publications

2021

1. TKDD

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

   Almeida, Matthew, Zhuang, Yong, Ding, Wei, Crouter, Scott E, and Chen, Ping

   ACM Transactions on Knowledge Discovery from Data (TKDD) 2021

   Bib PDF

   @article{zhuang2021mitigating,
     bibtex_show = {true},
     abbr = {TKDD},
     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},
     pdf = {zhuang2021mitigating/paper.pdf}
   }

2018

1. ICBK

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

   Zhuang, Yong, Small, David L, Shu, Xin, Yu, Kui, Islam, Shafiqul, and Ding, Wei

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

   Bib PDF Slides

   @inproceedings{zhuang2018galaxy,
     bibtex_show = {true},
     abbr = {ICBK},
     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},
     pdf = {zhuang2018galaxy/paper.pdf},
     slides = {zhuang2018galaxy/slides.pdf}
   }

2017

1. ICBK

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

   Zhuang, Yong, Almeida, Matthew, Morabito, Melissa, and Ding, Wei

   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,
     bibtex_show = {true},
     abbr = {ICBK},
     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},
     pdf = {zhuang2017crime/paper.pdf},
     poster = {zhuang2017crime/poster.pdf},
     slides = {zhuang2017crime/slides.pdf}
   }

2016

1. ICNSC

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

   Zhuang, Yong, Yu, Kui, Wang, Dawei, and Ding, Wei

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

   Bib PDF Slides

   @inproceedings{zhuang2016evaluation,
     bibtex_show = {true},
     abbr = {ICNSC},
     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},
     pdf = {zhuang2016evaluation/paper.pdf},
     slides = {zhuang2016evaluation/slides.pdf}
   }

2. CI

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

   Zhuang, Yong, and Ding, Wei

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