个人简介

秦雯，女，博士，现为四川师范大学计算机科学学院讲师，硕士生导师，于2024年获得重庆邮电大学计算机科学与技术专业博士学位；学术研究主要集中在大数据智能计算领域，专注于图表示学习、大规模数据应用的并行算法设计等方向，在IEEE T.FS、IEEE T.BD、IEEE T.ASE等国际著名期刊和会议上发表SCI/EI检索论文8篇；申请国家发明专利3项，已授权1项；同时，还担任IEEE T.ASE、Neurocomputing、IEEE/CAA JAS等国际著名学术会议和期刊等审稿人。

代表性论文

[1]      QIN W, LUO X. Asynchronous parallel fuzzy stochastic gradient descent for high-dimensional incomplete data representation. IEEE Transactions on Fuzzy Systems, 2024, 32(2): 445-459. (中科院一区，CCF-B)

[2]      QIN W, LUO X, LI S, et al. Parallel adaptive stochastic gradient descent algorithms for latent factor analysis of high-dimensional and incomplete industrial data. IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2023.3267609. (中科院二区，CCF-B)

[3]      QIN W, LUO X, ZHOU M C. Adaptively-accelerated parallel stochastic gradient descent for high-dimensional and incomplete data representation learning. IEEE Transactions on Big Data, 2024, 10(1): 92-107. (中科院二区)

[4]      LUO X, QIN W, DONG A, et al. Efficient and high-quality recommendations via momentum-incorporated parallel stochastic gradient descent-based learning. IEEE CAA Journal Automatica Sinica, 2021, 8(2): 402- 411. (中科院一区)

[5]      QIN W, LUO X. An asynchronously alternative stochastic gradient descent algorithm for efficiently parallel latent feature analysis on shared-memory. 2022 IEEE International Conference on Knowledge Graph, 2022: 217-224.

[6]      QIN W, LUO X, ZHOU M C. Adaptive Alternating Stochastic Gradient Descent Algorithms for Large-Scale Latent Factor Analysis. 2021 IEEE International Conference on Services Computing, 2021: 285-290. (CCF-C).

备注

欢迎对大数据分析、图表示学习、并行计算等感兴趣的同学联系我。联系邮箱：qinwen@sicnu.edu.cn。期待同学们的加入，共同进步！！！

编辑：计算机科学学院