Junming Liu

Assistant Professor Department of Information Systems City University of Hong Kong Office: Lau Ming Wai Building , Room 6-276 Tel: 852-3442 9323 Email: junmiliu@cityu.edu.hk

Short Biography [CV]

Junming Liu is an assistant professor in the Department of Information Systems at the City University of Hong Kong. He received his PhD from the Rutgers Business School at Rutgers University. Prior to joining Rutgers, he received his M.S. degree in Physics from Rutgers University and B.S. degree in Physics from University of Science and Technology of China (USTC). His general areas of research are data mining, supply chain analytics, urban computing, and large scale optimization, with a focus on developing effective and efficient data mining techniques for emerging big data & supply chain applications. He has published prolifically in top venues of knowledge discovery and data mining.

Publications

Journal

1. Iterative Prediction-and-Optimization for E-logistics Distribution Network Design
   Junming Liu, Weiwei Chen, Jingyuan Yang, Hui Xiong, Can Chen
   INFORMS Journal on Computing, forthcoming

2. Development of An Intelligent NLP-Based Audit Plan Knowledge Discovery System
   Qiao Li and Junming Liu
   Journal of Emerging Technologies in Accounting, 2019

3. Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network
   Bowen Du, Xiao Hu, Leilei Sun, Junming Liu, Yanan Qiao, and Weifeng Lv
   IEEE Transactions on Intelligent Transportation Systems, 2020

4. Coordinating Supplier Selection and Project Scheduling in Resource-Constrained Construction Supply Chains
   Weiwei Chen, Lei Lei, Zhengwei Wang, Mingfei Teng and Junming Liu
   International Journal of Production Research, 2018

5. A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis
   Yanjie Fu, Junming Liu, Xiaolin Li and Hui Xiong
   ACM Transactions on Intelligent Systems and Technology, 2017

Conference Proceeding

1. Storytelling in E-marketplace Live Streaming: The Effects of Narrative Content and Delivery on Sales Performance
   Yunhui Wang, Junming Liu, Yulin Fang
   In Proceedings of the Pacific Asia Conference on Information Systems (PACIS), 2021

2. Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching
   Zixuan Yuan, Hao Liu*, Junming Liu, Yanchi Liu, Yang Yang, Renjun Hu, Hui Xiong*
   In Proceedings of the Web Conference 2021 (WWW2021), Ljubljana, Slovenia. ACM, New York, NY, USA, 2021

3. Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
   Denghui Zhang, Junming Liu, Hengshu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong
   Proceedings of the 28th ACM International Conference on Information and Knowledge Management(CIKM2019), 2019

4. Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion
   Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu and Hui Xiong
   In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2017), Halifax, Nova Scotia - Canada, 2017. (AR: 67\748=8.96% Poster Research)

5. Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams
   Junming Liu, Yanjie Fu, Jingci Ming, Yong Ren, Leilei Sun and Hui Xiong
   In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2017), Halifax, Nova Scotia - Canada, 2017. (AR: 64\748=8.56% Oral Research)

6. Warehouse Site Selection for Online Retailers in Inter-connected Warehouse Networks
   Can Chen, Junming Liu, Qiao Li, Yijun Wang, Hui Xiong, and Shanshan Wu
   In Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM2017), NEW ORLEANS, USA, 2017.

7. Rebalancing bike sharing systems: a multi-source data smart optimization
   Junming Liu, Leilei Sun, Weiwei Chen, Hui Xiong
   In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2016), San Francisco, CA, USA, 2016. (AR: 70\784=8.9% Oral Research)

8. Service Usage Analysis in Mobile Messaging Apps: A Multi-Label Multi-View Perspective
   Yanjie Fu, Junming Liu, Xiaolin Li, Xinjiang Lu, Hui Xiong.
   In Proceedings of the 2016 IEEE International Conference on Data Mining (ICDM 2016), 2016. (acceptance rate: 19.6%)

9. Exploiting Human Mobility Patterns for Gas Station Site Selection
   Hongting Niu, Junming Liu, Yanjie Fu, Yanchi Liu, and Bo Lang
   In Proceedings of the 21st International Conference on Database Systems for Advanced Applications (DASFAA 2016), 2016.

