Pengzhan Guo’s research project covers methodology and applications in machine learning and data mining. He is especially interested in parallel computing, human resource management and mobile computing. His teaching interests at Duke Kunshan include linear algebra and machine learning.

He has published papers in refereed journals and conference proceedings such as IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Intelligent Systems and Technology (TIST), and the IEEE International Conference on Data Mining (ICDM). He has obtained many awards including the Stony Brook AMS Graduate Award (Excellence in Research), TMC-21 Best Paper Award and ICDM-2019 Student Travel Award. He received his master’s and Ph.D. degrees in applied mathematics and statistics from Stony Brook University.

News

• New! (2023-9-3) One paper were accepted to ICDM 2023 about Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model.
• (2022-5-18) One paper were accepted to Scientific Report about Intelligent career planning via stochastic subsampling reinforcement learning.
• (2021-12-16) One paper on Route optimization via environment-aware deep network and reinforcement learning was accepted to TIST.
• (2020-12-19) One paper on Weighted aggregating stochastic gradient descent for parallel deep learning was accepted to TKDE.
• (2019-11-8) One paper on A weighted aggregating sgd for scalable parallelization in deep learning was accepted to ICDM 2019.

Selected Publications

• Pengzhan Guo, Keli Xiao, Zeyang Ye, Hengshu Zhu and Wei Zhu. Intelligent Career Planning via Stochastic Subsampling Reinforcement Learning.. Scientific Report, 2022.
• Pengzhan Guo, Keli Xiao, Zeyang Ye and Wei Zhu. Route Optimization via Environment-Aware Deep Network and Reinforcement Learning.. ACM Transactions on Intelligent Systems and Technology, 2021.
• Pengzhan Guo, Keli Xiao, Zeyang Ye and Wei Zhu. Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning.. IEEE Transactions on Knowledge and Data Engineering, 2020.