Xiaohan, a Research Associate at the Institute for Manufacturing (IfM), specializes in cutting-edge research within the realm of 3D printing. Her current work focuses on employing reinforcement learning techniques to control flow rates in Fused Deposition Modeling (FDM). Leveraging video detections processed by Swin Transformer, this innovative research is geared towards improving the overall quality of 3D-printed objects.

Prior to her role at IfM, Xiaohan earned her PhD in Engineering from the University of Edinburgh, where she contributed to the design of surrogate models for laser powder bed fusion digital twins. She obtained an MSc in control systems from Imperial College London and a BEng in railway traffic signalling and control from Southwest Jiaotong University. She was awarded by Principal's Career Development Scholarship, Edinburgh Global Research Scholarship, and Santander Master's Scholarship.