Tieqiao's Homepage

Tieqiao Wang (pronounced: tyeh-chyao wahng) Ph.D. Student in Computer Science School of Electrical Engineering and Computer Science, Oregon State University, Kelley Engineering Center Corvallis, OR, 97331, United States (wangtie(AT)oregonstate.edu) Links: Google Scholar, GitHub

About Me

I am a Ph.D. candidate at Oregon State University started from Fall 2020. My advisor is Prof. Sinisa Todorovic. Earlier, I earned a B.E. in Computer Science from the University of Jinan in 2020. From September 2020 to August 2021, I balanced remote studies with work as an Algorithm Engineer Intern at Volkswagen Group China. My role primarily involved enhancing the efficiency and robustness of segmentation and detection models.

Research Interests

• Video Action Segmentation

• Weakly Supervised Learning

• Image Segmentation and Object Detection

Publications

• Christina Hospodar*, Tieqiao Wang*, Yasmine Elasmar, Sinisa Todorovic, and Karen Adolph. Infant gait modifications: Integration of computer vision with human annotation provides accurate step classification and location as infants navigate varied terrain. (*Equal Contribution, Oral) In IEEE International Conference on Development and Learning, 2024. (Source Code)

• Danyang Han*, Nicolas Aziere*, Tieqiao Wang, et al. Infants’ developing environment: integration of computer vision and human annotation to quantify where infants go, what they touch, and what they see. (*Equal Contribution, Oral, Best Paper Award) In IEEE International Conference on Development and Learning, 2024. (Source Code)

• Tieqiao Wang, Pico Sankari, Jostan Brown, Achyut Paudel, Liqiang He, Manoj Karkee, Ashley Thompson, Cindy Grimm, Joe Davidson, Sinisa Todorovic. Automatic estimation of trunk cross sectional area using deep learning. (Oral) In Precision agriculture, pp. 491-498. Wageningen Academic, 2023. (Source Code)

• Tieqiao Wang, Sijie Niu, Jiwen Dong, and Yuehui Chen. Weakly supervised retinal detachment segmentation using deep feature propagation learning in sd-oct images. In International Workshop on Ophthalmic Medical Image Analysis, pages 146–154. Springer, 2020. (Source Code)

• Haochen Yang, Tieqiao Wang, Xuesong Zhou, Jiwen Dong, Xizhan Gao, and Sijie Niu. Quantitative estimation of rainfall rate intensity based on deep convolutional neural network and radar reflectivity factor. In Proceedings of the 2nd International Conference on Big Data Technologies, pages 244–247, 2019.