I am a Ph.D. Candidate in the Department of Industrial and Operations Engineering at the University of Michigan, where I am fortunate to be advised by Professor Ruiwei Jiang. Previously, I received a B.S. in Mathematics and Physics and a B.E. in Industrial Engineering from Tsinghua University in 2024.
Research
I study decision-making under uncertainty through two connected directions:
- Scalable methods for stochastic optimization, robust optimization, and integer programming.
- Trustworthy decision-making with incomplete, biased, or AI-generated data.
Please feel free to reach out if you would like to chat!
1205 Beal Ave
Ann Arbor, Michigan 48109
Recent Papers
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2026
Soft Separation for Adaptive Robust Optimization
Preprint · arXiv:2609.36275
Short summary of Soft Separation for Adaptive Robust Optimization
How can adaptive robust optimization avoid repeated expensive separation while preserving solution guarantees?
@misc{xu2026softseparationadaptiverobust, title={Soft Separation for Adaptive Robust Optimization}, author={Qingyuan Xu and Ruiwei Jiang}, year={2026}, eprint={2609.36275}, archivePrefix={arXiv}, primaryClass={math.OC}, url={https://arxiv.org/abs/2609.36275} } -
2026
Robust Contextual Optimization with Missing Covariates
Preliminary version accepted to ICMLSpotlight & Oral0.7%
Short summary of Robust Contextual Optimization with Missing Covariates
How can decisions informed by context remain reliable when relevant covariates are partially missing?
@InProceedings{pmlr-v306-xu26v, title={Robust Contextual Optimization with Missing Covariates}, author={Xu, Qingyuan and Jiang, Ruiwei}, booktitle={Proceedings of the 43rd International Conference on Machine Learning}, pages={141106--141167}, year={2026}, volume={306}, series={Proceedings of Machine Learning Research}, publisher={PMLR}, url={https://proceedings.mlr.press/v306/xu26v.html} }