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Riyaz Sikora

Name

[Sikora, Riyaz]
  • Assoc Prof
 701 South Nedderman Drive
Last Updated: August 7, 2018

Biography

Riyaz Sikora holds a Bachelors of Engineering degree in Electronics and Communication Engineering from Osmania University, India and a Ph.D. in Information Systems (IS) from University of Illinois at Urbana-Champaign, specializing in Artificial Intelligence (AI). He is an Associate Professor of Information Systems in the College of Business, University of Texas at Arlington. Prof. Sikora's current research interests are in the area of learning in multi-agent information systems, machine learning, data mining, and business applications of evolutionary computation. He is a senior editor for Journal of Information Systems and e-Business Management, and serves on the editorial boards of the International Journal of Computational Intelligence and Organizations , Journal of Database Management, International Journal of Intelligent Information Technologies, is a founding co-chair of the Association of Information Systems SIG on Agent-based Information Systems, and chairs the special interest group on Enterprise Integration in the INFORMS College on Artificial Intelligence. He is a member of the Association of Computing Machinery (ACM), the American Association for Artificial Intelligence (AAAI), the IEEE Computer Society, the Association for Information Systems (AIS), the Institute for Operations Research and the Management Sciences (INFORMS), and the Decision Sciences Institute (DSI).

Professional Preparation

    • 1994 Ph.D. in Information Systems ,  University of Illinois at Urbana-Champaign
    • 1987 B.Engr in Electronics and Communications Engineering ,  Osmania University

Appointments

    • Jan 2002 to Jan 2013 Assoc Prof
      University of Texas at Arlington
    • Jan 1997 to Jan 2002 Assist Professor
      University of Illinois at
    • Jan 1994 to Jan 1997 Assist Professor
      University of Michigan-Dearborn
    • Jan 1991 to Jan 1994 Tutor for Computer Science and Mathematics
      University of Illinois
    • Jan 1989 to Jan 1994 Research and Teaching Assistant
      University of Illinois
    • Jan 1988 to Jan 1989 Resarch Assistant
      Washington University

Memberships

  • Membership
    • Oct 2002 to Present ACM
    • Oct 2002 to Present AAAI
    • Oct 2002 to Present INFORMS
    • Oct 2002 to Present ieee computer s
    • Oct 2002 to Present ais

Research and Expertise

  • Machine Learning

    machine learning

  • Data Mining

    Data Mining

  • Multi-Agent Systems

    multi-agent information systems

  • Evolutionary Computation

    business applications of evolutionary computation

Publications

      Journal Article Accepted

      • Lee, YS. and Sikora, R., “Application of Adaptive Strategy for Supply Chain Agent,” Information Systems and eBusiness Management, forthcoming, 2018

        {Journal Article }

      Journal Article 2017

      • You, L., Yao, D., Sikora, R., and Nag, B., "An Adaptive Supplier Selection Mechanism in eProcurement Marketplace," Journal of International Technology and Information Management, Vo. 26 (2), pp. 94-116, 2017.

        {Journal Article }

      Journal Article 2016

      • Lee, Y.S. and Sikora, R., "Design of Intelligent Agents for Supply Chain Management," Lecture Notes in Business Information Processing, pp. 27-39, Feb. 2016.

        {Journal Article }

      Journal Article 2014

      • Sikora, R. and You, L., “Effect of Reputation Mechanisms and Ratings Biases on Traders’ Behavior in Online Marketplaces,” International Journal of Organizational Computing and Electronic Commerce, Vol. 24 (1), pp.58-73, 2014.

        {Journal Article }
      2014

      • You, L. and Sikora, R., “Performance of Online Reputation Mechanisms under the Influence of Different Types of Biases,” Information Systems and eBusiness Management, Volume 12, Issue 3, pp 417-442, August 2014.

