Kangning Diao

I'm a Ph.D. student in Physics at Tsinghua University currently working on cosmology and machine learning. My research focuses on developing novel computational methods to study the epoch of reionization through 21cm hydrogen line observations.

I develop software tools and statistical techniques to analyze cosmological data, with a particular focus on synchrotron emission and 21cm signals. Recently, I created synax, a differentiable and GPU-accelerated package for simulating Galactic synchrotron emission. I'm also working on applying machine learning techniques like GANs and diffusion models to generate and analyze cosmological images.

My work aims to bridge modern computational methods with fundamental questions about the early universe. I'm particularly interested in developing efficient emulators and statistical tools that can help us extract maximum information from upcoming radio telescope observations like those from SKA and HERA. Through my research, I hope to contribute to our understanding of how the first stars and galaxies formed and influenced the evolution of the universe.