I am continuously looking for self-motivated PhD and MSc students. I am also continuously looking for self-motivated 2nd and 3rd year BSc students to apply for internship in my group. Please feel free to email me with your CV if you are interested.

Jingyuan Wang, the head of BIGSCity, is a Professor in School of Computer Science and Engineering, Beihang University. He got his Ph.D. degree in 2011 from Computer Science Department, Tsinghua University. He published more than 60 papers on top journals and conferences, as well as named inventor on several granted CN and US patents. His research interests include AI, data mining, and urban computing.

• 2021 - present    Professor in Beihang University (BUAA), Beijing, China
• 2016 - 2021          Associate Professor in Beihang University (BUAA), Beijing, China
• 2011 - 2016          Assistant Professor in Beihang University (BUAA), Beijing, China
• 2011    Received PhD degree from Tsinghua University, Beijing, China
• 2009    Visiting Student at Hong Kong Unversity of Science & Technology
• 2006    Received the B.S. degree from Beijing Institute of Technology, Beijing, China

Spatio-temporal Data Mining & Urban Computing

• J. Cheng, J. Wang, Y. Zhang, J. Ji, Y. Zhu, Z. Zhang, and X. Zhao, "POI-Enhancer: An LLM-based semantic enhancement framework for poi representation learning," in Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI'25). (CCF A) read more
• J. Wang, Y. Lin, and Y. Li, "GTG: Generalizable trajectory generation model for urban mobility," in Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI'25). (CCF A) read more
• C. Han, J. Wang, Y. Wang, X. Yu, H. Lin, C. Li, and J. Wu, "Bridging traffic state and trajectory for dynamic road network and trajectory representation learning," in Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI'25). (CCF A) read more
• J. Ji, W. Zhang, J. Wang, and C. Huang, "Seeing the unseen: Learning basis confounder representations for robust traffic prediction," in Proceedings of the 31th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'25).  (CCF A)  read more
• J. Xue, S. Sun, M. Liu, Y. Wang, Z. Liu, and J. Wang, "Learnable sparse customization in heterogeneous edge computing," in Proceedings of the 41th International Conference on Data Engineering (ICDE'25). (CCF A) read more
• J. Xue, S. Sun, M. Liu, Y. Wang, X. Meng, J. Wang, JB. Zhang, and K. Xu, "Burst-sensitive traffic forecast via multi-property personalized fusion in federated learning," IEEE Transactions on Mobile Computing (TMC), 2025. (CCF A, IF = 7.721) read more
• S. Guo, B. Deng, C. Chen, J. Ke, J. Wang, S. Long, and K. Xu, "Seeking in ride-on-demand service: A reinforcement learning model with dynamic price prediction," IEEE Internet of Things Journal (IOT), 2024. (CCF C, IF = 7.596) read more
• Z. Liu, Z. Li, J. Wang, and Y. He, "Full bayesian significance testing for neural networks in traffic forecasting," in Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24). (CCF A) read more code
• KH. Hettige, J. Ji, S. Xiang, C. Long, G. Cong and J. Wang, "Airphynet: Harnessing physics-guided neural networks for air quality prediction," in Proceedings of the The Twelfth International Conference on Learning Representations (ICLR'24). (CCF A, Acceptance rate = 30.8%) read more
• 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, W. Zhang, J. Wang, and C. Huang, "Seeing the unseen: Learning basis confounder representations for robust traffic prediction," in Proceedings of the 31th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'25).  (CCF A)  read more
• Z. Liu, Z. Li, J. Wang, and Y. He, "Full bayesian significance testing for neural networks in traffic forecasting," in Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24). (CCF A) read more code
• Z. Liu, Z. Li, J. Wang, and Y. He, "Full bayesian significance testing for neural networks," in Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI'24), vol. 38, no. 8, pp. 8841-8849. (CCF A, Acceptance rate = 23.75%) read more  code
• 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
• KH. Hettige, J. Ji, S. Xiang, C. Long, G. Cong, and J. Wang, "Airphynet: Harnessing physics-guided neural networks for air quality prediction," in Proceedings of the The Twelfth International Conference on Learning Representations (ICLR'24). (Acceptance rate = 30.8%) 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. 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, and 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

• C. Yang, J. Wang, X. Jiang, and J. Wu,"Learning universal multi-level market irrationality factors to Improve stock return forecasting," in Proceedings of the 31th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'25).  (CCF A)  read more
• 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
• 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

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

• National Natural Science Foundation of China: IoT & Social Computing
• National Natural Science Foundation of China: Urban Computing
• National Natural Science Foundation of China: Datacenter Congestion Control
• National High-tech R&D Program (863 Program): Smartcity
• Science and Technology Foundation of Beihang University: Data Ming
  
