王静远 - 北京航空航天大学

个人简介

王静远，男，副教授，硕士生导师。2011 年7 月毕业于清华大学计算机系，获工学博士学位。现任北京航空航天大学计算机学院副教授，硕士研究生导师。研究兴趣：大数据与人工智能；应用方向：智慧城市、计算金融、智慧健康。承担和参与课题包括：国家自然科学基金重点项目/面上项目/青年项目、973 项目、863“智慧城市（一期/二期）”项目、国家重点研发计划等国家级科研项目多项；欧盟第六框架项目、“中国共产党第十八次全国代表大会会务辅助系统”建设等国际合作与横向课题项目十余项。发表学术论文30 余篇，其中包括大数据领域顶级学术会议ACM KDD、IEEE ICDM，人工智能领域顶级学术会议AAAI，计算机通讯领域顶级学术会议IEEE Infocom、ACM Mobicom，以及权威国际学术期刊IEEE Intelligent Systems、IEEE Communications Letters等。并申请中国专利9项，美国专利2项，AVS 标准提案一项。研究成果已经被中国移动、科大讯飞、腾讯地图、北京市交通委、万达期货、公安部第一研究所、发改委国储局、国家卫计委等政府及企事业单位实际应用。毕业生去向包括微软亚洲研究院、高德地图、百度大数据实验室等国内外知名企业，并有多名毕业生赴卡耐基梅隆、宾夕法尼亚大学、南加州大学等美国知名大学深造。担任Frontiers of Computer Science 期刊 Managing Editor，The Third IEEE/ASE International Conference on Big Data Science and Computing会议Program Committee Member，The International Conference on Cloud and Service Computing 会议 Publication Chair，AAAI会议Program Committee Member，中国计算机学会大数据专委会通讯委员，中国城市科学研究会大数据专委会委员，自动化学会经济管理专委会SIG委员等。

研究领域

大数据与人工智能；应用方向：智慧城市、计算金融、智慧健康

近期论文

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Spatio-temporal Data Mining & Urban Computing S. Guo, Q. Shen, Z. Liu, C. Chen, C. Chen, J. Wang, Z. Li and K. Xu, "Seeking based on dynamic prices: Higher earnings and better strategies in ride-on-demand services," IEEE Transactions on Intelligent Transportation Systems (ITS), 2023. (CCF B, IF = 9.551) read more J. Ji, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, J. Zhang, and Y. Zheng, "Spatio-temporal self-supervised learning for traffic flow prediction," in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). (CCF A, Acceptance rate = 19.6%) read more code J. Jiang, C. Han, WX. Zhao, and J. Wang, "PDFormer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction," in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). (CCF A, Acceptance rate = 19.6%) read more code W. Jiang, WX. Zhao, J. Wang, and J. Jiang, "Continuous trajectory generation based on two-stage GAN," in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23). (CCF A, Acceptance rate = 19.6%) read more code J. Jiang, D. Pan, H. Ren, X. Jiang, C. Li, and J. Wang, "Self-supervised trajectory representation learning with temporal regularities and travel semantics," in Proceedings of the 39th International Conference on Data Engineering (ICDE'23). (CCF A) read more code J. Ji, J. Wang, J. Wu, B. Han, J. Zhang, and Y. Zheng, "Precision cityshield against hazardous chemicals threats via location mining and self-supervised learning," in Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'22), pp. 3072-3080. (CCF A, Acceptance rate = 14.9%) read more Z. Wang, Z. Pan, S. Chen, S. Ji, X. Yi, J. Zhang, J. Wang, et al., "Shortening passengers’ travel time: A dynamic metro train scheduling approach using deep reinforcement learning," IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. (CCF A, IF = 9.235) read more H. Wang, K. Zhou, WX. Zhao, J. Wang, and J. Wen, "Curriculum pre-training heterogeneous subgraph transformer for top-n recommendation," ACM Transactions on Information Systems (TOIS), vol. 41, no. 19, pp, 1-28, 2022. (CCF A, IF = 4.797) read more J. Wang, J. Ji, Z. Jiang and L. Sun, "Traffic flow prediction based on spatiotemporal potential energy fields," IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. (CCF A, IF = 9.235) read more J. Ji, J. Wang, Z. Jiang, J. Jiang, and H. Zhang, "STDEN: Towards physics-guided neural networks for traffic flow prediction," in Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22), vol. 36, no. 4, pp. 4048-4056. (CCF A, Acceptance rate = 15.0%) read more code J. Wang, J. Jiang, W. Jiang, C. Li, and W. X. Zhao, “Libcity: An open library for traffic prediction,” in Proceedings of the 29th International Conference on Advances in