Wei Biao Wu

Wei Biao Wu applies creative solutions to complex statistical challenges as a Professor in the Department of Statistics and the College at the University of Chicago. With a keen interest in high-dimensional inference under dependence, Professor Wu's research spans a diverse array of topics, including model selection, covariance matrix estimation, regression, and mean vector estimation. His work also delves into multiple testing problems, deviation and concentration inequalities, and the deep Gaussian approximation problem, reflecting his commitment to advancing the field of statistics through innovative methodologies. Professor Wu's dedication to teaching and research has been recognized through several prestigious awards. In 2022, he was honored with the Faculty Award for Excellence in Graduate Teaching, a testament to his ability to inspire and educate the next generation of statisticians. His contributions to the field have also been acknowledged internationally, as evidenced by the Alexander von Humboldt Foundation Research Award he received in 2019. Throughout his career, Professor Wu has been at the forefront of statistical research, consistently pushing the boundaries of what is possible in the field. His work not only addresses theoretical challenges but also has practical implications, providing valuable insights and tools for tackling real-world problems. In addition to his research and teaching, Professor Wu is actively involved in the academic community, contributing to various conferences and workshops. His collaborative approach and commitment to fostering a vibrant intellectual environment make him a respected figure among his peers and students alike. Professor Wu's passion for statistics is matched by his enthusiasm for mentoring students, guiding them to develop their own innovative solutions to statistical problems. His influence extends beyond the classroom, as he continues to shape the future of the field through his groundbreaking research and dedication to education.