About

The UMass Lowell Center of Biomedical and Health Research in Data Sciences (CHORDS), where Professor Hong Yu is associated, conducts cutting-edge informatics research to accelerate biomedical and healthcare discoveries through innovative computational methods and technologies in information science, data science, and translational science. The center includes leaders and experts from a diverse set of fields including computer science, epidemiology, biostatistics, nursing, public health, and mathematics. The research focuses on leveraging big data in the biomedical field to improve health outcomes by mining literature, analyzing surveys for social determinants of health, and using electronic health records for surveillance and health risk factors.

Research topics

• Machine Learning
• Computer Science
• Artificial Intelligence
• Mathematics
• Data Mining
• Political Science
• Engineering
• Geography
• Statistics
• Reliability engineering
• Law

Selected publications

• Understanding China’s Belt and Road Initiative

  Asia in transition · 2024 · 80 citations

  1st authorCorresponding
  • Political Science
  • Political Science
  • Geography

  This book series, indexed in Scopus, is an initiative in conjunction with Springer under the auspices of the Universiti Brunei Darussalam -Institute of Asian Studies (http://ias.ubd.edu.bn/).It addresses the interplay of local

  DOI

• RUL Prediction of Wind Turbine Gearbox Bearings Based on Self-Calibration Temporal Convolutional Network

  IEEE Transactions on Instrumentation and Measurement · 2022 · 90 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  The prediction of the remaining useful life (RUL) of wind turbine gearbox bearings is critical to avoid catastrophic accidents and minimize downtime. Temporal convolutional network (TCN), as a potential method of RUL prediction, utilizes dilated causal convolution to extract historic information in the time series, by which it can avoid the disadvantage of long-term dependence faced by classical recurrent neural networks (RNNs). However, a large amount of local information is lost after dilated causal convolution, restricting further improvement of accuracy in RUL prediction or even making TCN invalid when the time series data are not sufficient. To address this issue, an improved TCN denoted as self-calibration temporal convolutional network (SCTCN) is proposed for RUL prediction of wind turbine gearbox bearings, in which the dilated causal convolution of TCN is inherited to extract the long-term historic information, and the self-calibration module is used to focus on the local information in the time series. As a result, SCTCN can learn more complete historic information to improve the accuracy of RUL prediction. Bearing RUL prediction experiments on both test bench and wind turbine gearbox are performed to verify the effectiveness of the proposed method, and the experimental results show that SCTCN has higher prediction accuracy compared with other state-of-the-art methods.

  DOI

• Incremental approaches for heterogeneous feature selection in dynamic ordered data

  Information Sciences · 2020 · 45 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Data Mining
  DOI

Recent grants

• Improving Suicide Prediction using NLP-Extracted Social Determinants of Health

  NIH · $3.1M · 2020–2025

Frequent coauthors

• Yan Wang

  Chinese Academy of Medical Sciences & Peking Union Medical College

  748 shared

• Yang Wang

  University of Science and Technology of China

  456 shared

• Feng Li

  First Affiliated Hospital of Chengdu Medical College

  242 shared

• Qi Liu

  154 shared

• Jie Zheng

  144 shared

• Zhigang Wang

  Chinese Academy of Medical Sciences & Peking Union Medical College

  120 shared

• Fei Xu

  Nanchang University

  88 shared

• Xiaohui Wang

  South China University of Technology

  79 shared

Similar researchers at University of Massachusetts Amherst

• Benjamin Brauh-141
• Vincent Rotelloh-132
• Steve Granickh-99
• Andrew McCallumh-89
• Zhu Chenh-85