Xihe Qiu

Associate Professor
School of Electronic and Electrical Engineering · Shanghai University of Engineering Science
Office: No.333 Longteng Road, Shanghai, 201620
Email: qiuxihe@sues.edu.cn

I am an Associate Professor at School of Electronic and Electrical Engineering, Shanghai University of Engineering Science. I completed my undergraduate studies in Biomedical Engineering at Northeastern University in 2014. Subsequently, I obtained my Ph.D. from the National University of Singapore in September 2018. My research interest is AI for Healthcare, such as artificial intelligence and its applications on healthcare.

Our Team

Group Research Interests

My research interests include artificial intelligence and its applications on medical image computing, robotic surgical data science. I recently work on spatial-temporal representation learning, data efficient learning, and multi-modality learning.

Key Achievements & Contributions

Academic Publications

In the past five years, I have published over 80 papers in top-tier international conferences and high-impact journals. These include CCF A-ranked conferences such as ICCV, WWW, ICML, KDD, SIGIR, EMNLP, and ICRA, as well as journals such as IEEE Transactions on Emerging Topics in Computational Intelligence and IEEE Transactions on Artificial Intelligence. I am the first or corresponding author on more than 50 of these publications.

Research Grants (as PI)

  • National Natural Science Foundation of China (NSFC) - Young Scientists Fund
  • Shanghai Natural Science Foundation - General Program
  • Shanghai Soft Science Research Project - Young Scientists Program
  • Shanghai Rising-Star Program for Young University Teachers
  • Completed 5 industry-commissioned projects and internal university grants

Graduate Student Supervision

Supervised over 20 Master's students, with 12 graduates to date. Notable student achievements include:

  • Yongxin Deng was admitted to University of Technology Sydney for Ph.D. studies with full Scholarchip.
  • Shaojie Shi was admitted to Fudan University and Haoyu Wang was admitted to Tongji University for Ph.D. studies.
  • One student was honored as a 'Shanghai Outstanding Graduate'.
  • Two students received 'Outstanding Master's Thesis' awards.
  • Four students awarded National Scholarships(Yajun Ru 2023, Jiahui Qian 2023, Haoyu Wang 2024, Gengchen Ma 2025).

Leadership & Key Collaborations

  • Leader for the 'Grand Health' initiative at the Shanghai Collaborative Innovation Center for Data Intelligence Technology and Its Applications.
  • Overseeing industry-academia-research collaborations with leading medical institutions, including the Eye and ENT Hospital of Fudan University, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Zhongshan Hospital affiliated to Fudan University, and Songjiang District Central Hospital affiliated to Shanghai Jiao Tong University School of Medicine.

Academic & Professional Services

  • Editorial Board Member, Complex & Intelligent Systems (CAS Q2 Journal).
  • Program Chair for ECAI 2024 and ECAI 2025.
  • Long-term reviewer for CCF Class A conferences including KDD, CVPR, ICCV, ICML, ACL, and AAAI.

Join Our Team!

*Opening!* My BIOAIT research group accepts 3-5 master's students annually. If you are interested in my research direction, please feel free to email me for further inquiries.

BIOAIT Research Group Team Photo

Recent Group News

*NEWS!* Our work is accepted by ACL 2026!(Author: Chen Zhan, Xiaoyu Tan, Gengchen Ma, Yu-Jie Xiong, Xiaoyan Jiang, Xihe Qiu.)

*NEWS!* Our work is accepted by ICLR 2026!(Author: Yuxuan Fu, Xiaoyu Tan, Teqi Hao, Chen Zhan, Xihe Qiu)

*NEWS!* Our work is accepted by WWW 2026!(Author: Sijia Li, Xiaoyu Tan, Shahir Ali, Niels Schmidt, Gengchen Ma, Xihe Qiu)

*NEWS!* Our work is accepted by IEEE TCDS!(Author: Xihe Qiu, Shaojie Shi, Teqi Hao, Xiaoyu Tan)

*NEWS!* Our work is accepted by IEEE Journal of Biomedical and Health Informatics!(Author: Xihe Qiu, Gengchen Ma, Haoyu Wang, Chen Zhan, Xiaoyu Tan, Shuo Li)

*NEWS!* Our work is accepted by IEEE transactions on Artificial Intelligence!(Author: Xihe Qiu,Leijun Cheng,et al.)

