About Me

I am currently a fifth-year Ph.D. student in Statistics at UC Davis, advised by Professors Krishna Balasubramanian and Debashis Paul. My research interests lie at the intersection of optimization, meta-learning, high-dimensional statistics, in-context learning, and transformers, with a particular focus on developing theoretical foundations for modern machine learning and deep learning. During my Ph.D., I have also worked with Professor Lifeng Lai on problems in differential privacy.

Before pursuing my Ph.D., I earned a Master’s degree in Statistics from UC Davis, where I developed a strong background in statistical modeling, predictive analytics, statistical inference, and computational methods. I received my Bachelor’s degree in Mathematics from the University of Chinese Academy of Sciences (UCAS), where I built a rigorous foundation in probability, linear algebra, and mathematical optimization under the mentorship of Professor Qizhai Li.

Throughout my academic training, I have developed expertise in both the theoretical and computational aspects of machine learning, including statistical inference, optimization, high-dimensional statistics, and scalable learning algorithms, as well as practical experience with machine learning frameworks such as PyTorch and TensorFlow. My research aims to bridge rigorous mathematical theory with modern machine learning and artificial intelligence.