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个人简介：Personal Profile 贾西西，西安电子科技大学副教授，华山学者菁英副教授，硕士生导师。本科、硕士和博士均毕业于西安电子科技大学，澳门科技大学博士后。2016-2017, 2018-2019分别以助理研究员以及副研究员的身份前往香港理工大学计算机系进行访问。2023-2024年以研究员身份前往香港理工大学应用数学系进行访问。中国运筹学会终身会员。主要研究方向：图像处理的深度学习方法，最优化理论与应用以及大规模矩阵快速计算。科研项目：主持国家自然科学基金面上项目1项(在研经费50万)， 国家自然科学基金青年项目1项(已结题，经费24万)，国家博士后基金项目1项（经费8万）。学术成果：在IEEE Transactions on Image Processing, IEEE Transactions on Neural Network and Learning Systems, Information Sciences等期刊以及CVPR, NeurIPS, ICCV, ICLR, ICIP等会议发表学术论文十余篇。奖励荣誉：获得获得“澳门青年学者计划”，陕西省数学会优秀论文一等奖。google scholar: https://scholar.google.com.hk/citations?hl=zh-CN&user=K4hTFFwAAAAJ代表性学术论文：1. 会议论文[1] Jia, X., Wang, H., Peng, J., Feng, X., & Meng, D. (2024). Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent. Advances in Neural Information Processing Systems, 36. (NeurIPS, CCF A)[2] Jia, X., Liu, S., Feng, X., & Zhang, L. (2019). Focnet: A fractional optimal control network for image denoising. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6054-6063). (CVPR, CCF A)[3] Wang, Q., Wang, R., Wu, Y., Jia, X., & Meng, D. (2023). Cba: Improving online continual learning via continual bias adaptor. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 19082-19092). (ICCV, CCF A)[4] Wang, R., Jia, X., Wang, Q., Wu, Y., & Meng, D. (2022, September). Imbalanced Semi-supervised Learning with Bias Adaptive Classifier. In The Eleventh International Conference on Learning Representations. (ICLR, 人工智能顶级会议)[5] Jia, X., Feng, X., & Wang, W. (2016, September). Adaptive regularizer learning for low rank approximation with application to image denoising. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3096-3100). IEEE. (ICIP, CCF C)2. 期刊论文[1] Jia, X., Feng, X., Yong, H., & Meng, D. (2022). Weight decay with tailored Adam on scale-invariant weights for better generalization. IEEE Transactions on Neural Networks and Learning Systems.[2] Jia, X., Feng, X., Wang, W., & Zhang, L. (2020). Generalized unitarily invariant gauge regularization for fast low-rank matrix recovery. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1627-1641.[3] Kong, S., Wang, W., Feng, X., & Jia, X. (2021). Deep red unfolding network for image restoration. IEEE Transactions on Image Processing, 31, 852-867.[4] Zhu, F., Liang, Z., Jia, X., Zhang, L., & Yu, Y. (2019). A benchmark for edge-preserving image smoothing. IEEE Transactions on Image Processing, 28(7), 3556-3570.[5] Jia, X., Feng, X., & Liu, S. (2021). Dual non-autonomous deep convolutional neural network for image denoising. Information Sciences, 572, 263-276.[6] Jia, X., Feng, X., Wang, W., Huang, H., & Xu, C. (2019). Online Schatten quasi-norm minimization for robust principal component analysis. Information Sciences, 476, 83-94.[7] Jia, X., Meng, D., Zhang, X., & Feng, X. (2022). PDNet: Progressive denoising network via stochastic supervision on reaction-diffusion–advection equation. Information Sciences, 610, 345-358.