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, 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

• 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 (SIGKDD'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 (SIGKDD'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 (SIGKDD'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

• 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 (SIGKDD'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 (SIGKDD'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

• Jingyuan Wang; Jiangtao Wen; Yuxing Han. TCP congestion control for heterogeneous networks. Appl. No.: 13/085,516. Patent No.: US 8,547,839 B2. Date of Patent: Oct. 1, 2013
• Jingyuan Wang; Jiangtao Wen; Yuxing Han. TCP congestion control for heterogeneous networks. Appl. No.: 14/021,218.  Pub. No.: US 2013/0343187 A1. Pub. Date: Dec. 26, 2013

• 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.