About

G. Greg Wang is a Professor of Pharmacology and Cancer Biology at Duke University and a member of the Duke Cancer Institute. He is based at the MSRB3, 3 Genome Court, Durham, NC. His role involves leading research in pharmacology and cancer biology, contributing to the academic and scientific community through his expertise and leadership. As a primary faculty member, he is engaged in advancing understanding in his field, supporting research initiatives, and mentoring students and colleagues within the Duke University Medical School and Duke Health systems.

Research topics

• Computer Science
• Algorithm
• Artificial Intelligence
• Data Mining
• Computer network
• Database
• World Wide Web
• Operating system
• Distributed computing

Selected publications

• GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification

  IEEE Transactions on Knowledge and Data Engineering · 2020 · 228 citations

  • Computer Science
  • Algorithm
  • Artificial Intelligence

  Feature reduction is an important aspect of Big Data analytics on today&#x2019;s ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the <i>priori</i> domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no <i>a priori</i> knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than <inline-formula><tex-math notation="LaTeX">$O(N^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq1-2997039.gif"/></alternatives></inline-formula>. To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity <inline-formula><tex-math notation="LaTeX">$O(N)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-2997039.gif"/></alternatives></inline-formula>, is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at <uri>http://www.cquptshuyinxia.com/GBNRS.html</uri>.

  DOI

• A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction

  IEEE Transactions on Knowledge and Data Engineering · 2020 · 203 citations

  • Computer Science
  • Computer Science
  • Data Mining

  How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. An LF model can be improved by identifying user and service neighborhoods based on user and service geographical information. However, such information can be difficult to acquire in most applications with the considerations of information security, identity privacy, and commercial interests in a real system. Besides, the existing LF model-based QoS predictors mostly ignore the reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a data-characteristic-aware latent factor (DCALF) model to implement highly accurate QoS predictions, where ‘data-characteristic-aware’ indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Experimental results on two QoS datasets generated by real-world Web services demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors, making it highly competitive in addressing the issue of Web service selection and recommendation.

  DOI

• Optimized Content Caching and User Association for Edge Computing in Densely Deployed Heterogeneous Networks

  IEEE Transactions on Mobile Computing · 2020 · 211 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Computer network

  Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

  DOI

Frequent coauthors

• Qinghua Zhang

  151 shared

• Shuyin Xia

  Chongqing University of Posts and Telecommunications

  95 shared

• Hong Yu

  Southwest Petroleum University

  65 shared

• Lawrence Carin

  King Abdullah University of Science and Technology

  48 shared

• Jie Yang

  Zunyi Normal College

  39 shared

• Feng Hu

  38 shared

• Qun Liu

  Chongqing University of Posts and Telecommunications

  36 shared

• Xinbo Gao

  Chongqing University of Posts and Telecommunications

  28 shared

Labs

• Research by LabPI

Education

• PhD, Computer Sciece and Technology

  Xi'an Jiaotong University

  1996

• Master, Computer Sciece and Technology

  Xi'an Jiaotong University

  1994

• Bachelor, Computer Sciece and Technology

  Xi'an Jiaotong University

  1992

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