Qiao Zhuang joined UMKC in 2024, after working as a postdoctoral scholar at Worcester Polytechnic Institute. He received his PhD. in Mathematics from Virginia Tech in 2020.
His research interests include numerical analysis and scientific computing, as well as machine learning and data science. He has been working on finite element methods, especially for solving interface problems on unfitted meshes. He also works on meshless collocation methods, such as radial basis function methods. More recently, he delves into scientific machine learning for PDEs, with particular interests in developing neural networks for multiscale and interface problems. He is passionate about extracting insights from classical numerical methods and integrating them into neural network frameworks to address complex and real-world problems.