*NEWS!* Our work is accepted by IEEE Transactions on Cognitive and Developmental Systems!(Author:Haoyu Wang, Zhijun Fang, Xihe Qiu,et al.)

*NEWS!* Our work is accepted by Pattern Recognition!(Author: Xihe Qiu, Yingchen Wei,et al.)

*NEWS!* 3 papers are accepted by MICCAI 2025!(Authors: Chen zhan/Teqihao/Bin Li)

*NEWS!* 1 paper is accepted by IROS 2025!(Authors:Haoyu Wang,et al.)

*NEWS!* Our work is accepted by IEEE Transactions on Emerging Topics in Computational Intelligence!(Author: Yongxin Deng,Xihe Qiu Xiaoyu Tan,Yaochu Jin)

*NEWS!* Our work is accepted by IEEE transactions on Artificial Intelligence!(Author: Xihe Qiu, Haoyu Wang, Xiaoyu Tan,Yaochu Jin )

*NEWS!* Our work is accepted by KDD 2025!(Author: Xiaoyu Tan,Haoyu Wang, Xihe Qiu,et al.)

*NEWS!* Our work is accepted by WWW 2025!(Author: Xiaoyu Tan,Bin Li, Xihe Qiu,et al.)

*NEWS!* Our work is accepted as oral by ICRA 2024!(Author: Haoyu Wang, Xiaoyu Tan, Xihe Qiu, Chao Qu)

*NEWS!* Our work is accepted by SIGIR 2024!(Author: Xiaoyu Tan, Leijun Cheng, Xihe Qiu, et. al)

*NEWS!* Our work is accepted as by ICCV 2023!(Author: Xihe Qiu, Shaojie Shi, et. al)

*UPDATES* For all publications, please check my Google Scholar.

Research Topics

Sleep Breathing Disorder Research

Research on Comprehensive Personalized Assisted Diagnosis for Sleep Breathing Disorder Patients.

Participating group member: Yingchen Wei, Bin Li, Yue Zhang, Youwei Song.

Collaborator: Eye&ENT hospital of Fudan University, Fudan University.

In response to the practical challenges in diagnosing Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) with invasive treatments and cumbersome examinations, this project focuses on researching an intelligent assisted diagnosis algorithm for OSAHS throughout the entire patient journey. It aims to address the complexities, time-consuming nature, and high manpower costs associated with monitoring multi-channel polysomnography in clinical settings. By constructing a time-series model with high accuracy and generalization performance, the project precisely analyzes and assesses patients' snoring patterns. Leveraging this snoring time-series model, it achieves an accurate classification of disease severity, enabling a streamlined, precise, and cost-effective progressive clinical assisted diagnosis. This advancement aids hospitals in improving diagnostic efficiency and accuracy.

Video Nystagmography Research

Research on Video Nystagmography Classification for Benign Paroxysmal Positional Vertigo (BPPV).

Participating group member: Shaojie Shi

Collaborator: Eye&ENT hospital of Fudan University, Fudan University

The analysis of eye movement characteristics in BPPV patients is crucial for the diagnosis of BPPV. In clinical practice, eye movement information is typically obtained through positional tests. Video Nystagmography technology significantly enhances the detection rate of BPPV eye movements. In this study, we developed a computer-aided diagnostic method and compared it with manual diagnosis. The computer-aided diagnostic approach demonstrates objectivity, high efficiency, and considerable clinical value compared to traditional manual methods.

Time-Series Models Research

Research on Prediction Algorithms Based on Time-Series Models.

Participating group member: Haoyu Wang, Siyue Shao, Jiahui Qian, Yajun Ru

Collaborator: Eye&ENT hospital of Fudan University, Fudan University

Time-series models find extensive application across various domains, notably in the prediction of clinical events and energy-related forecasts. In the clinical domain, leveraging past observed clinical events (such as past medication instructions or previous laboratory measurements) or physiological signals enables the anticipation of a series of future events. This proactive approach enables healthcare practitioners to intervene in advance and prepare resources, thus enhancing the overall quality of patient care and preparedness for clinical events.

