Publications

* Equal Contribution
<> Additional Presentation at a Workshop

Journals and Conference Proceedings

• Medical Deep Research: Orchestrating Large Language Model Agents and Resources for Medical Investigation.
  Yuan Li, Hengjin Zhu, Claire Liu, Matthew Pan, Yingtao Luo.
  NeurIPS 2025 GenAI4Health Workshop. [Code]

• Benchmarking Waitlist Mortality in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset.
  Yingtao Luo, Rema Padman, Carlos Martinez, Reza Skandari, Arman Kilic.
  American Medical Informatics Association Annual Symposium (AMIA 2025). [Paper]
  

• Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting.
  Yingtao Luo, Shikai Fang, Binqing Wu, Qingsong Wen, Liang Sun.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025). [Code] [Paper] [DOI]

• Fairness without Demographics through Learning Graph of Gradients.
  Yingtao Luo, Zhixun Li, Qiang Liu, Jun Zhu.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025). [Code] [Paper] [DOI]

• Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training.
  Yunshu Wu, Yingtao Luo, Xianghao Kong, Evangelos E. Papalexakis, Greg Ver Steeg.
  Advances in Neural Information Processing Systems 37 (NeurIPS 2024). [Code] [Paper]

• From One to Zero: RAG-IM Adapts Language Models for Interpretable Zero-Shot Predictions on Clinical Tabular Data.
  Sazan Mahbub, Caleb Ellington, Sina Alinejad, Kevin Wen, Yingtao Luo, Ben Lengerich, Eric P Xing.
  Machine Learning for Health Symposium 2024. <NeurIPS 2024 TRL & AFM>. [Paper]

• Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning.
  Jannik Deuschel*, Caleb N Ellington*, Yingtao Luo, Benjamin J Lengerich, Pascal Friederich, Eric P Xing.
  International Conference on Machine Learning (ICML 2024). [Code] [Paper]

• BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition.
  Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun.
  International Conference on Machine Learning (ICML 2024) [Spotlight Paper (191/9473)]. [Code] [Paper]
  

• Fairness without Demographics on Electronic Health Records.
  Yingtao Luo, Zhixun Li, Qiang Liu, Jun Zhu.
  AAAI 2024 Spring Symposium on Clinical Foundation Models. [Code] [Paper]
  

• GSLB: The Graph Structure Learning Benchmark.
  Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen,
  Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu.
  Advances in Neural Information Processing Systems 36 (NeurIPS 2023). [Code] [Paper]

• Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations.
  Yingtao Luo*, Qiang Liu*, Yuntian Chen, Wenbo Hu, Tian Tian, Jun Zhu.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023).
  <NeurIPS 2022 AI for Science>. [Code] [Paper]

• Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training.
  Juyong Jiang*, Peiyan Zhang*, Yingtao Luo*, Chaozhuo Li,
  Jae Boum Kim, Kai Zhang, Senzhang Wang, Sunghun Kim, Philip S. Yu
  IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE). [Code] [Paper]

• AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation.
  Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jaeboum Kim,
  Kai Zhang, Senzhang Wang, Xing Xie, Sunghun Kim.
  ACM International Conference on Information and Knowledge Management (CIKM 2023). [Code] [Paper]

• Deep Stable Representation Learning on Electronic Health Records.
  Yingtao Luo, Zhaocheng Liu, Qiang Liu.
  IEEE International Conference on Data Mining (ICDM 2022). [Code] [Paper] [arXiv]

• Learning differential operators for interpretable time series modeling.
  Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022). [Paper] [Poster]

• Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE).
  Yuntian Chen, Yingtao Luo, Qiang Liu, Hao Xu, Dongxiao Zhang.
  Physical Review Research. [Code] [PDF] [Paper] [media (机器之心)]

• RMT-Net: Reject-aware Multi-Task Network for Financial Credit Scoring.
  Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang.
  IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE). [Paper]

• STAN: Spatio-Temporal Attention Network for the Next Location Recommendation.
  Yingtao Luo, Qiang Liu, Zhaocheng Liu.
  The Web Conference (WWW 2021). [Code] [Paper] [talk] [media (AI科技评论)].

• Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures.
  Ying-Tao Luo, Peng-Qi Li, Dong-Ting Li, Yu-Gui Peng, Zhi-Guo Geng,
  Shu-Huan Xie, Yong Li, Andrea Alù, Jie Zhu, Xue-Feng Zhu.
  Research (Science Partner Journal). <NeurIPS 2020 ML4PS>. [Code] [Paper] [media (Science)]

• Deep spatial representation learning of polyamide nanofiltration membranes.
  Ziyang Zhang*, Yingtao Luo*, Huawen Peng, Yu Chen, Rong-Zhen Liao, Qiang Zhao.
  Journal of Membrane Science. <NeurIPS 2020 ML4Mol>. [Code] [Paper]

Conference Abtracts and Talks

• Learning from Simulated Patient Trajectories: A Sequential Decision-Making Agent for Heart Transplantation.
  Yingtao Luo, Rema Padman, Carlos Martinez, Reza Skandari, Arman Kilic.
  INFORMS Annual Meeting 2025 (Invited Talk). [2025-10-26, Atlanta, GA, USA] [Abstract]

• Benchmarking Waitlist Mortality in Heart Transplantation Through Time-to-Event Modeling using New UNOS Dataset.
  Yingtao Luo, Rema Padman, Carlos Martinez, Reza Skandari, Arman Kilic.
  Machine Learning for Healthcare (MLHC 2025). [2025-08-15, Rochester, MN, USA] [Abstract]

• On Interpretable Heart Transplant Mortality Prediction and Benchmarking.
  Yingtao Luo, Rema Padman, Carlos Martinez, Reza Skandari, Arman Kilic.
  World Transplant Congress 2025 (Oral Presentation). [2025-08-03, San Francisco, CA, USA] [Abstract]

• Interpretable Mortality Simulation and Decision-Making Agent for Heart Transplantation.
  Yingtao Luo, Rema Padman, Reza Skandari, Arman Kilic.
  INFORMS Annual Meeting 2024 (Invited Talk). [2024-10-21, Seattle, WA, USA] [Abstract]

Ongoing/Submitted Works

• Interpretable Heart Transplant Mortality Prediction and Benchmarking.
  Yingtao Luo, Rema Padman, Reza Skandari, Arman Kilic.

• LLM-Empowered Medical Patient Communication: A Data-Centric Survey From a Clinical Perspective.
  Ruosi Shao, Md Shamim Seraj, Kangyi Zhao, Yingtao Luo*, Lincan Li, Bolin Shen, Averi Bates, Yue Zhao, Chongle Pan, Lisa Hightow-Weidman, Shayok Chakraborty, Yushun Dong.

• Self-Evolving Agentic LLMs for Financial Sentiment Analysis through Reflective Reasoning.
  Kangyi Zhao, Xinyu Wei, Luojia Liu, Shuwei Liu, Manqi Cai, Yushun Dong, Yingtao Luo.

• Using LLMs to detect statistical anomalies in real-world data: Combining Glass-box learning with Generative AI.
  Benjamin Lengerich, Sebastian Bordt, Yingtao Luo, Mark E. Nunnally, Ryan Crowley, William R. Small, Theodore M. Hill, Rich Caruana.