        {Journal Article }
      2014

      • Sikora, R. and Al-Laymoun, O., “A Modified Stacking Ensemble Machine Learning Algorithm using Genetic Algorithms,” forthcoming, Journal of International Technology and Information Management., 23(1), pp. 1-12, Nov. 2014

        {Journal Article }

      Journal Article 2012
      • Sikora, R. and Chauhan, K., “Estimating Sequential Bias in Online Reviews: A Kalman Filtering Approach,” Knowledge-Based Systems, Vol. 27, pp. 314-321, 2012.
        {Journal Article }

      Journal Article 2011
      • You, L. and Sikora, R., “An adaptive evaluation mechanism for online traders,” European Journal of Operations Research, Vol. 214 (3), pp. 739-748, Nov. 2011
        {Journal Article }

      Book Chapter 2010
      • You, L.; Sikora, R. Agent-based Modeling of Reputation for Sellers in an eMarketplace. In Strategic Advantage of Computing Information Systems in Enterprise Management; Majid Sarrafzadeh and Panagiotis Petratos, Eds.; ATINER: Athens, Greece, 2010, pp 287-304.
        {Book Chapter }

      Conference Proceeding 2010
      • L. You and R. Sikora. "The Influence of Bias on Reputation Models in E-Marketplace," in Annual DSI Meeting (San Diego, CA, 2010).
        {Conference Proceeding }
      2010
      • L. You and R. Sikora. "Agent-based Modeling of Reputation for Sellers in an eMarketpla," in 6th Annual International Conference on Computer Science and Information Systems (Athens, Greece, 2010).
        {Conference Proceeding }

      Book Chapter 2009
      • Li, J., Sikora, R., and Shaw, M., “Supply Chain Management: A Multi-Agent System Framework,” Supply Chain Management and Knowledge Management - Integrating Critical Perspectives in Theory and Practice, pp. 151 – 169, Palgrave, 2009.
        {Book Chapter }

      Journal Article 2009
      • S. Piramuthu and R. Sikora. "Iterative Feature Construction for Improving Inductive Learning Algorithms," Expert Systems with Applications, vol. 36, no. 2, pp. 3401-3406, March 2009.
        {Journal Article }

      Conference Proceeding 2008
      • L. You and R. Sikora. "An Adaptive Reputation Mechanism for Online Traders," in INFORMS Conference on Information Systems and Technology (Washington, D.C., 2008).
        {Conference Proceeding }

      Journal Article 2008
      • R. Sikora and V. Sachdev. "Learning Bidding Strategies with Autonomous Agents in Environments with Unstable Equilibrium," Decision Support Systems, vol. 46, no. 1, pp. 101-114, December 2008.
        {Journal Article }
      2008
      • R. Sikora. "Meta-Learning Optimal Parameter Values in Non-Stationary Environments," Knowledge-Based Systems, vol. 21, no. 8, pp. 800-806, December 2008.
        {Journal Article }

      Journal Article 2007
      • R. Sikora and S. Piramuthu. "Framework for Efficient Feature Selection in Genetic Algorithm based Data Mining," European Journal of Operations Research, vol. 180, no. 2, pp. 723-737, 2007.
        {Journal Article }

      Conference Proceeding 2006
      • R. Sikora. "Learning Optimal Parameter Values in Dynamic Environment: An Experiment with Softmax Reinforcement Learning Algorithm," in INFORMS Workshop on AI and Data Mining (Pittsburgh, 2006).
        {Conference Proceeding }
      2006
      • L. You and R. Sikora. "Comparison of the Effects of the Sample Size and Noise Level on the Performance of Logistic Model Tree, Neural Networks and Logistic Regression," in Annual DSI Meeting (San Antonio, TX, 2006).
        {Conference Proceeding }
      2006
      • A. Jain and R. Sikora. "A Classification of Auction Mechanism: Potentiality for Agent Modeling," in Annual Southwest DSI Meeting (Oklahoma City, 2006).
        {Conference Proceeding }
      2006
      • R. Sikora. "Meta-Learning Optimal Parameter Values in Non-Stationary Environments.," in International Symposium of Information Systems (Hyderabad, India, 2006).
        {Conference Proceeding }
      2006
      • S. Piramuthu and R. Sikora. "Genetic Algorithm based Learning using Feature Construction," in INFORMS Workshop on AI and Data Mining (Pittsburgh, 2006).
        {Conference Proceeding }