Large Language Models Research

Research on Developing More Reliable Large Language Models (LLMs)

Participating group member: Leijun Cheng, Shaojie Shi, Teqi Hao, Haoyu Wang

Collaborator: INF Technology (Shanghai) Co. Ltd, Fudan University

Large Language Models (LLMs), such as ChatGPT, GPT-4, BARD, Claude, have made rapid and remarkable progress in the field of natural language processing. Our aim is to create, enhance, fine-tune, and conduct research by integrating existing LLMs with our medical applications.

Pulmonary Tumors Research

Advancements in Image-Assisted Diagnostic Algorithms for Pulmonary Tumors.

Participating group member: Gengchen Ma, Yang Dai, Jiaxun Qin

Collaborator: Zhongshan Hospital of Fudan University

Numerous studies have shown that radiomics has significant advantages in the diagnosis and treatment of lung cancer. Radiomics has evolved into a valuable tool for assisting in the diagnosis, analysis, and prediction of lung cancer metastasis. Our objective is to research state-of-the-art algorithms to aid in the diagnostic process.

Intelligent Ventilator Systems Research

Research on Reliable and Intelligent Ventilator Systems and Data Analysis.

Participating group member: Teqi Hao, Weiyi Zhao, Sijia Li, Yuxuan Fu

Collaborator: Shanghai Jiao Tong University Affiliated Songjiang Hospital

This research focuses on developing advanced algorithms for respiratory machine control, data-driven optimization of ventilation parameters, and intelligent decision support systems for clinicians. We aim to enhance the efficacy of mechanical ventilation and improve patient outcomes through real-time data analysis and smart adjustments.

Cooperative Organizations

Cooperative Organizations Hospitals

Fundings

Research on Model-Constrained Reinforcement Learning-Driven Clinical Decision Support Method

Funded by the National Natural Science Foundation. Amount: ¥300,000
2021 - 2024

Research on Comprehensive Diagnostic Algorithm Application for Sleep Breathing Disorders Patients

Funded by the Shanghai Natural Science Foundation. Amount: ¥200,000
2023 - 2026

Youth AI Innovation Project Design and Guidance

Commissioned by a corporate entity. Amount: ¥450,000
2020 - 2023

Intelligent Diagnosis of Sleep Breathing Disorders - Interdisciplinary Innovation Team

Affiliated with the Eye&ENT hospital of Fudan University, Fudan University. Amount: ¥300,000
2022 - 2025

Publications

Note: * denotes corresponding author. Within each year, publications are ordered as CCF-A, SCI Q1, CCF-B, SCI Q2, and IEEE Transactions. Only publications in these categories are listed below. For a comprehensive list, please refer to my Google Scholar profile.

2026

  1. Fu, Y., Tan, X., Hao, T., Zhan, C., & Qiu, X.* (2026). "PRISM: Festina lente proactivity-Risk-sensitive, uncertainty-aware deliberation for proactive agents." The International Conference on Learning Representations 2026.
  2. Zhan, C., Tan, X., Ma, G., Xiong, Y. J., Jiang, X., & Qiu, X.* (2026). "From answers to arguments: Toward trustworthy clinical diagnostic reasoning with Toulmin-guided curriculum goal-conditioned learning." Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics.
  3. Li, S., Tan, X., Ali, S., Schmidt, N., Ma, G., & Qiu, X.* (2026). "Curiosity driven knowledge retrieval for mobile agents." Proceedings of the ACM Web Conference 2026, 5198-5209.
  4. Tan, X., Yao, T., Qu, C., Li, B., Yang, M., Lu, D., ... & Qiu, X.* (2026, August). "Aurora: Automated training framework of universal process reward models via ensemble prompting and reverse verification." In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1 (pp. 1378-1389).
  5. Qiu, X., Shi, S., Hao, T., & Tan, X. (2026). "CLAIM: Mitigating catastrophic forgetting in continual instruction fine-tuning large language models." IEEE Transactions on Cognitive and Developmental Systems, 1-11. DOI: 10.1109/TCDS.2026.3674105.
  6. Qiu, X., Wei, Y., Tan, X., Xu, W., Wang, H., Ma, J., Huang, J., & Fang, Z. (2026). "MIMAR-OSA: Enhancing obstructive sleep apnea diagnosis through multimodal data integration and missing modality reconstruction." Pattern Recognition, 169, 111917.
  7. Dai, Y., Tan, X., Wang, H., Ma, G., Xiong, Y., & Qiu, X.* (2026). "MDM-DTA: Message passing neural network with molecular descriptors and mixture of experts for drug-target affinity prediction." Computer Methods and Programs in Biomedicine, 274, 109163.