10. Station Site Optimization in Bike Sharing Systems
    Junming Liu, Qiao Li, Meng Qu, Weiwei Chen, Jingyuan Yang, Xiong Hui, Hao Zhong and Yanjie Fu
    In Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM 2015), 2015. (acceptance rate: 18.2%)

11. Multi-source Information Fusion for Personalized Restaurant Recommendation
    Jing Sun, Yun Xiong, Yangyong Zhu, Junming Liu, Chu Guan, Hui Xiong
    In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2015), 2015. (acceptance rate: 20%)

12. A cost-effective recommender system for taxi drivers
    Meng Qu, Hengshu Zhu, Junming Liu, Guannan Liu, and Hui Xiong
    In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2014), 2014. (acceptance rate: 14.6%)

Under Review

1. Fleet Repositioning for Vehicle Sharing Systems: the Optimality of Balanced Myopic Policy
   Yihang Yang, Yimin Yu, Qian Wang, Junming Liu
   Available at SSRN 3763049, 2021

Research Projects

Due to the geographical and temporal unbalance of bike usage demand, a number of bikes need to be reallocated among stations so as to maintain a high service level of the bike sharing system. To optimize such bike rebalancing operations, I have addressed two challenges: (1) to accurately predict the station-level bike pick-up and drop-off demand, so as to determine the rebalancing target for each station, and (2) to efficiently solve the large-scale multiple capacitated vehicle routing problems by developing a Capacity Constrained K-center Clustering (CCKC) algorithm to decompose the multi-vehicle routing problem into smaller and tractable single-vehicle routing problems.

Another key to the success of a bike sharing system is the in-service area expansion and the bike demand prediction for expansion areas. There are two major challenges in this demand prediction problem: (1) the bike transition records are not available for the expansion area and (2) sta- tion level bike demands have big variances across the urban city. To address these challenges, I have developed a hierarchical station bike demand predictor which analyzed bike demands from functional zone level to station level. The bike station site selection was studied by integrating a station bike demand & operation cost prediction model (based on Artificial Neural Network Predictor) and a site optimization model (based on Genetic Algorithm).

The system is built to keep the inventory level of retailers at its target level to improve the service level while reducing the inventory cost. I built three modules to support the system: retailer aggregation and sales prediction, dynamic inventory model, and product replenishment and transshipment optimization. The sales of clusters of retailers were predicted by our sequence to sequence time series prediction model. The dynamic inventory model determined the factory production quantity based on the prediction value, prediction error distribution, and current inventory level. Finally, before distributing the products to retailers, I implemented a Mixed Integer Linear Programming model to determine the replenishment quantity from factory to retailers and the transshipment quantity from oversupplied retailers to the retailers with shortages.

The design goal of the recommender system for taxi drivers is to maximize their net profits by following the recommended routes for searching passages. In particular, the system can provide an entire driving route and the drivers are able to find a customer with the largest potential profit using our efficent recursive searching strategy.
The following problems have been investigated:
1) Dynamic taxi pick-up possibility prediction model;
2) Taxi driver Maximum Net Profit (MNP) route recommendation system;
3) Road rebalancing strategy for taxi crowdedness.

Professional Services

Teaching at City University of Hong Kong

• IS4834: Business Intelligence and Analytics [Syllabus]
• IS4861: Machine Learning for Business [Syllabus]
• IS6400: Business Data Analytics [Syllabus]

Teaching at Rutgers University

• Data Mining [Syllabus]
• Production and Operation Management [Syllabus]

Journal Reviewer

• Transportation Science - INFORMS
• Journal on Computing - INFORMS
• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• IEEE Transactions on Mobile Computing (TMC)
• IEEE Transactions on Big Data (TBD)
• IEEE Transactions on Systems, Man and Cybernetics: Systems
• Knowledge and Information Systems (KAIS)
• Journal of Cleaner Production
• ACM Transactions on Knowledge Discovery from Data
• ACM on Interactive, Multimedia, Wearable and Ubiquitous Technologies

Professional Service

• SPC, the International Joint Conferences on Artificial Intelligence (IJCAI 2021)
• PC, the 35rd AAAI Conference on Artificial Intelligence (AAAI 2021)
• PC, the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019)
• PC, the 39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019)
• PC, the International Conference on Knowledge Science, Engineering and Management (KSEM 2018)
• PC, the International Conference on Knowledge Science, Engineering and Management (KSEM 2017)
• PC, the 8th IEEE International Conference on Big Knowledge (ICBK 2017)