Rong Liu (rliu20)

Rong Liu

Associate Professor

School of Business

Babbio Center 619
(201) 216-5028

Education

  • PhD (2006) Penn State University (Information Systems)

Research

• Blockchain and FinTech
• Deep Learning
• Text Mining
• Business Process Management

Experience

2006 - 2017: Research Staff Member, IBM T.J. Watson Research Center

Institutional Service

  • Committee on Committees Member
  • Graduate Curriculum Committee Member

Professional Service

  • Decision Support Systems Special Issue on Blockchain Technology and Applications Associate Editor
  • Workshop on Information Systems Technology (WITS) Program Committee
  • IEEE Services Symposium on Future of Financial Services Program Commitee
  • Journal of Database Management Special issue Co-editor
  • The 2019 Pre-ICIS SIGBPS Workshop on Blockchain Technologies and Smart Contracts Program Co-chair
  • Workshop of Information Systems Technologies (WITS) Program Commitee

Appointments

2017 - present: Associate Professor, Stevens Institute of Technology

Professional Societies

  • INFORMS – INFORMS Member
  • AIS Member
  • ACM Member

Patents and Inventions

1. Recommending personalized jobs from automated review of writing samples and resumes, US 11,164,136, granted on November 2, 2021.

2. Hierarchical video concept tagging and indexing system for learning content orchestration, US 11,095,953, granted on August 17, 2021.

3. Cognitive blockchain automation and management, US 10,997,142, granted on May 4, 2021.

4. Cognitive regulatory compliance automation of blockchain transactions, US 10,984,483, granted on April 20, 2021.

5. Automatic extraction of user mobility behaviors and interaction preferences using spatio-temporal data, US 10,831,827, granted on November 10, 2020.

6. Hierarchical video concept tagging and indexing system for learning content orchestration, US 10,567,850, granted on February 18, 2020.

7. Automatic generating analytics from blockchain data, US 10,515,233, granted on December 24, 2019.

8. Cognitive blockchain automation and management, US 10,452,998, granted on October 22, 2019.

9. Search for a ticket relevant to a current ticket, US 10,257,055, granted on April 9, 2019.

10. Process management using representation state transfer architecture, US 10,235,330, granted on March 19, 2019.

11. Predicting anomalies and incidents in a computer application, US 9,921,943, granted on March 20, 2018.

12. Predicting anomalies and incidents in a computer application, US 9,582,344, granted on February 28, 2017.

13. Task association analysis in application maintenance service delivery, US 9,575,799, granted on February 21, 2017.

14. Diagnosing incidents for information technology service management, US 9,317,829, granted on April 19, 2016.

15. Process management using representation state transfer architecture, US 8,984,046, granted on March 17, 2015.

16. Monitoring enterprise performance, US 8,949,104, granted on February 3, 2015.

17. Building, reusing and managing authored content for incident management, US 8,892,539, granted on November 18, 2014.

18. Creation of flexible workflows using artifacts, US 8,661,444, granted on February 25, 2014.

19. Automatic generation of executable components from business process models, US 8,340,999, granted on December 25, 2012.

Selected Publications

Book

  1. Liu, R.; Subramanian, H. (2021). Journal Of Database Management- Special Issue on Blockchain and Smart Contracts. Journal Of Database Management. IGI-GLOBAL.

