His research lies at the intersection of machine learning, parallel computing, and data mining. He is particularly interested in integrating machine learning and data mining techniques to address economic and decision-making problems. At Duke Kunshan University, his teaching interests include linear algebra and statistical machine learning.

He has published several papers in leading data mining and machine learning conferences and journals, including ICDM, TKDE, and TIST. He has also served as a reviewer for top venues such as TNNLS, TKDE, SIGIR, KDD, ICDM, CIKM, and WSDM. In addition, he received the Best Paper Award at WITS 2024.

Pengzhan holds an M.A. in Computational Applied Mathematics and a Ph.D. in Statistics, both from Stony Brook University.

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 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.