• Open Project Program of State Key Laboratory: Smartcity

United States authorized patents

• Jingyuan Wang; Jiangtao Wen; Yuxing Han; Jun Zhang; TCP congestion control for large latency networks, 2015-02-12, US, US20150043339A1
• Bing Zhou; Jingyuan Wang; Jiangtao Wen; Zixuan Zou; Network packet loss processing method and apparatus, 2014-07-17, US20140198651A9
• Jingyuan Wang; Jiangtao Wen; Yuxing Han; TCP congestion control for heterogeneous networks, 2013-12-26, US, US20130343187A1

China authorized patents

• Jingyuan Wang; Yu Mu; Shu Li; Ying Yang; Xu Ma; Long Wang; Zuoqi Peng; Zhang Xiong; 一种基于相对危险度决策树模型的妊娠结局影响因子评估方法, 2020-01-14, China, CN 107491656B
• Jingyuan Wang; Chao Chen; Zhang Xiong; 一种基于上下文感知的非负张量分解的城市动态分析方法, 2019-4-26, China, CN109684604A
• Jingyuan Wang; Chao Chen; Junjie Wu; Zhang Xiong; 多源数据融合的移动轨迹生成模型的时空模式挖掘方法, 2019-1-8, China, CN109165245A
• Jingyuan Wang; Yunjing Jiang; Chao Li; Yuanxin Ouyang; Zhang Xiong; 一种基于ECN机制的TCP友好速率控制方法, 2017-5-10, China, CN103297346B
• Jingyuan Wang; Fei Gao; Chao Li; Yuanxin Ouyang; Zhang Xiong; 一种微博数据管理系统及其实现方法, 2017-5-10, China, CN103488683B
• Bing Zhou; Jingyuan Wang; Jiangtao Wen; 一种媒体文件传输方法和装置, 2016-6-22, China, CN102904907B
• Jiangtao Wen; Jingyuan Wang; Yuxing Han; 异构网络的TCP拥塞控制, 2015-2-4, China, CN102739515B
• Jingyuan Wang; Bing Zhou; Jiangtao Wen; 流媒体传输控制方法、媒体传输控制方法、相关设备; 2014-10-8, China, CN102710586B
• Bing Zhou; Jingyuan Wang; Jiangtao Wen; 网络丢包处理方法及装置, 2014-7-30, China, CN102468941B

Patents in application

• Jingyuan Wang; Yuan Ma; Ying Yang; Chao Li; Xiaoxuan Zou; Qin Xu; Xu Ma; 于自注意力机制的孕期数据建模方法, 2019-11-26, China, CN110942831A
• Jingyuan Wang; Jialin Liao; Jingtian Ma; Houxing Ren; 基于强化学习的机器学习模型预测时机估计模型, 2019-11-26, China, CN111079897A
• Ning Wu; Jingyuan Wang; Rongchen Guo; Fanzhang Peng; 一种基于A星搜索和深度学习的个性化路线推荐方法, 2019-05-16, China, CN110070239A
• Ning Wu; Jingyuan Wang; Fanzhang Peng; Rongchen Guo; 基于双向长短时记忆模型和卡尔曼滤波的轨迹去噪方法, 2019-5-9, China, CN110232169A
• Xiaoda Wang; Chao Li; Jingyuan Wang; 基于深度模糊认知图模型的可解释预测方法, 2018-12-21, China, CN109492760A
• Jingyuan Wang; Xuqiao Li; Jianfeng Li; Chao Li; 一种采用深度学习融合网络模型确定手机用户位置的方法, 2018-12-3, China, CN110232169A
• Jingyuan Wang; Shu Li; Ying Yang; Xu Ma; 一种基于词向量模型的疾病模式挖掘方法及装置, 2018-11-1, China, CN109360658A
• Jingyuan Wang; Ning Wu; 一种基于深度学习和卡尔曼滤波修正的轨迹恢复方法, 2018-9-20, China, CN109409499A
• Jingyuan Wang; Yating Lin; Junjie Wu; Zhang Xiong; 一种基于线性回归因子非负矩阵分解模型的医疗机构推荐方法, 2016-11-29, China, CN106779181A

• Associate Editor of ACM Transactions on Intelligent Systems and Technology
• Associate Editor of IET Smart Cities
• Publication Chair of the 2013 International Conference on Cloud and Service Computing (CSC2013)
• Web Chair of the 28th ACM International Conference on Information and Knowledge Management (CIKM2019)
• TPC Member of BigDataScience 2014, AAAI 2017, AAAI 2018,  AAAI 2019, AAAI 2020, CIKM 2019, ICDM 2019, ICDM 2020,  IJCAI 2017, IJCAI 2019, IMWUT 2018, KDD 2018, KDD 2019, KDD 2020, WWW 2021
• Managing Editor of Frontiers of Computer Science Journal
• Reviewer: ACM IMWUT/UbiComp, IEEE T-PAMI, IEEE T-ITS, IEEE T-COM, ACM T-IST, DMKD, Computers, Environment and Urban Systems, IEEE ACCESS, IEEE T-CSVT and etc.