Publications

2025

1. PAKDD

   Improving Generalization in Deep Neural Networks by Mitigating Memorization

   Yong Zhuang, Tianyu Kang, Zhen Lu, and 2 more authors

   In The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2025

   Bib

   @inproceedings{zhuang2025ProNeR,
     title = {Improving Generalization in Deep Neural Networks by Mitigating Memorization},
     author = {Zhuang, Yong and Kang, Tianyu and Lu, Zhen and Ding, Wei and Chen, Ping},
     booktitle = {The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
     year = {2025}
   }

2024

1. TKDD

   Horizon Forcing: Improving the Recurrent Forecasting of Chaotic Systems

   Yong Zhuang, Matthew Almeida, Wei Ding, and 3 more authors

   ACM Transactions on Intelligent Systems and Technology (TIST), 2024
2. BigData

   Deep Learning Models for Diabetic Retinopathy Detection: A Comparative Study

   Munmi Thakuria, and Yong Zhuang

   In 2024 IEEE International Conference on Big Data (BigData), 2024

   Bib

   @inproceedings{thakuria2024deep,
     title = {Deep Learning Models for Diabetic Retinopathy Detection: A Comparative Study},
     author = {Thakuria, Munmi and Zhuang, Yong},
     booktitle = {2024 IEEE International Conference on Big Data (BigData)},
     pages = {5078--5081},
     year = {2024},
     organization = {IEEE}
   }

2023

1. BigData

   CASTLE: A Cascaded Spatio-Temporal Approach for Long-lead Streamflow Forecasting

   Yong Zhuang, David L Small, Patrick D Flynn, and 4 more authors

   In 2023 IEEE International Conference on Big Data (BigData), 2023

   Bib PDF

   @inproceedings{zhuang2023castle,
     title = {CASTLE: A Cascaded Spatio-Temporal Approach for Long-lead Streamflow Forecasting},
     author = {Zhuang, Yong and Small, David L and Flynn, Patrick D and Palash, Wahid and Islam, Shafiqul and Chen, Ping and Ding, Wei},
     booktitle = {2023 IEEE International Conference on Big Data (BigData)},
     pages = {1031--1038},
     year = {2023},
     organization = {IEEE},
   }

2022

1. ICDM

   Widening the Time Horizon: Predicting the Long-Term Behavior of Chaotic Systems

   Yong Zhuang, Matthew Almeida, Wei Ding, and 3 more authors

   In 2022 IEEE International Conference on Data Mining (ICDM), 2022

   Bib PDF Code Slides

   @inproceedings{zhuang2022widening,
     title = {Widening the Time Horizon: Predicting the Long-Term Behavior of Chaotic Systems},
     author = {Zhuang, Yong and Almeida, Matthew and Ding, Wei and Flynn, Patrick D and Islam, Shafiqul and Chen, Ping},
     booktitle = {2022 IEEE International Conference on Data Mining (ICDM)},
     pages = {833--842},
     year = {2022},
     organization = {IEEE},
   }

2021

1. TKDD

   Mitigating class-boundary label uncertainty to reduce both model bias and variance

   Matthew Almeida, Yong Zhuang, Wei Ding, and 2 more authors

   ACM Transactions on Knowledge Discovery from Data (TKDD), 2021

   Bib PDF

   @article{zhuang2021mitigating,
     title = {Mitigating class-boundary label uncertainty to reduce both model bias and variance},
     author = {Almeida, Matthew and Zhuang, Yong and Ding, Wei and Crouter, Scott E and Chen, Ping},
     journal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},
     volume = {15},
     number = {2},
     pages = {1--18},
     year = {2021},
     publisher = {ACM New York, NY, USA},
   }

2018

1. ICBK

   Galaxy: Towards Scalable and Interpretable Explanation on High-dimensional and Spatio-Temporal Correlated Climate Data

   Yong Zhuang, David L Small, Xin Shu, and 3 more authors

   In 2018 IEEE International Conference on Big Knowledge (ICBK), 2018

   Bib PDF Slides

   @inproceedings{zhuang2018galaxy,
     title = {Galaxy: Towards Scalable and Interpretable Explanation on High-dimensional and Spatio-Temporal Correlated Climate Data},
     author = {Zhuang, Yong and Small, David L and Shu, Xin and Yu, Kui and Islam, Shafiqul and Ding, Wei},
     booktitle = {2018 IEEE International Conference on Big Knowledge (ICBK)},
     pages = {146--153},
     year = {2018},
     organization = {IEEE},
   }

2017

1. ICBK

   Crime hot spot forecasting: A recurrent model with spatial and temporal information

   Yong Zhuang, Matthew Almeida, Melissa Morabito, and 1 more author

   In 2017 IEEE International Conference on Big Knowledge (ICBK), 2017

   Abs Bib PDF Poster Slides

   Crime is a major social problem in the United States, threatening public safety and disrupting the economy. Understanding patterns in criminal activity allows for the prediction of future high-risk crime "hot spots" and enables police precincts to more effectively allocate officers to prevent or respond to incidents. With the ever-increasing ability of states and organizations to collect and store detailed data tracking crime occurrence, a significant amount of data with spatial and temporal information has been collected. How to use the benefit of massive spatial-temporal information to precisely predict the regional crime rates becomes necessary. The recurrent neural network model has been widely proven effective for detecting the temporal patterns in a time series. In this study, we propose the Spatio-Temporal neural network (STNN) to precisely forecast crime hot spots with embedding spatial information. We evaluate the model using call-for-service data provided by the Portland, Oregon Police Bureau (PPB) for a 5-year period from March 2012 through the end of December 2016. We show that our STNN model outperforms a number of classical machine learning approaches and some alternative neural network architectures.

   @inproceedings{zhuang2017crime,
     title = {Crime hot spot forecasting: A recurrent model with spatial and temporal information},
     author = {Zhuang, Yong and Almeida, Matthew and Morabito, Melissa and Ding, Wei},
     booktitle = {2017 IEEE International Conference on Big Knowledge (ICBK)},
     pages = {143--150},
     year = {2017},
     organization = {IEEE},
   }

2016

1. ICNSC

   An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting

   Yong Zhuang, Kui Yu, Dawei Wang, and 1 more author

   In 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC), 2016

   Bib PDF Slides

   @inproceedings{zhuang2016evaluation,
     title = {An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting},
     author = {Zhuang, Yong and Yu, Kui and Wang, Dawei and Ding, Wei},
     booktitle = {2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC)},
     pages = {1--6},
     year = {2016},
     organization = {IEEE},
   }

2. CI

   Long-lead prediction of extreme precipitation cluster via a spatiotemporal convolutional neural network

   WY Zhuang, and Wei Ding

   In Proceedings of the 6th International Workshop on Climate Informatics: CI, 2016

   Bib PDF

   @inproceedings{zhuang2016long,
     title = {Long-lead prediction of extreme precipitation cluster via a spatiotemporal convolutional neural network},
     author = {Zhuang, WY and Ding, Wei},
     booktitle = {Proceedings of the 6th International Workshop on Climate Informatics: CI},
     year = {2016},
   }