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Huajie Qian
About Me
I'm a Staff Algorithm Engineer in the Decision Intelligence Lab at Alibaba DAMO Academy, where I innovate and implement optimization algorithms for large-scale mixed-integer linear programs using both classical and machine-learning techniques. I earned my Ph.D. in Operations Research at Columbia University in 2020 under the supervision of Prof. Henry Lam, developing statistically and computationally efficient data-driven methodologies for Monte Carlo simulation and stochastic optimization. Before that, I obtained my M.S. degree in Applied and Interdisciplinary Mathematics from the University of Michigan and my B.S. degree in Mathematics from Fudan University.
Publications
Refereed Conference Proceedings
Simultaneous Confidence Bounds for Aggregated Effects via Exact Subset Optimization
Weihang Xu*, Huajie Qian*, Wotao Yin, Xinshang Wang
Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026.
Subsampled Ensemble Can Improve Generalization Tail Exponentially
Huajie Qian, Donghao Ying, Henry Lam, Wotao Yin
Advances in Neural Information Processing Systems (NeurIPS), 2025.
HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent
Ziang Chen, Jianfeng Lu, Huajie Qian, Xinshang Wang, Wotao Yin†
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu, Huajie Qian, Qingsong Wen, Liang Sun, Peng Li, Zhimin Tang
Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
RobustScaler: QoS-Aware Autoscaling for Complex Workloads
Huajie Qian, Qingsong Wen, Liang Sun, Jing Gu, Qiulin Niu, Zhimin Tang
Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE), 2022.
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms
Yingying Zhang, Zhengxiong Guan, Huajie Qian, Leili Xu, Hengbo Liu, Qingsong Wen, Liang Sun, Junwei Jiang, Lunting Fan, Min Ke
Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.
Learning Prediction Intervals for Regression: Generalization and Calibration
Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang†
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
Validating Optimization with Uncertain Constraints
Henry Lam, Huajie Qian†
Proceedings of the Winter Simulation Conference (WSC), 2019.
Random Perturbation and Bagging to Quantify Input Uncertainty
Henry Lam, Huajie Qian†
Proceedings of the Winter Simulation Conference (WSC), 2019.
Subsampling Variance for Input Uncertainty Quantification
Henry Lam, Huajie Qian†
Proceedings of the Winter Simulation Conference (WSC), 2018.
Assessing Solution Quality in Stochastic Optimization via Bootstrap Aggregating
Henry Lam, Huajie Qian†
Proceedings of the Winter Simulation Conference (WSC), 2018.
The Empirical Likelihood Approach to Simulation Input Uncertainty
Henry Lam, Huajie Qian†
Proceedings of the Winter Simulation Conference (WSC), 2016.
Journal Articles
Subsampling to Enhance Efficiency in Input Uncertainty Quantification
Henry Lam, Huajie Qian†
Operations Research, 70(3):1891–1913, 2022.
Optimization-Based Calibration of Simulation Input Models
Aleksandrina Goeva, Henry Lam, Huajie Qian, Bo Zhang†
Operations Research, 67(5):1362–1382, 2019.
Optimization-Based Quantification of Simulation Input Uncertainty via Empirical Likelihood
Henry Lam, Huajie Qian†
under revision in Management Science.
Bounding Optimality Gap in Stochastic Optimization via Bagging: Statistical Efficiency and Stability
Henry Lam, Huajie Qian†
under revision in Mathematics of Operations Research.
Combating Conservativeness in Data-Driven Optimization under Uncertainty: A Solution Path Approach
Henry Lam, Huajie Qian†
under revision in Management Science.
† authors are listed in alphabetical order.
* denotes equal contribution.
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