[0] 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
Major revision at INFORMS Journal on Data Science

[Paper]   [slides]  

[1] Ryan F. Lin$^{\dagger}$, Keyu Tian$^{\dagger}$, Congjing Zhang$(\star)$, Hanming Zheng, Li Zeng, and Shuai Huang, CrowdLLM: A Synthetic Crowd of LLM-based Decision-Makers Augmented with Generative Models
Under review at INFORMS Journal on Data Science

Runner-up of the 2025 INFORMS QSR best paper competition [Paper]   [slides]  

[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] 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 at the 14th International Conference on Learning Representations (ICLR)

[Preprint]  

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

[Preprint]  

[2] Junyuan Hong, Huanhuan Chen, and Ryan F. Lin, Disturbance Grassmann Kernels for Subspace-Based Learning
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$^{\dagger}$, Congjing Zhang$^{\dagger}(\star)$, Kendra Vehik, Hemang Parikh, Mingqian Li, Richard Oram, Xiaoning Qian, and Shuai Huang, Benchmarking Fairness of Genetic Risk Score Models for Early-stage Prediction of Type 1 Diabetes from the TEDDY study
To be submitted to JAMA Network Open

[Preprint]  

[2] Yuantao Wei$^{\dagger}(\star)$, Ryan F. Lin$^{\dagger}$, and Shuai Huang, Learning Causal Graph from Human Knowledge: A Mixture-of-Chains Framework
To be submitted to INFORMS Journal on Data Science

[pdf]   [slides]   [video]  

[3] 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

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

[pdf]   [slides]   [video]