Assistant Professor Data Science, Division of Natural and Applied Sciences Duke Kunshan University Kunshan, Jiangsu 215000, China Email: pg180@duke.edu [CV] [Google Scholar] [中文主页]

About Me

I am an Assistant Professor of Data Science at Duke Kunshan University. My research focuses on machine learning, data mining, reinforcement learning, stochastic optimization, and parallel and distributed computing, with applications in intelligent decision-making, recommendation systems, transportation, and career planning.

I received my Ph.D. in Statistics from the Department of Applied Mathematics and Statistics at Stony Brook University in 2021. Prior to that, I received my M.S. in Computational Applied Mathematics from Stony Brook University in 2018.

Research Interests

• Machine learning and data mining
• Reinforcement learning and multi-agent reinforcement learning
• Stochastic optimization and large-scale optimization
• Parallel and distributed machine learning
• Intelligent recommendation and sequential decision-making
• Applications in transportation, career planning, and urban computing

Education

Ph.D., Stony Brook University (Dec. 2018 – Dec. 2021) Program: Statistics, Department of Applied Mathematics and Statistics Advisors: Prof. Wei Zhu and Prof. Keli Xiao Honor: AMS Graduate Award – Excellence in Research

M.S., Stony Brook University (Aug. 2017 – Dec. 2018) Program: Computational Applied Mathematics, Department of Applied Mathematics and Statistics

Academic and Professional Experience

Duke Kunshan University

• Assistant Professor of Data Science (Jul. 2022 – Present)

Samsung Research America

• Research Intern (May 2021 – Aug. 2021)

Honors and Awards

• Best Oral Paper Award, International Conference on Brain Inspired Cognitive Systems (BICS), 2025

• Best Paper Award, Workshop on Information Technologies and Systems (WITS), 2024

• AMS Graduate Award – Excellence in Research, Stony Brook University, 2022

• Best Paper Award, ACM Talent and Management Computing Workshop (TMC), 2021

• ICDM Student Travel Award, IEEE International Conference on Data Mining (ICDM), 2019

• IACS Travel Scholarship, Institute for Advanced Computational Science, Stony Brook University, 2019

• Chinese National Scholarship, 2016

Publications

• Shapley in Context: Explaining Financial Language with Domain Expertise.
  Dangxing Chen and Pengzhan Guo.
  2026.

• From Efficiency to Equity: A Multi-User Paradigm in Mobile Route Optimization.
  Pengzhan Guo* and Keli Xiao.
  Electronic Commerce Research and Applications, 2024.

• Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model.
  Pengzhan Guo, Keli Xiao, Hengshu Zhu and Qingxin Meng.
  Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM), 2023. [CCF B]

• Intelligent Career Planning via Stochastic Subsampling Reinforcement Learning.
  Pengzhan Guo, Keli Xiao, Zeyang Ye, Hengshu Zhu and Wei Zhu.
  Scientific Reports, 2022.

• 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.

• 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.

• 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), 2019. [CCF B]

Working Papers

• Societal Advancement through Equitable Decision Paths: A Performance-Adaptive Multi-Agent Reinforcement Learning Approach.
  Pengzhan Guo, Keli Xiao and Jingyuan Yang.
  Major Revision at Information Systems Research (ISR).

• Hybrid Decision MARL with Dynamical Allocation and Adaptive Search for Preference-Based Multi-User Mobile Sequential Recommendation.
  Yuhan Wei, Dalve Xue, Hanchi Zhao and Pengzhan Guo.
  Under Revision at ACM Transactions on Knowledge Discovery from Data (TKDD).

Research Grants

• Parallel Stochastic Gradient Descent: Theory and Algorithms
  Principal Investigator, National Natural Science Foundation of China (No. 62406130), 2025 – 2027

• Multi-Agent Recommendation Systems
  Principal Investigator, Kunshan Supercomputing Center, 2023 – Present

Teaching Experience

Duke Kunshan University

• MATH 105: Calculus

• MATH 202: Linear Algebra

• STATS 303: Statistical Machine Learning

Stony Brook University

• AMS 560: Big Data Systems, Algorithms and Networks

• AMS 527: Numerical Analysis II

• AMS 528: Numerical Analysis III

• AMS 510: Analytical Methods for Applied Mathematics and Statistics (Recitation Instructor; average student rating: A-)

Professional Activities

Journal Reviewer

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

• IEEE Transactions on Knowledge and Data Engineering (TKDE)

• IEEE Transactions on Parallel and Distributed Systems (TPDS)

• IEEE Transactions on Systems, Man, and Cybernetics: Systems

• IEEE Transactions on Industrial Informatics (TII)

• IEEE Transactions on Big Data

• ACM Transactions on Intelligent Systems and Technology (TIST)

• Electronic Commerce Research and Applications (ECRA)

• IEEE Access

• PeerJ Computer Science

Conference Service

• Program Committee: ICDM (2026), WAIC (2026)

• Reviewer: SIGIR (2022–2024), KDD (2020–2021), ICDM (2020–2021), CIKM (2020–2022), WSDM (2021)