马健兵博士2009年在英国女王大学获得人工智能博士学位,2010-2012在英国女王大学英国威廉希尔唯一官网从事智能监控研究,2013-2015在英国伯恩茅斯大学担任讲师,2015-2018年于英国考文垂大学担任讲师、副研究员,2018-2019于上海森亿医疗科技有限公司担任医学知识运营专家。马健兵博士于2007年和2003年硕士和本科毕业于清华大学。
马健兵博士承担和参与包括欧盟玛丽居里项目、英国Fusion基金项目、四川省科技厅项目等多项,累计科研经费200余万元,发表学术论文40余篇,其中SCI检索10余篇。
1. 四川省科技厅项目,“前沿科技全球发现关键技术研究”,(2021YFG0345)。
Yanfei Xiang, Jianbing Ma*, Xi Wu. A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning, Mathematical Problems in Engineering, vol:2020, 1-11, 2020. IF: 1.009
Jianbing Ma. A Correspondence between Belief Function Combination and Knowledge Base Merging.International Journal of Approximate Reasoning,2019, 104:1-8. IF: 2.845
Ma, WJ, Liu, WR, Luo, XD, McAreavey, K, Jiang, YC, Ma, JB. A Dempster-Shafer theory and uninorm-based framework of reasoning and multiattribute decision-making for surveillance system, INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, volume:34, pages: 3077-3104. Nov. 2019. IF: 2.929
Dubois, D., Liu, W.,Ma, J.and Prade, H. The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks.Information Fusion, Volume 32, Part A, November 2017, Pages 12–39. (IF: 13.669)
Pozos-Parra, P., Chavez-Bosquez, O., and Ma, J.Implementing ∆ps (PS-Merge) Belief Merging Operator for Belief Revision, Computation y Sistemas, 2017, 21(3):419-434. (IF: 0.52)
Ma, J., Liu, W. Miller, P. and Zhou, H. (2016) An Evidential Fusion Approach for Gender Profiling.Information Sciences, 333:10-20. (IF: 5.910)
M. Naiseh, N. Jiang, J. Ma and R. Ali., Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks. RCIS2020 - IEEE 14th International Conference on Research Challenges in Information Science, 2020.
M. Naiseh, N. Jiang, J. Ma and R. Ali. Personalising Explainable Recommendations: Literature and Conceptualisation, WorldCist'20 - 8th World Conference on Information Systems and Technologies, 2020.
B. Fan, N. Jiang, H. Dogan, R. Ali and J. Ma. An Ontological Approach to Inform HMI Designs for Minimising Driver Distractions with ADAS. BHCI, 2018.
B. Fan, J. Ma, N. Jiang, H. Dogan, and R. Ali. A rule-based Approach to Inform Improved HMI Designs for Minimising Driver Distractions with ADAS. IEEE SMC, 2018.