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Results: 232
Number of items: 232
  • Open Access
    Islam, N. T., Wang, R., & Zhao, Z. (2025). Using Explainable Techniques to Enhance Chain of Thoughts in LLMs. In 2025 IEEE International Conference on eScience : eScience 2025: 15-18 September 2025, Chicago, United States : proceedings (pp. 323-324). IEEE Computer Society. https://doi.org/10.1109/eScience65000.2025.00052
  • Open Access
    Wang, Y. (2025). From computational notebooks to collaborative workflows: A decentralized virtual research environment solution. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Xin, R., Wang, J., Chen, P., & Zhao, Z. (2025). Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review. ACM Computing Surveys, 57(5), Article 115. https://doi.org/10.1145/3701740
  • Open Access
    Xin, R., Chen, P., Grosso, P., & Zhao, Z. (2024). A fine-grained robust performance diagnosis framework for run-time cloud applications. Future Generation Computer Systems, 155, 300-311. https://doi.org/10.1016/j.future.2024.02.014
  • Open Access
    Cheng, L., Wang, Y., Cheng, F., Liu, C., Zhao, Z., & Wang, Y. (2024). A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling. IEEE Transactions on Sustainable Computing, 9(3), 422-432. https://doi.org/10.1109/TSUSC.2023.3303898
  • Open Access
    Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., & Zhao, Z. (2024). Autonomous selection of the fault classification models for diagnosing microservice applications. Future Generation Computer Systems, 153, 326-339. https://doi.org/10.1016/j.future.2023.12.005
  • Open Access
    Wang, Y., Kanwal, N., Engan, K., Rong, C., Grosso, P., & Zhao, Z. (2024). PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds. In J. Carretero, S. Shende, J. Garcia-Blas, I. Brandic, K. Olcoz, & M. Schreiber (Eds.), Euro-Par 2024: Parallel Processing: 30th European Conference on Parallel and Distributed Processing, Madrid, Spain, August 26–30, 2024 : proceedings (Vol. I, pp. 210-224). (Lecture Notes in Computer Science; Vol. 14801), (Advanced Research in Computing and Software Science). Springer. https://doi.org/10.1007/978-3-031-69577-3_15
  • Open Access
    Petzold, A., Bundke, U., Hienola, A., Laj, P., Lund Myhre, C., Vermeulen, A., Adamaki, A., Kutsch, W., Thouret, V., Boulanger, D., Fiebig, M., Stocker, M., Zhao, Z., & Asmi, A. (2024). Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science. Atmospheric Chemistry and Physics, 24(9), 5369-5388. https://doi.org/10.5194/acp-24-5369-2024
  • Open Access
    Jiang, W., Chen, K., Liang, Z., Luo, T., Yue, G., Zhao, Z., Song, W., Zhao, L., & Wen, J. (2024). HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM. IEEE Journal of Biomedical and Health Informatics, 28(2), 941-951. https://doi.org/10.1109/JBHI.2023.3331944
  • Open Access
    Krishnasamy, A., Wang, Y., & Zhao, Z. (2024). A Collaborative Framework for Facilitating Federated Learning among Jupyter Users. In EScience '24 proceedings: 2024 IEEE 20th International Conference on e-Science (e-Science) : September 16-20, 2024, Osaka, Japan (pp. 305-306). IEEE. https://doi.org/10.1109/e-science62913.2024.10678679
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