Yiqun is a PhD candidate in the Department of Biostatistics at the University of Washington. His dissertation focuses on leveraging toolkits from selective inference to quantify uncertainty in statistical machine learning, an increasingly critical topic in exploration-driven, real-world data analyses. He also has broad interests in applications of rigorous methods in computer science, public health, and biology. Prior to graduate school, Yiqun completed undergraduate degrees in Statistics, Computer Science, and Chemical Biology with higher distinction from the University of California, Berkeley.