      Journal Article 2006
      • C. Langdon and R. Sikora. "Conceptualizing Coordination and Competition in Supply Chains as Complex Adaptive System," Journal of Information Systems and e-Business Management: Special Issue on Agent-based Information Systems, vol. 4, no. 1, pp. 71-81, January 2006.
        {Journal Article }
      2006
      • R. Sikora and C. Langdon. "Agent-based Information Systems and Solutions in Business," Journal of Information Systems and e-Business Management: Special Issue on Agent-based Information Systems, vol. 4, no. 1, pp. 1-4, January 2006.
        {Journal Article }
      2006
      • J. Li, R. Sikora, and M. Shaw. "A Strategic Analysis of Inter-OrganizationalInformation Sharing," Decision Support Systems, vol. 42, no. 1, pp. 251-266, October 2006.
        {Journal Article }

      Conference Proceeding 2005
      • R. Sikora and V. Sachdev. "Learning Optimal Seller Strategies with Intelligent Agents: Application of Evolutionary and Reinforcement Learning," in 15th Workshop on Information Technologies and Systems (Las Vegas, 2005).
        {Conference Proceeding }
      2005
      • A. Gurung and R. Sikora. "Learning in Multi-Agent Information Systems: A Surveyfrom IS Perspective," in Proceedings of the Eleventh Americas Conference onInformation Systems (Omaha, NE, 2005).
        {Conference Proceeding }

      Journal Article 2005
      • S. Nerur, R. Sikora, V. Balijepally, and G. Mangalaraj. "Assessing the RelativeInfluence of Journals in a Citation Network," Communications of the ACM, 2005.
        {Journal Article }
      2005
      • R. Sikora and S. Piramuthu. "Efficient Genetic Algorithm based Data Mining using Feature Selection with Hausdorff Distance," Information Technology and Management, vol. 6, no. 4, pp. 315-331, 2005.
        {Journal Article }
      2005
      • R. Sikora. "Multi-Agent Data Mining Using Evolutionary Computing," Evolutionary Computation in Data Mining, vol. 163, pp. 57-78, 2005.
        {Journal Article }

      Conference Proceeding 2003
      • C. Langdon and R. Sikora. "Conceptualizing Coordination and Competition in Supply Chains as Complex Adaptive System: An Exploratory Analysis," in Second Workshop on e-Business (Seattle, WA, 2003).
        {Conference Proceeding }

      Conference Proceeding 2002
      • R. Sikora. "Efficient Genetic Algorithm based Data Mining using Simultaneous Feature Selection and Distributed Learning," in 33rd Annual Meeting of the Decision Sciences Institute (San Diego, CA, 2002).
        {Conference Proceeding }

      Conference Paper 2001
      • Sikora, R. and Shaw, M. "Enterprise Integration: An Application of Multi Agent
        Modeling", PRISM Symposium Proceedings, Purdue University, August 2001.
        {Conference Paper }

      Journal Article 1998
      • R. Sikora and M. Shaw. "A Multi-Agent Framework for the Coordination and Integration of Information Systems," Management Science, vol. 44, no. 11, pp. S65-S78, November 1998.
        {Journal Article }

      Journal Article 1997
      • R. Sikora and M. Shaw. "Coordination Mechanisms for Multi-Agent Information Systems: Applications to Integrated Manufacturing Scheduling," IEEE Transactions on Engineering Management, vol. 44, no. 2, pp. 175-187, May 1997.
        {Journal Article }
      1997
      • I. Lee, R. Sikora, and M. Shaw. "A Genetic Algorithm Based Approach to Flexible Flow-Line Scheduling with Variable Lot Sizes," IEEE Transactions on Systems, Man, and Cybernetics, vol. 27B, no. 1, pp. 36-54, February 1997.
        {Journal Article }

      Journal Article 1996
      • R. Sikora and M. Shaw. "A Computational Study of Distributed Rule Learning," Information Systems Research, vol. 7, no. 2, pp. 189-197, June 1996.
        {Journal Article }

      Journal Article 1994
      • R. Sikora and M. Shaw. "A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning," INFORMS Journal on Computing, vol. 6, no. 2, pp. 174-187, 1994.
        {Journal Article }

      Conference Paper 1993
      • Lee, I., Sikora, R., and Shaw, M., "Joint Lot Sizing and Sequencing with Genetic
        Algorithms for Scheduling: Evolving the Chromosome Structure," In S. Forrest,
        ed., Genetic Algorithms: Proceedings of the Fifth International Conference
        (GA93), Morgan Kaufmann, San Mateo, CA, pp. 383-389, 1993.
        {Conference Paper }

