Undergraduate Courses
Bayesian Statistics (Spring 2020–2026)
Foundations of Big Data Analytics (partial content; Fall 2019–2025)
Probability Theory and Mathematical Statistics (Fall 2021–2026)
Machine Learning (partial content; Spring 2019, Spring 2024)
Mathematical Analysis (teaching assistant; Fall 2016, Spring 2017, Spring 2019)
Graduate Courses
Bayesian Learning (Spring 2022–2024)
PhD Students
Graduated:
2022 cohort: Yuji Lin, "Structure Prior Guided Deep Light Field Image Restoration Method"
Current:
2026 cohort: Ying Liu, Chenhao Ding
2025 cohort: Wang Zhiwu
2023 cohort: Ruyi Shi
Master's Students
Graduated:
2022 cohort: Fengying Wang, "Meta-Learning Based Robust Deep Metric Learning"
2021 cohort: Jiangtao Zhang, "Research on Blind Deconvolution Method Based on Deep Blur Kernel Modeling and Image Prior"; Zibo Song, "Quantizing High-frequency Features for Image Rescaling"
2020 cohort: Ziming Liu, "The Study of Fractional Neural Ordinary Differential Equations"
2019 cohort: Hui Wang, "Research on Modern Bayesian Inference Methods and Applications"
2018 cohort: Xiuqi Shen, "Robust Autoencoder Based on Complex Noise Modeling and Its Application"
2017 cohort: Yi Sui, "A Deep Model-driven CT Denoising Network"
Transferred to PhD:
2024 cohort: Ying Liu, Chenhao Ding
2023 cohort: Zhiwu Wang
2022 cohort: Shinan Chen, Shunyao Wang
2021 cohort: Ruyi Shi
2020 cohort: Xinyi Liu, Yuji Lin
Current:
2026 cohort: Yangyang Kang, Kun Wang, Jizhe Li, Zhengyun Xu
2025 cohort: Shibo Zhao, Rongyu Feng, Erbin Ma, Tongwei Xu
2024 cohort: Yuanfang Chen, Peiqiang Yan
