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Publications

 

COVID-19 & e-Health

  • Y. Hou, K. Tang, J. Wang, D. Xie, and H. Zhang, "Assortative mating on blood type: Evidence from one million Chinese pregnancies" Proceedings of the National Academy of Sciences, 2022. (IF = 11.205) 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, 2022. 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, 2022. (Acceptance rate = 14.9%) read more

  • Z. Wang, P. Wu, J. Wang, J. Lourenço, B. Li, B. Rader, M. Laine, H. Miao, L. Wang, H. Song, N. Bharti, J. Brownstein, O. Bjornstad, C. Dye, H. Tian, "Assessing the asymptomatic proportion of SARS-CoV-2 infection with age in China before mass vaccination," Journal of the Royal Society Interface, 2022. (IF = 4.118) 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, 2022. (IF = 4.293) 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, 2021, pp. 3503–3511. (Acceptance rate=19.6%, CCF A)  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, vol. 30, no. 2, pp. 157–175, 2021. (IF = 4.344, CCF B) 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, N. Jia, W. Cao, “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, p. e047227, 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, 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, 2020. (Acceptance rate = 16.8%, CCF A) 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
  • J. Wang, K. Tang, K. Feng, X. Lin, W. Lv, K. Chen, and F. Wang, “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, p. e043863, 2021. (IF = 2.692) read more code

Explainable AI

  • 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. (IF = 12.029, CCF B) 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, 2020, pp.15-24. (Acceptance rate = 16.8%, CCF A) 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, K. Feng, and J. Wu, "SVM-Based Deep Stacking Networks," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 5273–5280. (Acceptance rate = 16.2%, CCF A) 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, 2019, pp. 1900–1908. (Acceptance rate = 18.4%, CCF A) 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, 2018, pp. 2437–2446. (Acceptance rate = 18.4%, CCF A) 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, 2016, pp.499-508. (Acceptance rate = 8.6%, CCF B) 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. (IF = 3.24, CCF B)  read more

Fintech & Econometrics

  • 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, no. 01, pp. 1-1, 2021. (IF = 9.235, CCF A)  read more
  • A. Subrahmanyam, K. Tang, J. Wang, X. Yang, "Leverage is a Double-Edged Sword." Available at SSRN 3855181, 2021. 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, 2019, pp. 1900–1908. (Acceptance rate = 18.4%, CCF A) 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

Spatio-temporal Data Mining & Urban Computing

  • 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, 2023. read more

  • 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, 2022,.(Acceptance rate = 14.9%)  read more
  • Z. Wang, Z. Pan, S. Chen, S. Ji, X. Yi, J. Zhang, J. Wang, Z. Gong, T. Li, and Y. Zheng, "Shortening passengers’ travel time: A dynamic metro train scheduling approach using deep reinforcement learning," IEEE Transactions on Knowledge and Data Engineering, 2022. (IF = 9.235, CCF A) 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, 2022.(IF = 4.797 CCF A) read more
  • 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, 2022.(Acceptance rate = 15.0%, CCF A) 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, 2021, pp. 145–148.(Acceptance rate = 22.4%)  read more  code
  • J. Wang, N. Wu, X. Zhao, "Personalized route recommendation with neural network enhanced A* search algorithm", IEEE Transactions on Knowledge and Data Engineering, no. 01, pp. 1-1, 2021.(IF = 9.235, CCF A) 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, Y. Ding, Y. Liu, X. Ke, Z. Yu, and D. Zhang, "A Force-directed Approach to Seeking Route Recommendation in Ride-on-demand Service Using Multi-source Urban Data," IEEE Transactions on Mobile Computing, 2020. (IF = 5.577, CCF A) 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, 2020, pp.6-14. (Acceptance rate = 16.8%, CCF A)  read more code
  • S. Guo, C. Chen, J. Wang, Y. Liu, X. Ke, Z. Yu, D. Zhang, and D.-M. Chiu, "Rod-revenue: Seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data," IEEE Transactions on Mobile Computing, vol. 19, no. 9, pp. 2202–2220, 2019. (IF = 5.577, CCF A) read more
  • S. Guo, C. Chen, J. Wang, Y. Liu, K. Xu, and D. M. Chiu, "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, pp.1923-1932. (Acceptance rate = 19.4% CCF B) 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. (IF = 9.235, CCF A) 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, 2019, pp.539-547. (Acceptance rate = 18.4%, CCF A) 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, vol. 32, no. 11, pp.14, 2020. (IF = 9.235, CCF A) 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. (Acceptance rate = 18.4%, CCF A) 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. (Acceptance rate = 24.6%, CCF A) 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. (Acceptance rate = 17.4%, CCF A) 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. (Acceptance rate = 24.6%, CCF A) 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. (Acceptance rate = 8.6%, CCF B) 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

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 and Networks (ICCCN'11), pp.2894-2902. read more
  • J. Wang, H. Li, Z. Zhai, X. Chen, and S. Yang, "An Improved TCP Friendly Rate Control Algorithm for Wireless Networks," IEICE Transactions on Fundamentals of Electronics, Communications Computer Sciences (TFECCS), vol. 94, no. 11, pp.2295-2305, 2011.(IF = 0.388)  read more