2025

  1. Tan, X., Li, B., Qiu, X.*, Qu, C., Chu, W., Xu, Y., & Qi, Y. (2025). "Meta-Agent-Workflow: Streamlining tool usage in LLMs through workflow construction, retrieval, and refinement." Companion Proceedings of the ACM on Web Conference 2025, 458-467.
  2. Tan, X., Wang, H., Qiu, X.*, Cheng, L., Cheng, Y., Chu, W., ... & Qi, Y. (2025, July). "Struct-X: Enhancing the reasoning capabilities of large language models in structured data scenarios." In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1 (pp. 2584-2595).
  3. Wang, H., Qiu, X.*, Xiong, Y., & Tan, X. (2025). "AutoGRN: An adaptive multi-channel graph recurrent joint optimization network with Copula-based dependency modeling for spatio-temporal fusion in electrical power systems." Information Fusion, 117, 102836.
  4. Deng, Y., Qiu, X.*, Tan, X., & Jin, Y. (2025). "FedSlate: A federated deep reinforcement learning recommender system." IEEE Transactions on Emerging Topics in Computational Intelligence, 9(6), 4202-4216.
  5. Cheng, L., Qiu, X.*, Tan, X., Wang, H., & Xiong, Y. (2025). "An innovative contrastive learning approach to improve image recognition robustness and interpretability via simulated environmental perturbations." Engineering Applications of Artificial Intelligence, 159, 111619.
  6. Wang, H., Tan, X., Yao, T., Fang, Z., Qi, P., & Qiu, X.* (2025). "Learning expressive task embeddings and sample-efficient exploration for context shift reduction in offline meta-reinforcement environment." IEEE Transactions on Cognitive and Developmental Systems, 1-15. DOI: 10.1109/TCDS.2025.3627812.
  7. Deng, Y., Qiu, X.*, Chen, J., & Tan, X. (2025). "Reward guidance for reinforcement learning tasks based on large language models: The LMGT framework." Knowledge-Based Systems, 322, 113689.
  8. Qiu, X., Ma, G., Wang, H., Zhan, C., Tan, X., & Li, S. (2025). "EEG-VLM: A Hierarchical Vision-Language Model with Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Prediction." IEEE Journal of Biomedical and Health Informatics (J-BHI), In Press, Early Access.
  9. Qiu, X., Shi, S., Li, B., Tan, X., Gao, Y., & Li, S. (2025). "LLM-GAODE: Large-language-model augmented neural ordinary differential equation network for video nystagmography classification." Knowledge-Based Systems, 326, 114050.
  10. Qiu, X., Shao, S., Wang, H., & Tan, X. (2025). "Bio-K-Transformer: A pre-trained transformer-based sequence-to-sequence model for adverse drug reactions prediction." Computer Methods and Programs in Biomedicine, 260, 108524.
  11. Tan, X., Hao, T., Qiu, X.*, Shi, S., Cheng, Y., Chu, W., Xu, Y., & Qi, Y. (2025). "Leave the bias in bias: Mitigating the label noise effects in continual visual instruction fine-tuning." 2025 IEEE International Conference on Multimedia and Expo, 1-7.
  12. Jiang, X., Yang, H., Zhu, K., Qiu, X.*, Zhao, S., & Zhou, S. (2025). "PTQ4RIS: Post-training quantization for referring image segmentation." 2025 IEEE International Conference on Robotics and Automation, 12663-12669.
