Ph.D. in Mathematics (2020), National University of Singapore.
Supervisor: Prof. Toh Kim-Chuan
Bachelor in Computational Mathematics (2015), University of Science and Technology of China.
Supervisor: Prof. Zhang Juyong
Optimization: Algorithm design, analysis, and software development.
AI: Mathematical foundation for data science, AI for optimization.
Data-driven applications: Healthcare, recommender system, metaverse.
Ph.D. Students: I am particularly interested in students who have a solid mathematical foundation and are willing to work hard on challenging problems including real-world applications in optimization and data science. English requirement for Ph.D. students (with or without a master degree): at least IELTS 6.5 or TOEFL 90. You may also want to know about the Hong Kong PhD Fellowship Scheme.
Research Assistants/Associates/Fellows/Postdoctoral Fellows (multiple positions are available): working on various projects about Deep Learning, Convex and Non-Convex Optimization, Optimal Transport, Software Development, and others. Priority will be given to those who have some computational experience.
Our group will work closely with several world-leading researchers in optimization, data science, and machine learning.
If you are interested in the above positions, please drop me an email.
Large Language Model Can Interpret Latent Space of Sequential Recommender.
Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, and Xiangnan He, (2023). (Under review)
XAI for In-hospital Mortality Prediction via Multimodal ICU Data.
Xingqiao Li, Jindong Gu, Zhiyong Wang, Yancheng Yuan, Bo Du, and Fengxiang He, (2023). (Submitted)
An efficient sieving based secant method for sparse optimization problems with least-squares constraints.
Qian Li, Defeng Sun, and Yancheng Yuan, (2023). (Under review)
Adaptive sieving: A dimension reduction technique for sparse optimization problems.
Yancheng Yuan, Meixia Lin, Defeng Sun, and Kim-Chuan Toh, (2023). (Under review)
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees.
Ziwen Wang, Yancheng Yuan, Jiaming Ma, Tieyong Zeng, and Defeng Sun, (2023). (Under review)
An Efficient HPR Algorithm for the Wasserstein Barycenter Problem with O(Dim(P)/ε) Computational Complexity
Guojun Zhang, Yancheng Yuan, and Defeng Sun, (2022). (Under review)
Differentiable Invariant Causal Discovery.
Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua, (2022). (Under revision)
Understanding deep learning via decision boundary.
Shiye Lei, Fengxiang He, Yancheng Yuan, Dacheng Tao.
IEEE Transactions on Neural Networks and Learning Systems (2023).
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion.
Zhengyi Yang, Jiancan Wu, Zhicai Wang, Yancheng Yuan, Xiang Wang, and Xiangnan He.
The 37th Conference on Neural Information Processing Systems (Neurips 2023) (Accepted).
A Highly Efficient Algorithm for Solving Exclusive Lasso Problems.
Meixia Lin, Yancheng Yuan, Defeng Sun, and Kim-Chuan Toh.
Optimization Methods and Software (2023). (In memory of Oleg Burdakov)
Invariant Collaborative Filtering to Popularity Distribution Shift.
An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, and Tat-Seng Chua.
In Proceedings of the ACM Web Conference 2023 (WWW ’23).
A Dimension Reduction Technique for Structured Sparse Optimization Problems with Application to Convex Clustering.
Yancheng Yuan, Tsung-Hui Chang, Defeng Sun, and Kim-Chuan Toh.
SIAM Journal on Optimization, 32: 3 (2022) 2294–2318.
On the Test Accuracy and Effective Control of the COVID-19 Pandemic: A Case Study in Singapore.
Guang Cheng, Yini Gao, Yancheng Yuan, Chenxiao Zhang, Zhichao Zheng.
INFORMS Journal on Applied Analytics, Vol. 52, No. 6, 2022, pp. 524–538.
(This paper has been selected for inclusion in the Special Issue on Analytics Remedies to COVID-19)
Convex clustering: model, theoretical guarantee and efficient algorithm.
Defeng Sun, Kim-Chuan Toh, and Yancheng Yuan.
Journal of Machine Learning Research, 22(9):1−32, (2021).
Learning Intents behind Interactions with Knowledge Graph for Recommendation.
Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, and Tat-Seng Chua.
In Proceedings of the ACM Web Conference 2021 (WWW ’21).
(Best Paper Award Finalist, currently Ranked #3 in Most Influential 2021 WWW Papers)
SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0).
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan, Xinyuan Zhao.
Optimization Methods and Software, 35 (2020) 87–115.
An efficient semismooth Newton based algorithm for convex clustering.
Yancheng Yuan, Defeng Sun, and Kim-Chuan Toh.
International Conference on Machine Learning (ICML), 2018.
Robust Interactive Image Segmentation Via Iterative Refinement.
Yao Peng, Juyong Zhang, Yancheng Yuan, Shuyuan Zhu, Lu Fang.
Proc. of IEEE International Conference on Image Processing (ICIP), 2014.
(Top 10% paper award)