Junming Liu

Ph.D. Candidate
Research Assistant
Rutgers Data Mining Group
Management Science and Information Systems Department
Rutgers Business School
Office: 1 Washington Park , Room 1003B
Tel: 862-576-2859
Email: jl1433@rutgers.edu

Short Biography [CV]


  1. 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, forthcoming, 2018

  2. 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 (TIST), 2017

  3. 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)

  4. 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)

  5. 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.

  6. 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)

  7. 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%)

  8. 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.

  9. 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%)

  10. 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%)

  11. 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%)

Research Projects

My general areas of research are data mining, recommender systems, and business analytics, with applications in urban computing and supply chain management. My primary research focuses on predictive analytics, constrained clustering, large-scale optimization and their combinations for solving emerging supply chain management problems. I started my research on Bike Sharing System and conducted numerous researches on large-scale station inventory rebalancing and facility site selection problems. Furthermore, I expanded my research to multi-product supply chain analytics and developed intelligent ordering, replenishment, and transshipment systems for perishable products and fast fashion.

Big Data Analytics in Supply Chain Management

Rebalancing Bike Sharing Systems: A Data-Driven Hierarchical Optimization.
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.

Facility Site Selection and In-service Area Expansion.
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).

Intelligent Ordering, Replenishment, and Transshipment Systems.
Industrial Collaboration: LineZone Data Technology 2018
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.

Spatioal and Temporal Data Analytics

Mobile Users' Activity Discovery under Encrypted Internet Traffic
In-App service usage analytics has recently become critical for mobile companies to enhance user experiences and for Internet providers to provide intelligent network resource distributions. A key task of In-App service usage analytics is to effectively classify mobile Internet traffic into different usage categories in a real-time manner. We develop an online iterative mobile app traffic analyzer that comprises a recursive time continuity constrained KMeans clustering (rCKC) algorithm for traffic flow segmentation and a Random Forest classifier for segmented traffic classification based on time window representation with adaptable features.

Route Recommender System for Taxi Drivers
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


Industrial Experience

Journal Reviewer

Conference PC Member

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