Sizhe Sui

Sizhe Sui

Master of Science in robotics, SJTU

Sizhe Sui is currently pursuing his Master’s degree in Robotics at Shanghai Jiao Tong University (SJTU). He received his Bachelors’s degree from SJTU and graduated from the Hsue-shen Tsien Honored Program (Top 5%). He is now supervised by Prof. Ye Ding and was fortunate to be advised by Prof. Animesh Garg as a summer intern in 2020.

His research interests involve (multi) robot kinematics calibration, motion planning and robotic vision. He is also fascinated with accelerating robotics algorithms with massive-parallel computation units (like GPU). He is eager to explore the possibilities of redesigning algorithms with parallelism considered from its initial build. Currently, he is carrying out a summer research with Prof. Luis Sentis and Dr. Andrew Bylard on such a topic.

He has an in-depth understanding of robot kinematic parameter calibration, multi-robot system calibration, time optimal trajectory planning, sample-based motion planning and point cloud registration thanks to his participation and dedication in previous projects. He also has rich experience with hardware robotics platforms by building experiment environments from scratch with ROS.

In his leisure time, he is a breath-stroke swimmer and a photographer (part-time Photojournalism). Some of his works are exhibited in Portfolios.

News

  • I will commence my journey as a Ph.D. candidate at the University of Texas at Austin this summer, under the guidance of Professor Luis Sentis!
  • I am privileged to have been selected as the Commencement Speaker for the Mechanical Engineering Department at Shanghai Jiao Tong University! (March 26, 2024)
Commencement Speaker
  • Awarded the ‘Outstanding Master’s Thesis’ for the Class of 2024!
  • Finished my visiting scholar program at UT Austin with Prof. Luis Sentis. Thank you so much for hosting me this summer!
Photo with Prof. Sentis
  • My recent work Solving the AXB=YCZ Problem for a Dual-Robot System with Geometric Calculus was accepted by IEEE Transactions of Automation and Science (TASE). The system is built from scratch with JAKA Zu12, ABB 4600, ROS and Gocator 3210 Camera. Read Publicaions and GitHub for detail!