He Xin

Research Area

1. He X, Zhao K, Chu X. AutoML: A Survey of the State-of-the-art Knowledge-based Systems. 2021 Jan 5;212:106622.

2. He X, Wang S, Chu X, Shi S, Tang J, Liu X, Yan C, Zhang J, Ding G. Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans. Proceedings of the AAAI conference on Artificial Intelligence 2021 May 18 (Vol. 35, No. 6, pp. 4821-4829).

3. He X, Ying G, Zhang J, Chu X. Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification. International Conference on Medical Image Computing and Computer-Assisted Intervention 2022 Sep 15 (pp. 560-570). Cham: Springer Nature Switzerland.

4. Ying G*, He X*, Gao B, Han B, Chu X. EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs. European Conference on Computer Vision 2022 Oct 21 (pp. 37-53). Cham: Springer Nature Switzerland. (* Co-first)

5. He X, Wang S, Shi S, Tang Z, Wang Y, Zhao Z, Dai J, Ni R, Zhang X, Liu X, Wu Z. Computer-aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. 2019 IEEE International Conference on Big Data (Big Data) 2019 Dec 9 (pp. 4839-4844). IEEE.

6. He X, Yao J, Wang Y, Tang Z, Cheung KC, See S, Han B, Chu X. Nas-lid: Efficient Neural Architecture Search with Local Intrinsic Dimension. Proceedings of the AAAI Conference on Artificial Intelligence 2023 Jun 26 (Vol. 37, No. 6, pp. 7839-7847).

7. He X, Chu X. MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical Image Classification. 2023 IEEE International Conference on Medical Artificial Intelligence (MedAI) 2023 Nov 18 (pp. 344-354). IEEE.

8. He X, Zhang S, Wang Y, Yin H, Zeng Z, Shi S, Tang Z, Chu X, Tsang I, Soon OY. ExpertFlow: Optimised Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference. arXiv preprint arXiv:2410.17954. 2024 Oct 23.

9. Tang Z, Zhang Y, Shi S, He X, Han B, Chu X. Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. International Conference on Machine Learning 2022 Jun 28 (pp. 21111-21132). PMLR.

10. Wang Y, Wang Q, Shi S, He X, Tang Z, Zhao K, Chu X. Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) 2020 May 11 (pp. 744-751). IEEE.

11. Tang Z, Wang Y, He X, Zhang L, Pan X, Wang Q, Zeng R, Zhao K, Shi S, He B, Chu X. Fusionai: Decentralised Training and Deploying LLMs with Massive Consumer-level GPUs. arXiv preprint arXiv:2309.01172. 2023 Sep 3.

12. Wang Y, Chen Y, Li Z, Kang X, Tang Z, He X, Guo R, Wang X, Wang Q, Zhou AC, Chu X. BurstGPT: A Real-world Workload Dataset to Optimise LLM Serving Systems.

13. Wang Y, Shi S, He X, Tang Z, Pan X, Zheng Y, Wu X, Zhou AC, He B, Chu X. Reliable and Efficient In-memory Fault Tolerance of Large Language Model Pretraining. arXiv preprint arXiv:2310.12670. 2023 Oct 19.

Conference Reviewer for NeurIPS 2023, AAAI 2020/2022/2024, ECCV 2022/2024, CVPR 2023 and ICCV 2023
Journal Reviewer for TPAMI, TMI, JBHI, Expert Systems with Applications and IEEE Transaction on Cybernetics

Publications