教育经历
2012年获得中国科学院博士学位
工作经历
2012-2013年，北京三星通讯研究院-算法工程师
2013-2022年，阿里巴巴达摩院-技术总监
2023年至今，复旦大学人工智能创新与产业研究院-研究员
研究方向
（1）AI+气象：气象大模型
（2）多模态大模型
（3）图像视频生成
（4）深度学习模型压缩与加速
主要成果
已发表人工智能顶会60余篇；申请发明专利100余项，授权30余项。
代表性成果简介：伏羲天气预报大模型、业内首个泛自然资源AI 引擎-AI Earth 创始人、最大的商品图像以图搜图产品-拍立淘发起人之一、探索AI 与行业结合，实现人工智能在电商、遥感、气象、安防、IOT 等场景的大规模落地。
招生专业
依托计算机科学技术学院招生，招生专业为计算机科学与技术。
发表论文
1.Lei Chen,Xiaohui Zhong,Feng Zhang,Yuan Cheng,Yinghui Xu,Yuan Qi,Hao Li.(2023). FuXi: A cascade machine learning forecasting system for 15-day global weather forecast. arXiv preprintarXiv:2306.12873.
2.Yi Xu,LeiShang,Jinxing Ye,Qi Qian,Yu-Feng Li,Baigui Sun,HaoLi,Rong Jin.Dash: Semi-supervised learning with dynamic thresholding[C]//International Conference on Machine Learning. PMLR, 2021: 11525-11536.
3.Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, Hao Li(2022). Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2781-2790).
4.Shuting He, Hao Luo, Weihua Chen, Miao Zhang, Yuqi Zhang, Fan Wang, Hao Li, Wei JiangHe.Multi-domain learning and identity mining for vehicle re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020: 582-583.
5.Qi Qian, Lei Chen, Hao Li, Rong JinQian. Dr loss: Improving object detection by distributional ranking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 12164-12172.
6.Hao Li, Jing Lu, Guohua Shi, and Yudong Zhang, "Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm," Biomed. Opt. Express 1, 31-40 (2010).
7.Li Hao, Lu J, Shi G, et al. Measurement of oxygen saturation in small retinal vessels with adaptive optics confocal scanning laser ophthalmoscope[J]. Journal of biomedical optics, 2011, 16(11): 110504-110504-3.
8.He, S., Luo, H., Wang, P., Wang, F., Li, H., & Jiang, W. (2021). Transreid: Transformer-based object re-identification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 15013-15022).
9.Qian Q, Shang L, Sun B, et al. Softtriple loss: Deep metric learning without triplet sampling[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 6450-64
10.Leng C, Dou Z, Li H, et al. Extremely low bit neural network: Squeeze the last bit out with admm[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).
11.Zhou Q, Yu C, Wang Z, et al. Instant-teaching: An end-to-end semi-supervised object detection framework[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 4081-4090.
12.Li, Y., Shi, T., Zhang, Y., Chen, W., Wang, Z., & Li, H. (2021). Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation.ISPRS Journal of Photogrammetry and Remote Sensing, 175, 20-33.
13.Lei Chen, Xiaohui Zhong, Jie Wu, Deliang Chen, Shangping Xie, Qingchen Chao, Chensen Lin, Zixin Hu, Bo Lu, Hao Li, Yuan Qi. FuXi-S2S: An accurate machine learning model for global subseasonal forecasts.arXiv preprint arXiv:2312.09926, 2023.
14.Xiaohui Zhong, Lei Chen, Jun Liu, Chensen Lin, Yuan Qi, Hao Li. FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model[J]. arXiv preprint arXiv:2310.19822, 2023.
15.Yuan Hu, Lei Chen, Zhi Bin Wang, Hao Li. SwinVRNN: A Data‐Driven Ensemble Forecasting Model via Learned Distribution Perturbation[J]. Journal of Advances in Modeling Earth Systems, 2023, 15(2): e2022MS003211.
