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Publications

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