Rate My Professor Xuelian Cheng

Dr. Xuelian Cheng serves as a Research Fellow at the Monash Suzhou Research Institute and an Adjunct Lecturer in the Department of Data Science & AI within the Faculty of Information Technology at Monash University, Australia. She earned her PhD from Monash University under the supervision of Associate Professor Zongyuan Ge, Associate Professor Mehrtash Harandi, and Professor Tom Drummond. Cheng's research specializations include deep learning for 3D visual perception and reconstruction, automated machine learning, video analysis, object detection, machine learning, computer vision, medical image analysis, and robotic surgery intelligence. Her interests also cover applications of visual medical data technologies such as AR/VR/XR for educational purposes, disease detection, diagnosis, monitoring, and surgical interventions. She has collaborated on research projects with industrial companies including Tencent Canberra XR Lab, IIAI, Airdoc, and SenseTime, leading to publications in top conferences.

In her professional career, Cheng has undertaken research internships at IIAI remotely, at Mohamed bin Zayed University of Artificial Intelligence from April 2021 to October 2021, and at Tencent XR Lab in Canberra, Australia, from August 2022 to February 2023. She lectures courses at Monash University Suzhou, including FIT5047 Fundamentals of Artificial Intelligence, FIT5226 Multi-Agent Systems and Collective Behaviour, FIT5216 Modelling Discrete Optimization Problems, and FIT5037 Network Security. Previously, she was a teaching assistant for ECE4179 Neural Networks and Deep Learning and ECE4076 Computer Vision at Monash University Clayton campus from 2020 to 2022. Notable publications include "Hierarchical Neural Architecture Search for Deep Stereo Matching" (NeurIPS 2020), "Skeleton Based Action Recognition using Translation-Scale Invariant Image Mapping and Multi-Scale Deep CNN" (ICMEW 2017), "EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos" (MICCAI 2023), "OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding" (ECCV 2024), "Deep Learning: A Primer for Neurosurgeons" (2024), and "Declaration of Computational Neurosurgery" (2024). She holds two patents.