Carl H. Lindner College of BusinessCarl H. Lindner College of BusinessUniversity of Cincinnati

Carl H. Lindner College of Business

Jay Shan, PhD

Assistant Professor
Professional Summary
Zhe Shan
Dr. Zhe (Jay) Shan is currently an assistant professor in Department of Operations, Business Analytics, and Information Systems in the Lindner College of Business at the University of Cincinnati. He earned his Ph.D. degree in Business Administration and Operations Research from Penn State University Smeal College of Business in 2011. Before joining UC OBAIS, he worked as Assistant Professor of Information Systems at Manhattan College School of Business for two years. Personal website: http://jayshanmis.net
Contact Information
E-mail:
Office:
317 Carl H. Lindner Hall
Phone:
513-556-7006
Fax:
513-556-5499
Teaching Interest
  • Business Intelligence & Data Mining
  • Business Process Modeling & Management
  • E-commerce & Web Technology
Research Interest
  • FinTech Innovation
  • Information Security Management
  • Patient-centered Healthcare
  • Business Process Analytics
History

Institution:
INFORMS College of Artificial Intelligence
Title:
Secretary and Treasurer


Institution:
Manhattan College
Title:
Assistant Professor
End Date:
2013-05-31


Awards | Honors

Organization:
Lindner College of Business, University of Cincinnati
Name:
Faculty Development Award - Fall 2017
Year Received:
2017


Organization:
Lindner College of Business, University of Cincinnati
Name:
Faculty Development Award - Spring 2017
Year Received:
2016


Organization:
Lindner College of Business, University of Cincinnati
Name:
Faculty Development Award - Fall 2016
Year Received:
2016


Organization:
Teradata
Name:
TUN PARTNERS Faculty Scholarship
Year Received:
2016


Organization:
Lindner College of Business, University of Cincinnati
Name:
Dean's List of Teaching Excellence - Spring 2016
Year Received:
2016


Organization:
P&G
Name:
P&G Higher Education Fund
Year Received:
2016


Organization:
Lindner College of Business, University of Cincinnati
Name:
Faculty Development Award - Fall 2015
Year Received:
2015


Organization:
Lindner College of Business, University of Cincinnati
Name:
Dean's List of Teaching Excellence - Spring 2015
Year Received:
2015


Organization:
IBM T. J. Watson Research Center
Name:
Co-inventor: US Patent 20,120,296,624 - "Monitoring Enterprise Performance"
Year Received:
2015


Organization:
UC Faculty Development Council
Name:
UC FDC Individual Award
Year Received:
2014


Education

Institution:
Pennsylvania State University
Location:
University Park, PA
Major:
Supply Chain and Information Systems
Completed:
2011
Degree:
Ph D


Institution:
City University of Hong Kong
Location:
Kowloon Tong, Hong Kong
Major:
Computer Science
Completed:
2003
Degree:
Other


Institution:
Nanjing University
Location:
Nanjing, China
Major:
Computer Science
Completed:
2000
Degree:
BS


Published Contributions

Zhe Shan, Akhil Kumar,  (2012). Optimal Adapter Creation for Process Composition in Synchronous vs. Asynchronous Communication. ACM Transactions on Management Information Systems, 1-33.


Dickson Chiu, Qing Li, Patrick Hung, Zhe Shan, S.C. Cheung, Yu Yang, Matthias Farwick,  (2011). Service Composition and Interaction in a SOC Middleware Supporting Separation of Concerns with Flows and Views. Journal of Database Management, .


Liangjie Zhang, Zhe Shan, Zhizhong Mao,  (2010). An optimal-control-based decision-making model and consulting methodology for services enterprises. IEEE Transactions on Engineering Management, 607-619.



Accepted Contributions

Dong-Gil Ko, Feng Mai, Zhe Shan,  (Accepted). Improving Patient Satisfaction by Managing Paradoxical Tensions Arising from Operational Processes: Evidence from Online Patient Generated Physician Reviews. The 11th China Summer Workshop on Information Management (CSWIM 2017) .


Zhe Shan, Rong Liu,  (Accepted). A Collaborative Filtering Method to Improve the Incident Handling in IT Service Management. 2015 SIGBPS Workshop on Business Processes and Services.


Xin Xu, Nanfei Sun, Zhe Shan, Yunjia Hong,  (Accepted). An Ontology-based Hierarchical Text Clustering Method. 2015 Pre-ICIS Business Analytics Congress.


Dong-Gil Ko, Feng Mai, Zhe Shan,  (Accepted). Managing Paradoxical Tensions to Improve Patient Care Quality: A View from the Patients through Online Physician Reviews. by 25th Workshop on Information Technologies and Systems.


Feng Mai, Zhe Shan, Dong-Gil Ko,  (Accepted). Managing Paradoxical Tensions to Improve Patient Satisfaction: A View from the Patients through User-Generated Online Physician Reviews. Annual Workshop on Health IT and Economics.


Feng Mai, Qing Bai, Zhe Shan, Xin Wang, Roger Chiang,  (Accepted). The Impacts of Social Media on Bitcoin Performance. International Conference on Information Systems 2015.




Research in progress

Title:
A User Similarity Network Based Approach for Distributed Recommendation Systems

Description:
Recommendation has now become an indispensable part of people’s everyday online experiences including viewing stream videos, listening to music, shopping online, and so on. However, serious issues on customer data privacy, recommendation security, and difficulty in generating cross-site recommendations arise with the centralized server-based architecture currently employed by online service and product providers. We propose a distributed recommender system based on the construction and maintenance of a user similarity network in which each user maintains only a small number of close neighbors for peer to peer (P2P) neighbor discovery and recommendation generation. Empirical results using the Netflix dataset show that our proposed system achieves comparable recommendation quality as the centralized recommendation with significantly fewer similarity calculations to identify user neighbor sets for recommendation generation. Various settings are tested on small-scale datasets to verify the practical designs of the distributed recommender system we proposed. A large-scale experiment also shows that our P2P recommender system has the potential to obtain high recommendation accuracy with very limited number of similar neighbors.

Status:
On-Going

Research Type:
Scholarly


Title:
Integrated Process Adaptation for Robust IoT Collaboration

Description:
The purpose of process adaptation is to mediate the communication between several independent IoT processes to overcome their mismatches and incompatibilities. In this work, we propose a new framework and efficient algorithms for creating optimal adapters for IoT process collaboration. This solution integrates message adaptation patterns with control flow adapters to create a complete adapter for multiple processes. The comparisons against existing methods show that our approach produces remarkable improvements.

Status:
On-Going

Research Type:
Scholarly


Title:
Using Machine Learning to Improve Phishing Detection Training


Status:
Writing Results

Research Type:
Scholarly


Presentations

Title:
Utilization of Predictive Modelling to identify socio-economic risk factors for infant mortality in Ohio
Organization:
University of Cincinnati
Year:
2016


Title:
The Impact of Operational Efficiency on Patient Perception of Healthcare Quality: Evidence from Online Physician Reviews
Organization:
CCHMC
Location:
Cincinnati, OH
Year:
2016