Geographic Information Systems (SIGSPATIAL'21), pp. 145–148. (Acceptance rate = 22.4%) read more code J. Wang, X. Lin, Y. Zuo, and J. Wu, "DGeye: Probabilistic Risk Perception and Prediction for Urban Dangerous Goods Management," ACM Transactions on Information Systems (TOIS), vol. 39, no. 28, pp. 1–30, 2021. (CCF A, IF = 4.797) read more J. Wang, N. Wu, X. Zhao, "Personalized route recommendation with neural network enhanced A* search algorithm," IEEE Transactions on Knowledge and Data Engineering (TKDE), no. 12, pp. 5910-5924, 2021. (CCF A, IF = 9.235) read more code L. Ye, S. Pan, J. Wang, J. Wu, and X. Dong, "Big data analytics for sustainable cities: An information triangulation study of hazardous materials transportation," Journal of Business Research, vol. 128, pp. 381–390, 2021. (IF = 7.55) read more 吴俊杰, 郑凌方, 杜文宇, 王静远, "从风险预测到风险溯源:大数据赋能城市安全管理的行动设计研究," 《管理世界》, 2020. (IF = 5.355) read more 吴俊杰, 刘冠男, 王静远, 左源, 部慧, 林浩, "数据智能: 趋势与挑战," 《系统工程理论与实践》, 2020. (IF = 2.858) read more S. Guo, C. Chen, J. Wang, et al., "A force-directed approach to seeking route recommendation in ride-on-demand service using multi-source urban data," IEEE Transactions on Mobile Computing (TMC), 2020. (CCF A, IF = 5.577) read more J. Ji, J. Wang, Z. Jiang, J. Ma and H. Zhang, "Interpretable spatiotemporal deep learning model for traffic flow prediction based on potential energy fields," 2020 IEEE International Conference on Data Mining (ICDM'20), pp. 1076-1081. (CCF B) read more N. Wu, X. W. Zhao, J. Wang, and D. Pan, "Learning effective road network representation with Hierarchical Graph Neural Networks," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'20), pp.6-14. (CCF A, Acceptance rate = 16.8%) read more code S. Guo, C. Chen, J. Wang, et al., "Rod-revenue: Seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data," IEEE Transactions on Mobile Computing (TMC), vol. 19, no. 9, pp. 2202–2220, 2019. (CCF A, IF = 5.577) read more S. Guo, C. Chen, J. Wang, et al., "Fine-grained dynamic price prediction in ride-on-demand services: Models and evaluations," Mobile Networks Applications, vol. 25, no. 2, pp.505-520, 2020.(IF = 3.426) read more N. Wu, J. Wang, W. X. Zhao, and Y. Jin, "Learning to effectively estimate the travel time for fastest route recommendation," in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM'19), pp.1923-1932. (CCF B, Acceptance rate = 19.4%) read more J. Wang, N. Wu, X. Lu, X. Zhao, and K. Feng, "Deep trajectory recovery with fine-grained calibration using kalman filter," IEEE Transactions on Knowledge Data Engineering (TKDE), 2019. (CCF A, IF = 9.235) read more J. Wang, N. Wu, W. X. Zhao, F. Peng, and X. Lin, "Empowering A* search algorithms with neural networks for personalized route recommendation," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19), pp. 539-547. (CCF A, Acceptance rate = 18.4%) read more code J. Wang, J. Wu, Z. Wang, F. Gao, and Z. Xiong, "Understanding urban dynamics via context-aware tensor factorization with neighboring regularization," IEEE Transactions on Knowledge Data Engineering (TKDE), vol. 32, no. 11, pp.14, 2020. (CCF A, IF = 9.235) read more S. Guo, C. Chen, J. Wang, Y. Liu, K. Xu, and D. M. Chiu, "Dynamic price prediction in ride-on-demand service with multi-source urban data," in Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous'18), pp.412-421. (CCF C) read more S. Guo, C. Chen, J. Wang, Y. Liu, K. Xu, D. Zhang, and D. M. Chiu, "A simple but quantifiable approach to dynamic price prediction in ride-on-demand services leveraging multi-source urban data," in Proceedings of the ACM on Interactive, Mobile, Wearable Ubiquitous Technologies (IMWUT'18), pp.1-24. read more J. Wang, X. Wang, and