基本信息：于腾腾，女，工学博士，副教授。2016年硕士毕业于西安电子科技大学应用数学专业，2021年博士毕业于河北工业大学控制理论与控制工程专业，2021年10月至2023年8月在中国科学院数学与系统科学研究院计算数学与科学工程计算研究所从事博士后研究。2023年8月起在基础教学部数学教研室从事数学基础课教学工作。

教学研究：主要为本科生讲授《高等数学》、《线性代数》等课程。主持博士后基金面上项目1项，校级项目1项，参与国家重点项目1项，国家自然科学基金青年项目1项。主要研究兴趣为大规模机器学习中的随机梯度算法及其应用等，相关成果发表在IEEE Transactions on Neural Networks and Learning Systems、Journal of Scientific Computing、Journal of the Operations Research Society of China等期刊。

代表性成果：

[1]Tengteng Yu, Xin-Wei Liu, Yu-Hong Dai, Jie Sun. A minibatch proximal stochastic recursive gradient algorithm using a trust-region-like scheme and Barzilai-Borwein stepsizes. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(10), 4627-4638. (SCI, JCR 1区,中科院1区TOP，IF: 14.255)[2] Tengteng Yu, Xin-Wei Liu, Yu-Hong Dai, Jie Sun. Stochastic variance reduced gradient methods using a trust-region-like scheme. Journal of Scientific Computing, 2021, 87, 5. (SCI, JCR 1区,中科院2区,中国数学会T2, IF: 2.843)[3] Tengteng Yu, Xin-Wei Liu, Yu-Hong Dai, Jie Sun. Variable metric proximal stochastic variance reduced gradient methods for nonconvex nonsmooth optimization. Journal of Industrial and Management Optimization, 2022, 18(4), 2611-2631. (SCI, JCR 2区,中科院4区, IF: 1.411)[4] Tengteng Yu, Xin-Wei Liu, Yu-Hong Dai, Jie Sun. A mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize. Journal of the Operations Research Society of China, 2023, 11, 277-307. (SCI, JCR 4区,中科院4区，IF: 0.21)

联系方式：yutengteng@bua.edu.cn

Basic information: Yu Tengteng, female, Doctor of Engineering, Associate Professor. In 2016, she got her master’s degree in applied mathematics, from Xi’anUniversityof Electronic Science and Technology. In 2021, she graduated from Hebei University of Technology with a doctor’s degree in control theory and control engineering. From October 2021 to August 2023, she was engaged in post doctoral research in the Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences. From August 2023, she has been working in the Mathematics Teaching and Research Office of the Basic Education Department to teach basic mathematics courses.

Teaching research: She is mainly teaching undergraduate students courses such as Advanced Mathematics and Linear Algebra. She has presided 1 postdoctoral fund general project, 1 university level project, participated in 1 national key project, and 1 National Natural Science Foundation youth project. Her main research interests include stochastic gradient algorithms and their applications in large-scale machine learning. Relevant achievements have been published in IEEE Transactions on Neural Networks and Learning Systems, Journal of Scientific Computing, Journal of the Operations Research Society of China, and other journals.