Faculty Director, Master's in Business Analytics (MSBA-PT), Business Analytics

Henry B. Tippie Research Fellow, Business Analytics

Associate Professor, Business Analytics

PhD in Algorithms, Combinatorics, and Optimization, Carnegie Mellon University

BS in Mathematical Science, Tsinghua University

Continuous optimization, first-order methods, distributed optimization, error bound conditions

Machine learning, predictive and prescriptive analytics, big data analysis, fairness in AI

Markov decision processes

Runner-up, Best Paper Award - INFORMS Workshop on Data Science, 2019

MBA Business Analytics Professor of the Year - Tippie College of Business, 2018 - 2019

Early Career Faculty Research Award - Tippie College of Business, 2018

Best Paper Award - INFORMS Workshop on Data Science, 2017

Qi, Q., Hu, Q., Lin, Q., & Yang, T. Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning. In Proceedings of Conference on Neural Information Processing Systems 2024.

Pakiman, P., Nadarajah, S., Soheili, N., & Lin, Q. (2024). Self-Guided Approximate Linear Programs: Randomized Multi-Shot Approximation of Discounted Cost Markov Decision Processes. Management Science.

Huang, Y. & Lin, Q. Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization. In Proceedings of Advances in Neural Information Processing Systems 2023.

Yao, Y., Lin, Q., & Yang, T. Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. In Proceedings of International Conference on Artificial Intelligence and Statistics 2023.

Lin, Q. & Xu, Y. (2023). Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method Problem. SIAM Journal on Optimization. 33 (1) pp. 1-35.

Yao, Y., Lin, Q., & Yang, T. Large-scale Optimization of Partial AUC in a Range of False Positive Rates. In Proceedings of Conference on Neural Information Processing Systems 2022.

Lin, Q., Ma, R., & Xu, Y. (2022). Complexity of an Inexact Proximal-Point Penalty Method for Constrained Smooth Non-Convex Optimization. Computational Optimization and Applications. 82 pp. 175–224.

Ragodos, R., Wang, T., Lin, Q., & Zhou, X. ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. In Proceedings of Conference on Neural Information Processing Systems 2022.

Liu, M., Rafique, H., Lin, Q., & Yang, T. (2021). First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems. Journal of Machine Learning Research.

Wang, T. & Lin, Q. (2021). Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. Journal of Machine Learning Research.

"Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization," Guest/Invited Speaker at SIAM Conference on Optimization, July 2023.

Lin, Q. (Co-Principal), Principal Investigator(s): Tianbao Yang. FAI: Advancing Optimization for Threshold-Agnostic Fair AI Systems. National Science Foundation (NSF). Funded. August 2022 - July 2025.

Lin, Q. (Principal Investigator). Advance Health Equity by Fairness-Aware Machine Learning: An Optimization-based Approach with Threshold-Agnostic Fairness Constraints. University of Iowa. Funded. April 2022 - March 2023.

Lin, Q. (Co-Investigator), Principal Investigator(s): Stephen Baek. ImagiQ: Asynchronous and Decentralized Federated Learning for Medical Imaging. National Science Foundation (NSF). Completed. September 2020 - May 2022.

Liu, W., Lin, Q., & Xu, Y. (2024). First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods.

Lin, Q., Soheili, N., Nadarajah, S., & Ma, R. (2024). A Parameter-free and Projection-free Restarting Level Set Method for Adaptive Constrained Convex Optimization Under the Error Bound Condition.

Yao, Y., Lin, Q., & Yang, T. (2023). Deterministic and Stochastic Accelerated Gradient Method for Convex Semi-Infinite Optimization.

Huang, Y., Lin, Q., Street, W. N., & Baek, S. (2022). Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization.

Faculty Director, Master's in Business Analytics (MSBA-FT)), Business Analytics, University of Iowa, 2023 - 2024

Referee/Reviewer, Mathematical Programming, 2021-22.

Referee/Reviewer, SIAM Journal on Optimization, 2021-22.

Referee/Reviewer, Journal of Machine Learning Research, 2022-23.

Referee/Reviewer, SIAM Journal on Mathematics of Data Science, 2022-23.

Referee/Reviewer, Open Journal of Mathematical Optimization, 2022-23.

Referee/Reviewer, IEEE Transactions on Signal Processing, 2021-22.

Referee/Reviewer, IEEE Open Journal of the Computer Society, 2020-21.

Referee/Reviewer, INFORMS Journal on Computing, 2020-21.

Referee/Reviewer, Mathematical Methods of Operations Research, 2020-21.

Referee/Reviewer, Naval Research Logistics, 2020-21.