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]

Publications

  1. 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

  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. 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

  4. 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

  5. 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

  6. 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)

  7. 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)

  8. 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.

  9. 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)

  10. 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%)

  11. 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.

  12. 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%)

  13. 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%)

  14. 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 Grants

  • PI: "AI-powered Optimization Framework for Inter-connected Warehouse Inventory Rebalancing", Start-up Grant - City University of Hong Kong , (2019-2021)
  • PI: "Political Effects of Social Media on Social Incidents in Hong Kong", Public Policy Research, (2020-2020)
  • PI: "A Data-driven Aggregate Production-distribution System for Perishable Product Retailers", Early Career Scheme - Hong Kong Research Grants Council, (2021-2021)

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.
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

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.

Members:

 
Li Xiang, Ph.D. Student, Fall 2020 ~ Now

Li Xiang is currently working on data mining related research projects. He mainly focuses on social media analysis, transform learning, and intelligent transportation.

 
CHEN Liben, Research Assistant, Fall 2020 ~ Now

Liben built an opinion mining system for political corpus on social media. His research strength is NLP, Deep Learning, and Graph Mining.



 
YANG Tao, Research Assistant, Fall 2020 ~ Now

Tao studied a multi-language context-aware sentiment classification model. His research strength is NLP, Ranking Model, imbalanced data in machine learning.



 
QIAN Xintao, Undergraduate Research Assistant, Fall 2020

Xintao participated in the "A Data-driven Aggregate Production-distribution System for Perishable Product Retailers" project. His is good at Data Processing and Visualization.


Openings:

I am actively seeking talented graduate students and postdoctoral researchers. To apply, please drop me an email with your research interest, CV, and sample publication (if any).
Email: junmiliu@cityu.edu.hk

For PhD admission: Please visit CityU Research Degree Programmes for general information, including entrance requirements, financial aids, and Hong Kong PhD Fellowship Scheme (HKPFS).

For Joint PhD Programme: Please visit Joint PhD Programme for general information.

Professional Services

Teaching at City University of Hong Kong

Teaching at Rutgers University

Journal Reviewer

Conference PC Member

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