyu Wen at Peking University.

I am also passionate about applying machine learning to address challenges in climate simulation. During a recent research internship at NVIDIA, I developed a Machine Learning Earth System Model, Ola, to extend the predictability of AI-based weather models to the seasonal timescale. By explicitly coupling machine learning-based atmosphere and ocean models, Ola simulates realistic equatorial Kelvin and Rossby waves and shows promising skill in forecasting ENSO events. Impressively, Ola can generate a six-month forecast in under a minute using a single A100 GPU, offering a cost-efficient approach to long-range climate prediction. Further details can be found in our preprint.