J. Wu, "Inferring metapopulation propagation network for intra-city epidemic control and prevention," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'18), pp.830-838. (CCF A, Acceptance rate = 18.4%) read more J. Wang, X. He, Z. Wang, J. Wu, N. J. Yuan, X. Xie, and Z. Xiong, "CD-CNN: A partially supervised cross-domain deep learning model for urban resident recognition," in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'18), pp.192-199. (CCF A, Acceptance rate = 24.6%) read more J. Wang, C. Chen, J. Wu, and Z. Xiong, "No longer sleeping with a bomb: A duet system for protecting urban safety from dangerous goods," in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'17), pp.1673-1681. (CCF A, Acceptance rate = 17.4%) read more J. Wang, Y. Lin, J. Wu, Z. Wang, and Z. Xiong, "Coupling implicit and explicit knowledge for customer volume prediction," in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'17), pp.1569-1575. (CCF A, Acceptance rate = 24.6%) read more J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong, "Traffic speed prediction and congestion source exploration: A deep learning method," 2016 IEEE 16th International Conference on Data Mining (ICDM'16), pp.499-508. (CCF B, Acceptance rate = 8.6%) read more J. Wang, Y. Mao, J. Li, Z. Xiong, and W.-X. Wang, "Predictability of road traffic and congestion in urban areas," PloS one, vol. 10, no. 4, pp.e0121825, 2015. (IF = 3.24) read more C. Yin, Z. Xiong, H. Chen, J. Wang, D. Cooper, and B. David, "A literature survey on smart cities," Science China Information Sciences (SCIS), vol. 58, no. 10, pp.1-18, 2015. (IF = 4.38) read more J. Wang, F. Gao, P. Cui, C. Li, and Z. Xiong, "Discovering urban spatio-temporal structure from time-evolving traffic networks," Asia-Pacific Web Conference (APWeb'14), pp.93-104. read more Z. Zhai, B. Liu, J. Wang, H. Xu, and P. Jia, "Product feature grouping for opinion mining," IEEE Intelligent Systems (IS), vol. 27, no. 4, pp.37-44, 2011. (IF = 3.405) read more COVID-19 & e-Health J. Wang, H. Shi, J. Ji, X. Lin, and H. Tian, "High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making — Wuhan City, Hubei Province, China, January 1–February 29, 2020," China CDC Weekly, vol. 5, no. 4, pp. 76-81, 2023. (IF = 4.7) read more H. Shi, J. Wang, J. Cheng, et al., "Big data technology in infectious diseases modeling, simulation and prediction after the COVID-19 outbreak: A survey," Intelligent Medicine, 2023. read more Y. Hou, K. Tang, J. Wang, et al., "Assortative mating on blood type: Evidence from one million Chinese pregnancies," in Proceedings of the National Academy of Sciences (PNAS), 2022. (IF = 11.205) read more H Shi, Q Tian, J Wang, and J Cheng, "Libepidemic: An open-source framework for modeling infectious disease with bigdata," in Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM'22), pp. 4980-4984. (CCF B) read more H. Ren, J. Wang, and WX. Zhao, "RSD: A reinforced siamese network with domain knowledge for early diagnosis," in Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM'22), pp. 1675-1684. (CCF B) read more H. Ren, J. Wang, and WX. Zhao, "Generative adversarial networks enhanced pre-training for insufficient electronic health records modeling," in Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'22), pp. 3810-3818. (CCF A, Acceptance rate = 14.9%) read more Z. Wang, P. Wu, J. Wang, et al., "Assessing the asymptomatic proportion of SARS-CoV-2 infection with age in China before mass vaccination," Journal of the Royal Society Interface, vol. 19, 2022. (IF = 4.293) read more X. Wang, X. Lin, P. Yang, Z. Wu, G. Li, J. M. McGoogan, Z. Jiao, X. He, S. Li, H. Shi, J. Wang, et al., "Coronavirus disease 2019 Outbreak in