Ruyi Lian
Assistant Professor of Computer Science
Biography
Dr. Ruyi Lian is an assistant professor in the McComish Department of Electrical Engineering and Computer Science at Ä¢¹½´«Ã½. She earned her Ph.D. in computer science from Stony Brook University in 2025, advised by Haibin Ling, and received her B.S. degree in applied mathematics from the University of Science and Technology of China in 2019.
Her research spans computer vision, robotics and artificial intelligence for science, with particular focus on image-based object pose estimation, 3D reconstruction and generation, and interdisciplinary applications across civil engineering, agriculture and biomedical imaging.
Her research spans computer vision, robotics and artificial intelligence for science, with particular focus on image-based object pose estimation, 3D reconstruction and generation, and interdisciplinary applications across civil engineering, agriculture and biomedical imaging.
Education
- Ph.D. in computer science | Stony Brook University, Stony Brook, New York | 2025
- B.S. in applied mathematics | University of Science and Technology of China, Hefei, China | 2019
Academic and Professional Experience
Academic Interests
- 3D computer vision
- Robotics
- AI for science
Academic Responsibilities
- CSC 422/522 – Computer Vision and Pattern Recognition, Spring 2026
- CSC 300 – Data Structures, Fall 2025
Ä¢¹½´«Ã½ and Scholarly Work
Areas of Ä¢¹½´«Ã½
- Image-based object pose estimation, 3D perception and robotic grasping
- 3D analysis, reconstruction and generation for visual understanding and embodied intelligence
- Interdisciplinary applications of computer vision and AI in civil engineering, construction, agriculture and biomedical imaging (cryo-EM, OCT, etc.)
Publications
- Lian, R., Lin, Y., Latecki, L.J. and Ling, H. (2025). VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation. IROS 2025 (Oral).
- Gong, Y., Yao, J., Lian, R., Lin, X., Chen, C., Divakaran, A. and Yao, Y. (2025). Recovering manifold representations via unsupervised meta-learning. Frontiers in Computer Science, 6: 1255517.
- Pandi, S.V., Li, Z., Lian, R., Lee, J., Cheng, W., Wang, L., Lin, Y., Liu, Q. and Ling, H. (2025). A Novel Contrastive Loss and Clustering Approach for Particle Detection in Cryo-EM. ISBI 2025.
- Lian, R. and Ling, H. (2023). CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network. ICCV 2023.
- Lian, R., Huang, B., Wang, L., Liu, Q., Lin, Y. and Ling, H. (2022). End-to-end orientation estimation from 2D cryo-EM images. Acta Crystallographica Section D.
- Huang, B., Lian, R., Samaras, D. and Ling, H. (2021). Modeling Deep Learning Based Privacy Attacks on Physical Mail. AAAI 2021.
Associated Areas
