Huajie Qian

About Me

I'm currently working at Alibaba DAMO Academy on algorithmic innovations and implementations of optimization solvers for linear and mixed integer programs. I earned my Ph.D. in Operations Research (thesis) at Columbia University in 2020, advised by Prof. Henry Lam. Before that, I obtained my B.S. degree in Mathematics from Fudan University and M.S. degree in Applied and Interdisciplinary Mathematics from University of Michigan.

My research focuses on developing statistically and computationally efficient data-driven methodologies for Monte Carlo simulation, stochastic and simulation-based optimization. I've also worked on related AIOps applications such as resource scaling in cloud computing.

Publications

Journal Articles

• Subsampling to Enhance Efficiency in Input Uncertainty Quantification, with H. Lam, Operations Research, 70(3):1891-1913, 2022.

• Optimization-Based Calibration of Simulation Input Models, with A. Goeva, H. Lam and B. Zhang, Operations Research, 67(5):1362-1382, 2019.

• Optimization-Based Quantification of Simulation Input Uncertainty via Empirical Likelihood, with H. Lam, under revision in Management Science.

• Bounding Optimality Gap in Stochastic Optimization via Bagging: Statistical Efficiency and Stability, with H. Lam, under revision in Operations Research.

• Combating Conservativeness in Data-Driven Optimization under Uncertainty: A Solution Path Approach, with H. Lam, under revision in Management Science.

Refereed Conference Proceedings

• HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent, with Z. Chen, J. Lu, X. Wang and W. 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, with Z. Zhou, C. Zhang, L. Ma, J. Gu, Q. Wen, L. Sun, P. Li and Z. Tang, AAAI/IAAI/EAAI, 2023.

• RobustScaler: QoS-Aware Autoscaling for Complex Workloads, with Q. Wen, L. Sun, J. Gu, Q. Niu and Z. Tang, Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE), 2022.

• CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms, with Y. Zhang et al., Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

• Learning Prediction Intervals for Regression: Generalization and Calibration, with H. Chen, Z. Huang, H. Lam and H. Zhang, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

• Validating Optimization with Uncertain Constraints, with H. Lam, Proceedings of the Winter Simulation Conference (WSC), 2019.

• Random Perturbation and Bagging to Quantify Input Uncertainty, with H. Lam, Proceedings of the Winter Simulation Conference (WSC), 2019.

• Subsampling Variance for Input Uncertainty Quantification, with H. Lam, Proceedings of the Winter Simulation Conference (WSC), 2018.

• Assessing Solution Quality in Stochastic Optimization via Bootstrap Aggregating, with H. Lam, Proceedings of the Winter Simulation Conference (WSC), 2018.

• The Empirical Likelihood Approach to Simulation Input Uncertainty, with H. Lam, Proceedings of the Winter Simulation Conference (WSC), 2016.