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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
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publications
A Diffusion Model with Contrastive Learning for ICU False Arrhythmia Alarm Reduction
Published in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023
Recommended citation: F. Wu, G. Zhao, X. Qian, L.H. Lehman. (2023). "A Diffusion Model with Contrastive Learning for ICU False Arrhythmia Alarm Reduction." Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023).
VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors
Published in Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
Recommended citation: L. Lehman, B. Moody, H. Deep, F. Wu, H. Saeed, L. McCullum, D. Perry, et al. (2023). "VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors." Advances in Neural Information Processing Systems 36, 38827-38843.
Forecasting Treatment Outcomes Over Time Using Alternating Deep Sequential Models
Published in IEEE Transactions on Biomedical Engineering, 2023
Recommended citation: F. Wu, G. Zhao, Y. Zhou, X. Qian, B.K. Elias, L.H. Lehman. (2023). "Forecasting Treatment Outcomes Over Time Using Alternating Deep Sequential Models." IEEE Transactions on Biomedical Engineering, 71(4), 1237-1246.
G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes
Published in Proceedings of the 9th Machine Learning for Healthcare Conference (MLHC 2024), PMLR 252, 2024
Recommended citation: H. Xiong*, F. Wu*, L. Deng, M. Su, L.H. Lehman. (2024). "G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes." Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252. (*equal contribution)
Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes
Published in Proceedings of Machine Learning Research 259 (ML4H 2024), 2024
Recommended citation: L. Deng, H. Xiong, F. Wu, S. Kapoor, S. Ghosh, Z. Shahn, L.H. Lehman. (2024). "Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes." Proceedings of Machine Learning Research, 259, 248.
Improving Conversational Recommendation System through Personalized Preference Modeling and Knowledge Graph
Published in IEEE Transactions on Knowledge and Data Engineering, 2024
Recommended citation: F. Wu, G. Zhao, T. Li, J. Shen, X. Qian. (2024). "Improving Conversational Recommendation System through Personalized Preference Modeling and Knowledge Graph." IEEE Transactions on Knowledge and Data Engineering, 36(12), 8529-8540.
Alleviating User-Sensitive Bias with Fair Generative Sequential Recommendation Model
Published in International Conference on Intelligent Computing (ICIC 2025), 2025
Recommended citation: Y. Liu, F. Wu, M.X. Zhu. (2025). "Alleviating User-Sensitive Bias with Fair Generative Sequential Recommendation Model." International Conference on Intelligent Computing, 223-235.
GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
Published in Advances in Neural Information Processing Systems (NeurIPS 2025), 2025
Recommended citation: X. Lan, F. Wu, K. He, Q. Zhao, S. Hong, M. Feng. (2025). "GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images." NeurIPS 2025.
HRRT: Hierarchical Reinforcement Learning for Renal Replacement Therapy Decision Support
Published in npj Digital Medicine, 2026
Recommended citation: Q. Xu, F. Wu, Z.Y.C. Thong, M.S.L. Goh, P. Chen, J. Yang, C. Huang, Z. Zhang, et al. (2026). "HRRT: Hierarchical Reinforcement Learning for Renal Replacement Therapy Decision Support." npj Digital Medicine.
medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions
Published in arXiv preprint arXiv:2602.03305, 2026
Recommended citation: Q. Xu, G. Habib, F. Wu, Y. Du, Z. Chen, S. Mishra, D. Perera, M. Feng. (2026). "medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions." arXiv preprint arXiv:2602.03305. https://arxiv.org/abs/2602.03305
Structured Prototype-Guided Adaptation for EEG Foundation Models
Published in arXiv preprint arXiv:2602.17251, 2026
Recommended citation: J. Ma*, F. Wu*, Y. Xing, Q. Lin, T. Liu, C. Liu, Z. Jia, M. Feng. (2026). "Structured Prototype-Guided Adaptation for EEG Foundation Models." arXiv preprint arXiv:2602.17251. (*equal contribution) https://arxiv.org/abs/2602.17251
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Published in International Conference on Learning Representations (ICLR 2026), 2026
Recommended citation: J. Ma*, F. Wu*, Q. Lin, Y. Xing, C. Liu, Z. Jia, M. Feng. (2026). "CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model." ICLR 2026. (*equal contribution)
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Published in arXiv preprint arXiv:2606.19888, 2026
Recommended citation: F. Wu, H. Deep, E. Lehman, S. Kapoor, G. Zhao, R. Krishnan, G. Clifford, et al. (2026). "SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models." arXiv preprint arXiv:2606.19888. https://arxiv.org/abs/2606.19888
MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), 2026
Recommended citation: Q. Xu, G. Habib, F. Wu, D. Perera, M. Feng. (2026). "MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support." KDD 2026.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.