Faculty
Bin Zhu
Associate Professor of Business Analytics, Director of Center for Business Analytics and Applied AI
Business Analytics

Bin Zhu

541-737-1803

bin.zhu@bus.oregonstate.edu

Faculty
Bin Zhu
Overview
Overview
Background
Publications

Overview

Career Interests

Dr. Bin Zhu is an Associate Professor of Business Information Systems in the College of Business. Prior to OSU she was an assistant professor at Boston University. She earned her Ph.D. in Management Information Systems from University of Arizona. Her current research interests include business intelligence, information analysis, social network, human-computer interaction, information visualization, computer-mediated communication, and knowledge management systems. She has been a lead author for papers that have appeared in Information Systems Research, Decision Support Systems, Journal of the American Society for Information Science and Technology, IEEE Transaction on Image Processing, and D-Lib Magazine. Her research also received an IBM faculty award.  Her teaching interests are business intelligence; database analysis and design; telecommunication; web technology; business programming; data structure and algorithms; e-commerce; information security/assurance; management information systems.

Background

Education

Ph.D. in Management  Information Systems, Minor in Computer Science, May 2002
Management Information Systems Department
Eller College of Management
University of Arizona, Tucson, AZ 85721

M.S. Atmospheric Science, May 1997
Department of Atmospheric Science
University of Arizona, Tucson, AZ 85721

B.S. Meteorology, July 1989
Department of Geophysics
Beijing University, Beijing, China

Experience

  • Associate Professor, Business Information Systems - September 2014-present, College of Business, Oregon State University
  • Assistant Professor, Business Information Systems - September 2011-2014, College of Business, Oregon State University
  • Assistant Professor, IS Department - July 2002-2011, School of Management, Boston University
  • Research Associate, Department of Management Information Systems, University of Arizona - August 1997-2002
  • Research Assistant, Atmospheric Science Department,  University of Arizona - 1994-1997
  • Associate Director of Information Systems, Chengdu Bureau of Meteorology, Chengdu, Sichuan, P.R. China - 1992-1994
  • Weather Forecaster, Chengdu Bureau of Meteorology, Chengdu, Sichuan, P.R. China - 1989-1992

Professional Affiliations

  • Association of Computing Machinery (ACM)
  • Institute of Electrical and Electronics Engineers (IEEE)
  • Association for Information Systems (AIS)

Honors & Awards

  • Best paper nomination, International Conference on Information Quality 2008, Boston, MA
  • IBM Faculty Research Award, Amount: $40k, 2003
  • Junior Faculty Research Fund, School of Management, Boston University, 2003-2004 , 2005-2006, 2006-2007, and 2008-2010
  • Doctoral Consortium, Annual ACM CHI (Computer Human Interaction) Conference: Human Factors in Computing Systems, Minneapolis, Minnesota, 2002
  • Graduate College Fellowship of University of Arizona, 1999-2000
  • Graduate Registration Scholarship, Department of Management Information Systems, University of Arizona, 1999-2002

Publications

Academic Journal
Business Analytics

“The Hl-index: Improvement of H-index Based on Quality of Citing Papers”

This paper proposes hl-index as an improvement of the h-index, a popular measurement for the research quality of academic researchers. Although the h-index integrates the number of publications and the academic impact of each publication to evaluate the productivity of a researcher, it assumes that all papers that cite an academic article contribute equally to the academic impact of this article. This assumption, of course, could not be true in most times. The citation from a well-cited paper certainly brings more attention to the article than the citation from a paper that people do not pay attention to. It therefore becomes important to integrate the impact of papers that cite a researcher’s work into the evaluation of the productivity of the researcher. Constructing a citation network among academic papers, this paper therefore proposes hl-index that integrating the h-index with the concept of lobby index, a measures that has been used to evaluate the impact of a node in a complex network based on the impact of other nodes that the focal node has direct link with. This paper also explores the characteristics of the proposed hl-index by comparing it with citations, h-index and its variant g-index.
Details

1021-1031 pages 2014 Akadémiai Kiadó and Springer Science+Business Media Li Zai Xiangbin Yan Bin Zhu View on: Google Scholar

Conference
Business Analytics

“Gender Classification for Product Reviewers in China: A Data-Driven Approach”

While it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the associations between the linguistic features used in those studies and gender type usually varies with context. The prototype system we propose to demo validates a framework for the development of gender classification that uses a more “data-driven” approach. It constantly extracts content-specific features from the discussion content. And the system could automatically adjust itself to accommodate the contextual changes in order to achieve better classification accuracy.
Details

2013 Proceedings of 23rd Workshop on Information Technology and Systems (WITS'2013) Bin Zhu Xiangbin Yan Jing Wang

Conference
Business Analytics

“A cognitive-neural approach to explaining market oscillations in a fully recurrent adaptive agent population”

Recreating market oscillations to study the markets often makes use of induced activity reversal via finite share or auction thresholds, strategically replacing agents via bankruptcy or genetic algorithm rules, heavily data specific network parameterization, or stochastic randomness. However, such techniques do not shed any additional light on how and why intelligent individual scale agents may spontaneously and rationally decide to endogenously change from a buying to a selling posture within a population. This paper introduces Social Netmap, an agent based population of general purpose, parameter-free, adaptive agents adjusting their behavior in real time to the directly observed aggregate and individual behaviors of their neighbors much like real intelligent actors might in a population. Without relying on random processes, validated parameters, turning-point thresholds, or agent replacement, Social Netmap was able to endogenously create typical market oscillations in 21 out of 30 cases of real Dow Jones Industrial Average data. Social Netmap points towards future work in more realistic group behavior of intelligent, rational agents.
Details

2013 Charles Wong Massimiliano Versace Bin Zhu

Academic Journal
Business Analytics

“An ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study”

In order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in a real world, we apply the ACP (Artificial societies, Computational experiments, and Parallel execution) approach to public health emergency management. We conducted a case study on the largest collective outbreak of H1N1 influenza at a Chinese university in 2009. We built an artificial society to reproduce H1N1 influenza outbreaks. In computational experiments, aiming to obtain comparable results with the real data, we applied the same intervention strategy as that was used during the real outbreak. Then we compared experiment results with real data to verify our models, including spatial models, population distribution, weighted social networks, contact patterns, students’ behaviors, and models of H1N1 influenza disease, in the artificial society. We then applied alternative intervention strategies to the artificial society. The simulation results suggested that alternative strategies controlled the outbreak of H1N1 influenza more effectively. Our models and their application to intervention strategy improvement show that the ACP approach is useful for public health emergency management
Details

1028-1041 pages 2013 Wei Duan Zhidong Cao Youzhong Wang Bin Zhu Daniel Zeng Fei-Yue Wang Xiaogang Qiu Hongbin Song Yong Wang View on: Google Scholar