Jingru Mu

An advocate for integrating knowledge on statistical methodologies and data analysis, Dr. Jingru Mu serves as an Assistant Professor in the Statistics Department at Kansas State University. With a robust academic foundation, she earned her Ph.D. in Statistics from Iowa State University, following her M.S. and B.S. degrees in Statistics and Mathematics from the University of California, Davis, and Zhengzhou University, respectively. Dr. Mu's research interests are diverse and cutting-edge, encompassing non/semi-parametric statistics, complex and large-scale data analysis, spatio-temporal data analysis, variable selection, and image analysis. Her work aims to address the challenges posed by the increasing complexity and volume of data in various fields, contributing to advancements in statistical methodologies and their applications. In addition to her research, Dr. Mu is deeply committed to teaching and mentoring students. She has a keen interest in courses such as mathematical statistics, statistical inference, and time series analysis, where she strives to inspire and equip her students with the necessary skills to excel in the field of statistics. Her approach to teaching emphasizes the importance of understanding theoretical concepts while also applying them to real-world problems. Dr. Mu's contributions to the academic community extend beyond her research and teaching. She actively participates in conferences and workshops, sharing her insights and collaborating with fellow researchers to push the boundaries of statistical science. Her dedication to her field is reflected in her numerous publications and presentations, which have garnered attention and respect from her peers. Through her work, Dr. Mu continues to make significant strides in the field of statistics, driven by a passion for discovery and a commitment to advancing knowledge. Her efforts not only enhance the academic environment at Kansas State University but also contribute to the broader scientific community's understanding of complex data analysis.