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 leader of BIGSCity, is an Assocaite 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 20 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.

• 2016 - present    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

COVID-19

• 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), 2020. (Acceptance rate=16.8%, CCF A)
• 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, working paper. SSRN
• J. Wang, K. Tang, K. Feng, and W. Lv, “High temperature and high humidity reduce the transmission of COVID-19.” Available at SSRN 3551767, 2020, working paper. SSRN

Explainable AI

• J. Wang, Z. Peng, X. Wang, C. Li and J. Wu, “Deep fuzzy cognitive maps for interpretable multivariate time series prediction,” in IEEE Transactions on Fuzzy Systems, doi: 10.1109/TFUZZ.2020.3005293.  (IF = 9.518)  paper read more
• J. Wang, Y. Wu, M. Li, X. Lin, J. Wu, 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), 2020. (Acceptance rate=16.8%, CCF A) 
• L. Cong, K. Tang, J. Wang, Y. Zhang, “AlphaPortfolio for investment and economically interpretable AI.” Available at SSRN 3554486, 2020, working paper. SSRN
• 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, pp. 5273–5280, 2019. (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 (KDD'19), pp. 1900–1908, 2019. (Acceptance rate=14.2%, 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 (KDD'18), pp. 2437–2446, ACM, 2018. (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 2016 IEEE 16th International Conference on Data Mining (ICDM'16), pp. 499–508, IEEE, 2016. (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. (IF=2.776)  read more
  

Fintech & Econometrics

• L. Cong, K. Tang, J. Wang, Y. Zhang, “AlphaPortfolio for investment and economically interpretable AI.” Available at SSRN 3554486, 2020, working paper. SSRN
• 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, 2019. (Acceptance rate=14.2%, 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, working paper. read more
• K. Feng, H. Hong, K. Tang, and J. Wang, “Decision making with machine learning and ROC curves,” Available at SSRN 3382962, 2019, working paper. read more

Spatio-temporal Data Mining & Urban Computing

• N. Wu, X. Zhao, J. Wang, 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), 2020. (Acceptance rate=16.8%, CCF A)  read more
• 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 (TMC), 2019. (IF=4.474, 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 and Applications (MONET), pp. 1–16, 2019. 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, 2019. (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 and Data Engineering (TKDE), 2019. (IF=3.857, 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 (KDD'19), pp. 539–547, 2019.  (Acceptance rate= 14.2%, 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 and Data Engineering (TKDE), 2019.  (IF=3.857, 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, ACM, 2018. 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,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp'18), vol. 2, no. 3, p. 112, 2018. (CCF A) 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, ACM, 2018. (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 Thirty-Second AAAI Conference on Artificial Intelligence (AAAI'18), 2018. (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, ACM, 2017. (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 Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17), 2017. (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,” in 2016 IEEE 16th International Conference on Data Mining (ICDM'16), pp. 499–508, IEEE, 2016. (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. (IF=2.776) 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, vol. 58, no. 10, pp. 1–18, 2015. read more
• J. Wang, F. Gao, P. Cui, C. Li, and Z. Xiong, “Discovering urban spatio-temporal structure from time-evolving traffic networks,” in Asia-Pacific Web Conference (APWeb'14), pp. 93–104, Springer, 2014. read more
• Z. Zhai, B. Liu, J. Wang, H. Xu, and P. Jia, “Product feature grouping for opinion mining,” IEEE Intelligent Systems, vol. 27, no. 4, pp. 37–44, 2011. (IF=4.464) 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, vol. 211, pp. 79–87, 2017. (IF=3.309) 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 and Computer Applications (JNCA), vol. 71, pp. 167–180, 2016. (IF=5.273) 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 and Computer Applications (JNCA), vol. 53, pp. 103–114, 2015. (IF=5.273) 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, vol. 17, no. 8, pp. 1664–1667, 2013. (IF=3.457) read more
• J. Wang, J. Wen, J. Zhang, and Y. Han, “TCP-FIT: An improved TCP congestion control algorithm and its performance,” in 2011 Proceedings IEEE INFOCOM (INFOCOM), pp. 2894–2902, IEEE, 2011. (Acceptance rate=16.0%, CCF A) 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 and Computer Sciences, vol. 94, no. 11, pp. 2295–2305, 2011. 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, IJCAI 2017, KDD 2018
• 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.