Xiang Que | Spatiotemporal data mining | Best Researcher Award

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Assoc. Prof. Dr Xiang Que | Spatiotemporal data mining | Best Researcher Award

Member of the Discipline Inspection Commission Fujian Agriculture and Forestry University ,China

 

Dr. Xiang Que is a highly capable and promising researcher with a strong academic background, interdisciplinary expertise, and significant contributions to the fields of geoinformatics and computational science. His work in spatiotemporal data modeling, GIS, and knowledge graphs has the potential to address complex challenges in various domains. As an associate professor with postdoctoral training abroad, he combines theoretical research with practical applications, making him a suitable candidate for prestigious awards.

 

Profile:

Scopus

orcid

 

Education and Training:

  • Postdoctoral Fellow
    Department of Computer Science, University of Idaho, USA (2022/05 – 2024/05)

  • Ph.D. in Geoinformatics Engineering
    China University of Geosciences, Wuhan, China (2013/09 – 2015/05)

Professional Experience:

  • Associate Professor
    College of Computer and Information Sciences, Fujian Agriculture and Forestry University, China (2022/06 – Present)

  • Assistant Professor
    College of Computer and Information Sciences, Fujian Agriculture and Forestry University, China (2015/12 – 2022/05)

  • Study Abroad Experience

    • Postdoctoral Fellow
      Department of Computer Science, University of Idaho, USA (2022/05 – 2024/05)

      • Funded by the University of Idaho

    • Visiting Scholar
      Department of Computer Science, University of Idaho, USA (2018/12 – 2019/12)

      • Funded by the China Scholarship Council

Skills and Expertise:

Prof. Ciruela is a leading expert in neuropharmacology, molecular pharmacology, and bioengineering, with deep specialization in GPCR biology. His technical expertise includes fluorescence-based biosensors, photopharmacology, and bioengineered tools for studying neurotransmission, pain, and neurological disorders. He also has extensive experience in coordinating interdisciplinary teams, clinical trials, and translational research.

Research Focus:

  • Spatiotemporal Weighted Regression and Applications

  • GIS Spatiotemporal Data Modeling and Analysis

  • Spatiotemporal Data Mining

  • Parallel Computing

  • Knowledge Graphs

Awards and Honors: 

  • Research on Multi-Scale Spatiotemporal Weighted Regression and Its Parallel Calibration Algorithm
    National Natural Science Foundation of China (NSFC), China (No. 42202333)

    • Sole PI: Xiang Que

    • Duration: 2023/01 – 2025/12

    • Amount: ¥300,000

  • OpenMindat – Open Access and Interoperable Mineralogy Data to Broaden Community Access and Advance Geoscience Research
    National Science Foundation (NSF), United States (No. 2126315)

    • Senior Personnel (Postdoc): Xiang Que

    • Duration: 05/2022 – 05/2024

    • Amount: $792,475

  • Leveraging Big Data to Improve Prediction of Tick-Borne Disease Patterns and Dynamics
    National Science Foundation (NSF), United States (No. 2019609)

    • Senior Personnel (Postdoc): Xiang Que

Publications:

  • Que X, Huang J, Ralph J, Zhang J, Prabhu A, Morrison S, Hazen R, Ma X. (2024). Using adjacency matrix to explore remarkable associations in big and small mineral data. Geoscience Frontiers, 15(5), 101823.

  • Wang Z, Que X (corresponding author), Li M, Liu Z, Shi X, Ma X, Fan C, Lin Y. (2024). Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns. Ecological Informatics, 84, 102870.

  • Hong Y, Que X, Wang Z, Ma X, Wang H, Salati S, Liu J. (2024). Mangrove extraction from super-resolution images generated by deep learning models. Ecological Indicators, 159, 111714.

  • Que X, Ma C, Ma X, Chen Q. (2021). Parallel computing for fast spatiotemporal weighted regression. Computers & Geosciences, 150, 104723.

  • Que X, Ma X, Ma C, Chen Q. (2020). A Spatiotemporal Weighted Regression Model (STWRv1.0) for Analyzing Local Non-stationarity in Space and Time. Geoscientific Model Development Discussions, 2020, 1-33.

 

Conclusion:

Dr. Xiang Que demonstrates an excellent balance of academic rigor, research innovation, and potential for applied impact. His contributions to spatiotemporal analysis and geoinformatics are both timely and critical to advancing the field. With some improvements in collaboration, visibility, and funding, he is well-positioned for continued success in the academic and applied research domains.