Beijing’s Xinfadi Market, China: a Modeling Study to Inform Future Resurgence Response," Infectious Diseases of Poverty, vol. 10, pp. 1-10, 2021. (IF = 4.520) read more H. Ren, J. Wang, W. X. Zhao, and N. Wu, “RAPT: Pre-training of time-aware transformer for learning robust healthcare representation,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'21), pp. 3503–3511. (CCF A, Acceptance rate=19.6%) read more L. Pee, S. L. Pan, J. Wang, and J. Wu, “Designing for the future in the age of pandemics: A future-ready design research (FRDR) process,” European Journal of Information Systems (EJIS), vol. 30, no. 2, pp. 157-175, 2021. (CCF B, IF = 4.344) read more X. Cui, L. Zhao, Y. Zhou, X. Lin, R. Ye, K. Ma, J.-F. Jiang, B. Jiang, Z. Xiong, H. Shi, J. Wang, et al., “Transmission dynamics and the effects of non-pharmaceutical interventions in the COVID-19 outbreak resurged in Beijing, China: A descriptive and modelling study,” BMJ open, vol. 11, no. 9, 2021. (IF = 2.692) read more LW. Cong, K. Tang, B. Wang, J. Wang, "An AI-assisted economic model of endogenous mobility and infectious diseases: The case of COVID-19 in the United States." Available at SSRN 3901449, 2021. read more J. Wang, K. Tang, K. Feng, et al., “Impact of temperature and relative humidity on the transmission of covid-19: A modelling study in china and the united states,” BMJ open, vol. 11, no. 2, 2021. (IF = 2.692) read more code J. Wang, X. Lin, Y. Liu, Qilegeri, K. Feng and H. Lin, “A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'20). (CCF A, Acceptance rate = 16.8%) read more code J. Wang, K. Tang, K. Feng, and W. Lv, “When is the covid-19 pandemic over? Evidence from the stay-at-home policy execution in 106 Chinese cities,” Available at SSRN 3561491, 2020. read more Explainable AI J. Ji, J. Wang, Z. Jiang, J. Jiang, H. Zhang, "STDEN: Towards physics-guided neural networks for traffic flow prediction," in Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI'22), vol. 36, no. 4, pp. 4048-4056. (CCF A, Acceptance rate = 15.0%) read more code J. Wang, J. Ji, Z. Jiang and L. Sun, "Traffic flow prediction based on spatiotemporal potential energy fields," IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. (CCF A, IF = 9.235) read more J. Wang, N. Wu, X. Zhao, "Personalized route recommendation with neural network enhanced A* search algorithm," IEEE Transactions on Knowledge and Data Engineering (TKDE), no. 12, pp. 5910-5924, 2021. (CCF A, IF = 9.235) read more code J. Wang, Z. Peng, X. Wang, C. Li, and J. Wu, "Deep fuzzy cognitive maps for interpretable multivariate time series prediction," IEEE Transactions on Fuzzy Systems (TFS), vol. 29, no. 9, pp. 2647-2660, 2020. (CAA A, IF = 12.029) read more J. Wang, Y. Wu, M. Li, X. Lin, J. Wu, and C. Li, "Interpretability is a kind of safety: An interpreter-based ensemble for adversary defense," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'20), pp.15-24, 2020. (CCF A, Acceptance rate = 16.8%) read more J. Ji, J. Wang, Z. Jiang, J. Ma and H. Zhang, "Interpretable spatiotemporal deep learning model for traffic flow prediction based on potential energy fields," 2020 IEEE International Conference on Data Mining (ICDM'20), pp. 1076-1081. (CCF B) read more L. W. Cong, K. Tang, J. Wang, and Y. Zhang, "AlphaPortfolio for investment and economically interpretable AI," Available at SSRN 3554486, 2020. read more J. Wang, N. Wu, X. Lu, X. Zhao, and K. Feng, "Deep trajectory recovery with fine-grained calibration using kalman filter," IEEE Transactions on Knowledge Data Engineering (TKDE), 2019. (CCF A, IF = 9.235) read more J. Wang, K. Feng, and J. Wu, "SVM-Based deep stacking networks," in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'19), vol. 