长期在某头部互联网大厂从事表征学习、迁移学习、多模态大模型、多Agent系统研发，以第一作者或通讯作者（含共同）在人工智能顶级期刊/会议发表论文10+篇（NeurIPS，ICML，ICDE，TKDE等），长期担任ICML，NeurIPS，ICLR等顶级会议审稿人，曾获“吴文俊”人工智能科技进步二等奖项。
研究方向：
1.多模态表征学习
2.大语言模型
3.迁移学习
4.运筹优化
现任岗位：华大研究院 北京分院 智能所 AI算法研发 主任科学家
教育背景：清华大学 MPI 高性能大规模数值计算方向 博士
参与项目：
1.蚂蚁增长智能平台
论文：
1、Liu X, Wang, B, Gao K, Li S. HVAE: Hyperbolic Variational Autoencoder For Flexible Knowledge Transfer Across Multiple Domains, International Conference on Machine Learning(ICML), 2026 (CCF-A，已接收). 通讯作者；
2、Liu X, Li S, Gao K, et al. A layer-wise natural gradient optimizer for training deep neural networks[J]. Advances in Neural Information Processing Systems(NeurIPS), 2024, 37: 28460-28489(CCF-A，已接收). 一作；
3、Xu W, Li S, Ha M, et al. Neural node matching for multi-target cross domain recommendation[C]//2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023: 2154-2166(CCF-A，已接收). 一作；
4、Guo X, Li S, Guo N, et al. Disentangled representations learning for multi-target cross-domain recommendation[J]. ACM Transactions on Information Systems, 2023, 41(4): 1-27(CCF-A，已接收). 一作；
5、Guo N, Liu X, Li S, et al. Hyperbolic contrastive graph representation learning for session-based recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023.(CCF-A，已接收). 一作；
6、Xiaobo Guo, Shaoshuai Li, Youru Li, Lu-an Dong, Zhenfeng Zhu. Information-bottlenecked variational autoencoder for top-N recommendation via gating mechanism[J]. ACM Transactions on Information Systems (TOIS), (CCF-A，已接收). 一作；
7、Cao J, Li S, Yu B, et al. Towards universal cross-domain recommendation[C]//Proceedings of the sixteenth ACM international conference on web search and data mining. 2023: 78-86.(CCF-B，已接收). 一作；
8、Wang, H., Zhang, X., Fang, S., Ran, L., Deng, Z., Zhang, Y., ... & Li, S. (2026). Open World Knowledge Aided Single-Cell Foundation Model with Robust Cross-Modal Cell-Language Pre-training. arXiv preprint arXiv:2601.05648. 通讯作者；
9、Pang, J., Qiu, P., He, Y., Li, B., Deng, Y., Wang, J., ... & Li, S., Xu, X. (2025). OmniCell: Unified Foundation Modeling of Single-Cell and Spatial Transcriptomics for Cellular and Molecular Insights. bioRxiv, 2025-12. 通讯作者；
