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Shengyu Chen

Ph.D. Candidate at University of Pittsburgh

About Me

Hi, my name is Shengyu Chen (Kevin). I am a Computer Science Ph.D. candidate at the University of Pittsburgh, advised by Dr. Xiaowei Jia. My research directions focus on developing machine learning and data mining methods to solve real-world problems with great societal and scientific impacts. My research portfolio spans various domains, including the Hydrological domain of streamflow and baseflow time-series prediction, the Physics domain of turbulence simulation modeling, the Geoscience domain of microcrack propagation, and the Medical domain of brain imaging. Several of my works have been accepted by prominent conferences such as KDD, CIKM, SDM, and more. To gain a more comprehensive understanding of my background, you can visit my Curriculum Vitae

Recent Publications

For the full publication list please refer to Google Scholar or Curriculum Vitae

Shengyu Chen, Nasrin Kalanat, Simon Topp, Jeffery Sadler, Yiqun Xie, Zhe Jiang, Xiaowei Jia, "Meta-Transfer-Learning for Time Series Data with Extreme Events: An Application to Water Temperature Prediction.", Conference on Information and Knowledge Management (CIKM), 2023. (Accept)

Shengyu Chen, Tianshu Bao, Peyman Givi, Can Zheng, Xiaowei Jia, "Reconstruction of Turbulent Flows Using Physics-Guided Spatio-Temporal Dynamics.", ACM Transactions on Intelligent Systems and Technology (TIST), 2023. (Accept)

(Best Paper Award) Shengyu Chen, Yiqun Xie, Xiang Li, Xu Liang, Xiaowei Jia, "Physics-Guided Meta-Learning Method in Baseflow Prediction over Large Regions.", In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 2023.

Shengyu Chen, Nasrin Kalanat, Yiqun Xie, Sheng Li, Jacob Zwart, Jeffrey Sadler, Alison Appling, Samantha Oliver, Jordan Read, Xiaowei Jia, "Physics-Guided Machine Learning from Simulated Data with Different Physical Parameters.", Knowledge and Information Systems (KIS),~2023.

Shengyu Chen, Jacob A. Zwart, and Xiaowei Jia, "Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks.", In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.

Bao, Tianshu*, Shengyu Chen*, Taylor T. Johnson, Peyman Givi, Shervin Sammak, and Xiaowei Jia, "Physics Guided Neural Networks for Spatio-temporal Super-resolution of Turbulent Flows.", In The 38th Conference on Uncertainty in Artificial Intelligence (UAI). 2022.

Shengyu Chen, Shervin Sammak, Peyman Givi, Joseph P. Yurko, and Xiaowei Jia, "Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks.", In 2021 IEEE International Conference on Big Data (Big Data), 2021.

Shengyu Chen, Alison Appling, Samantha Oliver, Hayley Corson-Dosch, Jordan Read, Jeffrey Sadler, Jacob Zwart, and Xiaowei Jia, "Heterogeneous stream-reservoir graph networks with data assimilation.", In 2021 IEEE International Conference on Data Mining (ICDM), 2021.

Research and Teaching Experience

Ph.D. Student Researcher - Los Alamos National Laboratory (2022 - Present)

● Applied machine learning methods in medical imaging (barin imaging) and microcrack coalescence problems.

Graduate Student Researcher - University of Pittsburgh (2020 - Present)

● Directions: Spatio-temporal Data Mining, Knowledge-guided Machine Learning, Computer Vision.

● Projects: Modeling turbulence simulation, Streamflow & Baseflow time-series forecasting, Medical imaging (barin imaging) recovery, and Microcrack propagation.

Teaching Assistant - University of Pittsburgh (Spring 2022)

● Discrete Structures (CS 441)

● Capstone (CS 1980)