Division of
Natural and Applied Sciences

Start

2023-11-29
12:00 PM

End

2023-11-29
01:00 PM

Location

IB 2025

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Event details

Date & Time

Time: 12:00-1:00 pm (CST), Nov.29

Venue: IB 2025

Zoom ID: 715 337 7467

*Light refreshments will be provided

Speaker

Prof. Pengzhan Guo

Assistant Professor of Data Science

DESCRIPTION

Data mining and machine learning are intrinsically linked: data mining employs machine learning algorithms to delve into and interpret intricate data structures, while machine learning thrives on the high-quality data extracted and refined through data mining techniques. Both fields play a pivotal role in propelling the domain of artificial intelligence and spearheading innovative solutions across a spectrum of industries. My research seamlessly bridges the theoretical foundations and pragmatic applications within machine learning and data mining. On the methodology front, our efforts are channeled towards exploring stochastic optimization in deep learning. Our objective is to pioneer a scalable parallel computing, culminating in a parallel version of the foundational stochastic gradient descent method. When transitioning to applications, we are focus on path optimization. Our work covers a wide array, from developing tools to help individuals make strategic career decisions to optimizing traffic route to reduce urban congestion. I look forward to sharing a comprehensive overview of our research project and discussing potential avenues for future exploration with prospective students eager to contribute to this dynamic field.

BIO 

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 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.