  13. Deng, Y., Qiu, X.*, Tan, X., Qu, C., Pan, J., Cheng, Y., Xu, Y., & Chu, W. (2025). "CogniDual framework: Self-training large language models within a dual-system theoretical framework for improving cognitive tasks." ICASSP 2025 IEEE International Conference on Acoustics, Speech and Signal Processing.
  14. Hao, T., Tan, X., Li, B., Wang, X., Qu, C., Xu, Y., & Qiu, X.* (2025, September). "MVP-LLMs: Optimizing intervention timing and subsequent decision support for mechanical ventilation parameter control using large language models." In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 455-464).
  15. Tan, X., Li, B., Xu, W., Qu, C., Chu, W., Xu, Y., Qi, Y., & Qiu, X.* (2025, September). "Prolog-driven rule-based diagnostics with large language models for precise clinical decision support." In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 413-423).
  16. Qiu, X., Zhan, C., Ma, G., Huang, J., & Tan, X. (2025, September). "Robust Sleep Stage Prediction from Electroencephalogram with Label Noise Using Multimodal Large Language Models." In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 575-585).
  17. Wei, Y., Qiu, X.*, Tan, X., Huang, J., Chu, W., Xu, Y., & Qi, Y. (2025, April). "An attentive dual-encoder framework leveraging multimodal visual and semantic information for automatic OSAHS diagnosis." In ICASSP 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1-5).
  18. Li, B., Wang, H., Tan, X., Li, Q., Chen, J., & Qiu, X.* (2025). "Adaptive heterogeneous graph reasoning for relational understanding in interconnected systems." The Journal of Supercomputing, 81(1), 112.
  19. Chen, J., Liu, W., Qiu, X.*, Lv, W., & Xiong, Y. (2025). "CRGT-SA: An interlaced and spatiotemporal deep learning model for network intrusion detection." Frontiers of Information Technology & Electronic Engineering, 26(7), 1115-1130.
  20. Ma, G., Qiu, X.*, & Tan, X. (2025). "DMFusion: A dual-branch multi-scale feature fusion network for medical multi-modal image fusion." Biomedical Signal Processing and Control, 105, 107572.
  21. Li, B., Qiu, X.*, Tan, X., Yang, L.*, Tao, J., Fang, Z., & Huang, J*. (2025). "An end-to-end audio classification framework with diverse features for obstructive sleep apnea-hypopnea syndrome diagnosis." Applied Intelligence, 55(6), 427.
  22. Zhang, Y., Qiu, X.*, Ma, G., Yang, L., Tao, J., & Huang, J.* (2025). "SPSleepNet: Enhancing EEG-based sleep staging for OSA patients via sleep position integration." Complex & Intelligent Systems, 11(9), 401.
  23. Wang, H., Qiu, X.*, Li, B., Tan, X., & Huang, J*. (2025). "Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis." Complex & Intelligent Systems, 11(1), 44.
  24. Qiu, X., Cheng, L., Hao, T., & Tan, X. (2025). "ARMA: Mitigating catastrophic forgetting using attention-regularized model averaging in continual fine-tuning large language models." IEEE Transactions on Artificial Intelligence. DOI: 10.1109/TAI.2025.3630623.
  25. Qiu, X., Wang, H., Tan, X., & Jin, Y. (2025). "CVDLLM: Automated cardiovascular disease diagnosis with large-language-model-assisted graph attentive feature interaction." IEEE Transactions on Artificial Intelligence, 6(6), 1575-1590.

