助理教授 |
我目前是昆山杜克大学的助理教授。 本人已于2021年12月从纽约州立大学石溪分校, 应用数学与统计学院获得博士学位。 在此之前, 我于2018年12月在 纽约州立大学石溪分校, 应用数学与统计学院, 计算数学专业获得了硕士学位。
我的研究兴趣主要包括: 并行计算,随机优化,数据挖掘,机器学习及他们的应用。
博士 纽约州立大学石溪分校 (2018.12 ~ 2021.12)
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硕士 纽约州立大学石溪分校 (2017.8 ~ 2018.12)
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测试集上提升了原模型5%的效果;
将word2vector应用到广告推荐中;
定义了合理有效的线下衡量方法。
MATH 105: Calculus
MATH 202: Linear Algebra
STATS 303: Statistical Machine Learning
AMS 560: Big Data Systems, Algorithms and Networks;
AMS 527: Numerical Analysis II, AMS 528: Numerical Analysis III;
AMS 510 (Taught Recitation Sessions; Mean Score: A-): Analytical Methods for Applied Mathematics and Statistics.
From efficiency to equity: A multi-user paradigm in mobile route optimization.
Pengzhan Guo*, Keli Xiao.
Electronic Commerce Research and Applications, 2024. [Impact Factor: 5.94]
Preference-constrained career path optimization: An exploration space-aware stochastic model.
Pengzhan Guo, Keli Xiao, Hengshu Zhu and Qingxin Meng.
ICDM, 2023. [CCF B]
Intelligent Career Planning via Stochastic Subsampling Reinforcement Learning.
Pengzhan Guo, Keli Xiao, Zeyang Ye, Hengshu Zhu and Wei Zhu.
Scientific Report, 2022. [Impact Factor: 4.379]
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning
Pengzhan Guo, Keli Xiao, Zeyang Ye and Wei Zhu.
ACM Transactions on Intelligent Systems and Technology (TIST), 2021. [Impact Factor: 4.654]
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning.
Pengzhan Guo, Zeyang Ye, Keli Xiao and Wei Zhu.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. [Impact Factor: 6.977]
A Weighted Aggregating SGD for Scalable Parallelization in Deep Learning.
Pengzhan Guo, Zeyang Ye and Keli Xiao.
Proceedings of the 19th IEEE International Conference on Data Mining (ICDM-19). [CCF B]
集中式并行随机梯度下降的理论和算法研究(2025.1-2027.12)
负责人; 经费:30万元
国家自然基金青年项目
多智能体推荐系统研究(2023.1-至今)
负责人;
昆山超算中心合作项目