Conference Proceeding

  1. Xiong, Z.; Liu, R.; Chen, Y.; Lee, C. (2023). Overcoming the Novelty Discount: The Roles of Open-source Development in the Initial Coin Offerings (ICOs). Academy of Management Proceedings.
  2. Li, L.; Sun, J.; Liu, R.; Lappas, T. (2023). Predicting Employee Occupation Mobility: A Theory-Driven Deep Learning Approach. AMCIS 2023.
  3. Huang, J.; Liu, R.; Wu, Y.. More than Words: Quantifying Colloquial Skepticism toward Firm's Fundamentals. FMA Annual Meeting 2022 .
  4. Huang, J.; Liu, R. (2021). Trace Changes in Narrative Financial Disclosures to Detect Fraud . Workshop on Information System Technologies (WITS 2021).
  5. Wang, H.; Liu, R.; Ning, Y.; Wu, Y. (2020). Fairness of Classification Using Users’ Social Relationships in Online Peer-To-Peer Lending, FATES (Fairness, Accountability, Transparency, Ethics and Society) on the Web, joint with the Web Conference 2020 proceeding, 733-742. FATES (Fairness, Accountability, Transparency, Ethics and Society) on the Web, joint with the Web Conference 2020 proceeding.
  6. Hui, W.; Li , Y.; Ning, Y.; Liu, R.; Wu, Y. (2020). Fairness of Classification Using Users' Social Relationships in Online Peer-To-Peer Lending. (pp. 733-742). Hoboken: Proceeding of WWW conference, 2020.
  7. Su, Y.; Liu, R.; Zhao, Y.; Sun, W.; Niu, C.; Pei, D. (2019). Robust anomaly detection for multivariate time series through stochastic recurrent neural network. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2828-2837).
  8. Su, Y.; Zhao, Y.; Xia, W.; Liu, R.; Bu, J.; Zhu, J.; Cao, Y.; Li, H.; Niu, C.; Zhang, Y.; Wang, Z.; Pei, D. (2019). CoFlux: Robustly correlating KPIs by fluctuations for service troubleshooting. Proceedings of the International Symposium on Quality of Service, IWQoS 2019.
  9. Zhao, N.; Zhu, J.; Liu, R.; Liu, D.; Zhang, M.; Pei, D. (2019). Label-Less: A Semi-Automatic Labelling Tool for KPI Anomalies. Proceedings - IEEE INFOCOM (vol. 2019-April, pp. 1882-1890).
  10. Li, Z.; Zhao, Y.; Liu, R.; Pei, D. (2019). Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection. 2018 IEEE/ACM 26th International Symposium on Quality of Service, IWQoS 2018.

Journal Article

  1. Li, L.; Lappas, T.; Liu, R. (2023). Measuring Employer Attractiveness in Diverse Talent Markets. Decision Support Systems.
  2. Liu, R.; Li, M.; Sin, S.; Tan, M. (2023). Ethical requirements in job advertisements: A deep learning approach. European Management Review. Wiley.
  3. Liu, R.; Huang, J.; Zhang, Z. (2022). Tracking Disclosure Change Trajectories for Financial Fraud Detection. Production and Operations Management.
  4. Liu, R.; Kumar, A.; Lee, J. (2021). Multi-level Team Assignment in Social Business Processes: An Algorithm and Simulation Study. Information Systems Frontier.
  5. Subramanian, H.; Liu, R. (2021). Blockchain and Smart Contract - A Review and Preface to the Special Edition. Journal of Database Management (1 ed., vol. 32). Journal of Database Management.
  6. Liu, R.; Mai, F.; Wu, Y. (2020). Predicting Shareholder Litigation on Insider Trading from Financial Text: An Interpretable Deep Learning Approach, 2020, Rong Liu, Feng Mai, Jay Shan, and Ying Wu. Information and Management [ABS-3 Journal, IF: 5.155] (8 ed., vol. 57, pp. 103387).
    https://doi.org/10.1016/j.im.2020.103387.
  7. Kumar, A.; Liu, R. (2020). Business Workflow Optimization through Process Model Redesign. IEEE Transactions on Engineering Management. IEEE Transactions on Engineering Management (17 ed., vol. 1).
  8. Kumar, A.; Liu, R.; Shan, Z. (2019). Is Blockchain a Silver Bullet for Supply Chain Management? Technical Challenges and Research Opportunities. Decision Sciences. Decision Sciences.
  9. Liu, R.; Wu, F. Y.; Kumaran, S. (2010). Transforming Activity-Centric Business Process Models into Information-Centric Models for SOA Solutions. Journal of Database Management. Journal of Database Management (4 ed., vol. 21, pp. 14-34).
  10. Liu, R.; Kumar, A. (2009). Leveraging Information Sharing to Configure Supply Chains. Information Systems Frontier . Information Systems Frontier (pp. 1-13).
  11. Liu, R.; Kumar, A.; van der Aalst, W. (2007). A Formal Modeling Approach for Supply Chain Event Management. Decision Support Systems. Decision Support Systems (vol. 43, pp. 761-778). Decision Support Systems.

Magazine

  1. Liu, R.; Subramanian, H. (2019). Smart Contracts Based Agile Software Development. IEEE Blockchain Technical Briefs (June 2109 ed.).
    https://blockchain.ieee.org/technicalbriefs/june-2019/smart-contracts-based-agile-software-development.

Courses

BIA-660: Web Mining
BIA-667: Introduction to Deep Learning and Business Applications