33, no. 01, pp. 5273–5280. (CCF A, Acceptance rate = 16.2%) read more J. Wang, N. Wu, W. X. Zhao, F. Peng, and X. Lin, "Empowering A* search algorithms with neural networks for personalized route recommendation," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19), pp. 539-547. (CCF A, Acceptance rate = 18.4%) read more code J. Wang, Y. Zhang, K. Tang, J. Wu, and Z. Xiong, "Alphastock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19), pp. 1900–1908. (CCF A, Acceptance rate = 18.4%) read more J. Wang, Z. Wang, J. Li, and J. Wu, "Multilevel wavelet decomposition network for interpretable time series analysis," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'18), pp. 2437–2446. (CCF A, Acceptance rate = 18.4%) read more J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong, "Traffic speed prediction and congestion source exploration: A deep learning method," in Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM'16), pp.499-508. (CCF B, Acceptance rate = 8.6%) read more J. Wang, Y. Mao, J. Li, Z. Xiong, and W.-X. Wang, "Predictability of road traffic and congestion in urban areas," PloS one, vol. 10, no. 4, p. e0121825, 2015. (CCF B, IF = 3.24) read more Fintech & Econometrics J. Wang, C. Yang, X. Jiang, and J. Wu,"WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis," in Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'23). (CCF A) read more A. Subrahmanyam, K. Tang, J. Wang, X. Yang, "Leverage is a double-edged Sword," The Journal of Finance (JF), Forthcoming, 2023. (UTD 24, IF = 7.87) read more B. Du, X. Sun, J. Ye, K. Cheng, J. Wang and L. Sun, "GAN-based anomaly detection for multivariate time series using polluted training set," IEEE Transactions on Knowledge & Data Engineering (TKDE), no. 01, pp. 1-1, 2021. (CCF A, IF = 9.235) read more 王静远, 葛逸清, 汤珂, 邓雅琳, "调整期货交易规则可以降低投资者杠杆吗？" 《管理科学学报》, 2020. (IF = 4.346) read more L. W. Cong, K. Tang, J. Wang, and Y. Zhang, "Alphaportfolio for investment and economically interpretable AI," Available at SSRN 3554486, 2020. read more J. Wang, Y. Zhang, K. Tang, J. Wu, and Z. Xiong, "Alphastock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19), pp. 1900-1908. (CCF A, Acceptance rate = 18.4%) read more H. Hong, X. Lin, K. Tang, and J. Wang, "Artificial-intelligence assisted decision making: A statistical framework," Available at SSRN 3508224, 2019. read more K. Feng, H. Hong, K. Tang, and J. Wang, "Decision making with machine learning and ROC curves," Available at SSRN 3382962, 2019. read more Transportation Protocols for Big Data W. Jing, D. Tong, Y. Wang, J. Wang, Y. Liu, and P. Zhao, "MaMR: High-performance MapReduce programming model for material cloud applications," Computer Physics Communications (CPC), vol. 211, pp.79-87, 2017. (IF = 4.39) read more J. Wang, J. Wen, J. Zhang, Z. Xiong, and Y. Han, "TCP-FIT: An improved TCP algorithm for heterogeneous networks," Journal of Network Computer Applications (JNCA), vol. 71, pp.167-180, 2016. (IF = 6.281) read more J. Wang, J. Wen, C. Li, Z. Xiong, and Y. Han, "DC-Vegas: A delay-based TCP congestion control algorithm for datacenter applications," Journal of Network Computer Applications (JNCA), vol. 53, pp.103-114, 2015. (IF = 6.281) read more J. Wang, J. Wen, Y. Han, J. Zhang, C. Li, and Z. Xiong, "CUBIC-FIT: A high performance and TCP CUBIC friendly congestion control algorithm," IEEE Communications Letters (CL), vol. 17, no. 8, pp.1664-1667, 2013. (IF = 3.436) read more J. Wang, J. Wen, J. Zhang, and Y. Han, "TCP-FIT: An improved TCP congestion control algorithm and its performance," in Proceedings of IEEE International Conference on Computer Communications (INFOCOM'11), pp.2894-2902. (CCF A) read more