[0] Ryan F. Lin$^{\dagger}$, Keyu Tian$^{\dagger}$, Congjing Zhang$(\star)$, Hanming Zheng, Li Zeng, and Shuai Huang, CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models
Accepted byINFORMS Journal on Data Science

Runner-up of the 2025 INFORMS QSR best paper competition [Preprint]   [Slides]  

[1] Congjing Zhang$^{\dagger}(\star)$, Ryan F. Lin$^{\dagger}$, Xinyi Zhao, Pei Guo, Wei Li, Lin Chen, Chaoyue Zhao, and Shuai Huang, ALARM: Automated LLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification
Accepted by INFORMS Journal on Data Science

[Preprint]  

[2] Ryan F. Lin, Chaoyue Zhao, Xiaoning Qian, Kendra Vehik, and Shuai Huang, Fair Collaborative Learning (FairCL): A Method to Improve Fairness amid Personalization
INFORMS Journal on Data Science, 2025

Finalist of the 2023 INFORMS QSR best paper competition [Paper]  

[3] Ryan F. Lin, Xiaoning Qian, Bobak Mortazavi, Zhangyang Wang, Shuai Huang, and Cynthia Chen, Modeling User Choice Behavior under Data Corruption: Robust Learning of the Latent Decision Threshold Model
IISE transactions, 2024

[Paper]  

[0] Yuantao Wei$^{\dagger}(\star)$, Huiling Liao$^{\dagger}$, Ryan F. Lin$^{\dagger}$, Xiaoning Qian, and Shuai Huang, Mixture-of-Chains: Learning Causal Graphs from Human Knowledged
Under review

[Preprint]  

[1] Congjing Zhang$^{\dagger}(\star)$, Ryan F. Lin$^{\dagger}$, Noelle So, Meera Patel, Mingqian Li, Huiling Liao, Lin Chen, Xiaoning Qian, and Shuai Huang, LIFT: Agentic LLMs for Lifecycle-Based Workflow Testing in Healthcare Risk Prediction
Under review

[Preprint]  

[2] Ryan F. Lin$^{\dagger}$, Yuantao Wei$^{\dagger}(\star)$, Huiling Liao, Xiaoning Qian, and Shuai Huang, Causal Discovery Should Embrace the Wisdom of the Crowd
Under review

[Preprint]  

[3] Congjing Zhang$^{\dagger}(\star)$, Ryan F. Lin$^{\dagger}$, and Shuai Huang, Team, then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
Under review

Finalist of the 2026 IISE DAIS Best Student Paper Competition [Preprint]  

[4] William Yang, Ryan F. Lin, Chaoyue Zhao, and Shuai Huang, A Rawlsian Mixed Integer Programming Approach for Fair Classification
In Proc. ofthe INFORMS Optimization Society Conference 2026

[Paper]  

[5] Yunkai Zhang, Qiang Zhang, Diji Yang, Ryan F. Lin, Ruizhong Qiu, Benyu Zhang, Hanchao Yu, Jason Liu, Yinglong Xia, Zhuokai Zhao, Lizhu Zhang, Xiangjun Fan, Zhuoran Yu, Abhishek Kumar, and Zeyu Zheng, Guiding Generative Recommender Systems via Structured Human Priors with Multi-head Decoding
In Proc. ofthe ACM Web Conference (WWW) 2026, 2026

Acceptance rate $20.1\%$ in the year of submission. [Paper]  

[6] Junyuan Hong, Huanhuan Chen, and Ryan F. Lin, Disturbance Grassmann Kernels for Subspace-Based Learning
In Proc. of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018

Acceptance rate $18.4\%$ in year of submission. [Paper]   [Video]  

[0] Ryan F. Lin, Ji Liu, and Shuai Huang, Learning to Collect: A Two-Stage Data Collection Framework for Data-Efficient Personalization
In submission to INFORMS Journal on Data Science

[Preprint]  

[1] Ryan F. Lin, Xufeng Cai, Lei Yuan, Boying Liu, Ali Selman Aydin, Ziwei Guan, Wenbo Ren, Yuting Zhang, Qunshu Zhang, Shuai Huang, Yinglong Xia, and Ji Liu, Robust Contextual Optimization for Personalization

[Preprint coming soon...]  

[2] Ryan F. Lin, Guang Zhao, Xiaoning Qian, and Shuai Huang, Relieving the Myopia: Bayesian Active Learning by Mean Objective Cost of Uncertainty with Confidence Ascending

[Preprint coming soon...]