2024

  1. Tan, X., Cheng, L., Qiu, X.*, Shi, S., Cheng, Y., Chu, W., ... & Qi, Y. (2024, August). "Enhancing personalized headline generation via offline goal-conditioned reinforcement learning with large language models." In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5762-5772).
  2. Tan, X., Cheng, L., Qiu, X.*, Shi, S., Cheng, Y., Chu, W., ... & Qi, Y. (2024, July). "Enhancing task performance in continual instruction fine-tuning through format uniformity." In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2384-2389).
  3. Qiu, X., Wang, H., & Tan, X. (2024). "Inferring intents from equivariant-invariant representations and relational learning in multiagent systems." IEEE Systems Journal, 18(3), 1765-1775.
  4. Tan, X., Qu, C., Xiong, J., Zhang, J., Qiu, X.*, & Jin, Y.* (2024). "Model-based off-policy deep reinforcement learning with model-embedding." IEEE Transactions on Emerging Topics in Computational Intelligence, 8(4), 2974-2986.
  5. Qiu, X., Qian, J., Wang, H., Tan, X., & Jin, Y. (2024). "An attentive copula-based spatio-temporal graph model for multivariate time-series forecasting." Applied Soft Computing, 154, 111324.
  6. Zhang, B., Qiu, X.*, & Tan, X. (2024). "Balancing therapeutic effect and safety in ventilator parameter recommendation: An offline reinforcement learning approach." Engineering Applications of Artificial Intelligence, 131, 107784.
  7. Wang, H., Tan, X., Qiu, X.*, & Qu, C.* (2024). "Subequivariant reinforcement learning framework for coordinated motion control." 2024 IEEE International Conference on Robotics and Automation, 2112-2118.
  8. Shi, S., Tan, X., Qiu, X.*, Qu, C., Nie, K., Cheng, Y., ... & Qi, Y. (2024, November). "ULMR: Unlearning large language models via negative response and model parameter average." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track (pp. 755-762).
  9. Tan, X., Li, B., Qiu, X.*, Huang, J., Xu, Y., & Chu, W. (2024). "Robust deep Hawkes process under label noise of both event and occurrence." In ECAI 2024 (pp. 2870-2877). DOI: 10.3233/FAIA240824.
  10. Qiu, X., Wang, H., Tan, X., & Qu, C. (2024, October). "ILTS: Inducing intention propagation in decentralized multi-agent tasks with large language models." In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 3989-3993).
  11. Wang, H., Qiu, X.*, & Tan, X. (2024). "Multivariate graph neural networks on enhancing syntactic and semantic for aspect-based sentiment analysis." Applied Intelligence, 54(22), 11672-11689.
  12. Qiu, X., Hao, T., Shi, S., Tan, X., & Xiong, Y. J. (2024). "Chain-of-LoRA: Enhancing the instruction fine-tuning performance of low-rank adaptation on diverse instruction set." IEEE Signal Processing Letters, 31, 875-879.
  13. Qiu, X., Wang, C., Li, B., Tong, H., Tan, X., Yang, L., Tao, J., & Huang, J. (2024). "An audio-semantic multimodal model for automatic obstructive sleep apnea-hypopnea syndrome classification via multi-feature analysis of snoring sounds." Frontiers in Neuroscience, 18, 1336307.

2023

  1. Qiu, X., Shi, S., Tan, X., Qu, C., Fang, Z., Wang, H., Gao, Y., Wu, P., & Li, H. (2023). "Gram-based attentive neural ordinary differential equations network for video nystagmography classification." 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 21282-21291.
  2. Tan, X., Shi, S., Qiu, X.*, Qu, C., Qi, Z., Xu, Y., & Qi, Y. (2023). "Self-Criticism: Aligning large language models with their understanding of helpfulness, honesty, and harmlessness." Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.
  3. Qian, J., Qiu, X.*, Tan, X., Li, Q., Chen, J., & Jiang, X. (2023). "An attentive LSTM based approach for adverse drug reactions prediction." Applied Intelligence, 53(5), 4875-4889.

2022

  1. Chen, S., Qiu, X.*, Tan, X., Fang, Z., & Jin, Y.* (2022). "A model-based hybrid soft actor-critic deep reinforcement learning algorithm for optimal ventilator settings." Information Sciences, 611, 47-64.
  2. Ru, Y., Qiu, X.*, Tan, X., Chen, B., Gao, Y., & Jin, Y.* (2022). "Sparse-attentive meta temporal point process for clinical decision support." Neurocomputing, 485, 114-123.
  3. Qiu, X., Tan, X., Li, Q., Chen, S., Ru, Y., & Jin, Y. (2022). "A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support." Knowledge-Based Systems, 237, 107689.

Teaching

  • Artificial Intelligence Fundamentals (for Undergraduate)
  • Artificial Intelligence Fundamentals (All-English) (for international students)
  • Machine Learning (for Undergraduate)
  • Comprehensive Laboratory on Big Data Architecture (for Undergraduate)
  • Artificial Intelligence and Its Applications (for Master)