      Journal Article 1992
      • Sikora, R., "Learning Control Strategies for a Chemical Process: A Distributed Approach," IEEE Expert, June, 1992, pp. 35-43
        {Journal Article }

      Journal Article 1990
      • Shaw, M and Sikora, R., "A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems," Technical Report CMURI-TR-90-28, Robotics Institute, Carnegie Mellon University, November 1990.
        {Journal Article }

Presentations

    • June  2016
      Using Reinforcement Learning For Supply Chain Management

      Lee, Y. and Sikora, R., “Using Reinforcement Learning For Supply Chain Management,”

      INFORMS International Conference, Hawaii, June 2016

      1 Event
    • December  2015
      Design of Intelligent Agents for Supply Chain Management

      Lee, Y. and Sikora, R., “Design of Intelligent Agents for Supply Chain Management,”

      Proceedings of the Workshop on eBusiness, Fort Worth, TX, Dec. 2015

      1 Event
    • June  2015
      Intelligent Agents for Supply Chain Management

      Lee, Y. and Sikora, R., “Intelligent Agents for Supply Chain Management,”

      CORS/INFORMS International Meeting, Montreal, QC, June 2015

      1 Event
    • December  2012

      Sikora, R. and You, L., “Effect of Reputation Mechanisms and Ratings Biases on Traders’ Behavior in Online Marketplaces,” Proceedings of the Workshop on eBusiness, Orlando, FL, Dec. 2012.
      1 Event

Support & Funding

This data is entered manually by the author of the profile and may duplicate data in the Sponsored Projects section.
    • Jan 2010 to Jan 2011 The Influence of Bias on Reputation Models in eMarketplace sponsored by  - $4000
    • Jan 2005 to Jan 2006 “Dynamics of Information Systems (IS) Strategy and Industry Structure: Effect sponsored by  - $9000
    • Jan 1996 to Jan 1997 "An Intelligent Fault Diagnosis System for Robotic Machines," Co-Investigator sponsored by  - $21000

Students Supervised

  • Doctoral
    • Present

      Supervisory Committee

    • Present

      Supervisory Committee

    • Present

      Dissertation Committee Chair

    • Oct 2011

      Dissertation Committee

    • Oct 2007

      Dissertation Committee Chair

    • Oct 2007

      Dissertation Committee

    • Oct 2005

      Dissertation Committee

  • Master's
    • Oct 2011

      Dissertation Committee Chair

Courses

      • INSY 3305-001 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:(1) Project Initiation and Requirements Determination; (2) Analysis Modeling; and (3) Design Modeling.

        Fall - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 3305-002 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:(1) Project Initiation and Requirements Determination; (2) Analysis Modeling; and (3) Design Modeling.

        Fall - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 5341-001 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:(1) Project Initiation and Requirements Determination; (2) Analysis Modeling; and (3) Design Modeling.

        Fall - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 5339-002 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 5339-080 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 5309-001 OBJECT-ORIENTED BUSINESS PROGRAMMING

        Java object oriented programming introductory course.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 4305-002 ADVANCED APPLICATION DEVELOPMENT

        Java object oriented programming introductory course.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 6392-001 SELECTED TOPICS IN INFORMATION SYSTEMS

        This doctoral-level course will cover the foundations of Artificial Intelligence (AI) and heuristic search and learning techniques We will cover some of the important problem-solving tools & techniques from AI including search strategies, expert systems, machine learning, data mining and move onto the more advanced topics involving agents. We will also be interested in agent-based modeling of contexts of strategic interaction (CSI) (like prisoner’s dilemma). Consequently, we shall learn effective agent learning techniques, including evolutionary methods and reinforcement learning. We will also be using a fair amount of mathematics. Familiarity with linear algebra and mathematical notations will be helpful.

        Spring - Regular Academic Session - 2018 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Fall - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • INSY 5309-001 OBJECT-ORIENTED BUSINESS PROGRAMMING

        Java object oriented programming introductory course.

        Fall - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • INSY 5339-080 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • INSY 5341-001 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • INSY 5341-080 ANALYSIS AND DESIGN

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Spring - Regular Academic Session - 2017 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 Business Data Mining

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 5339-002 Business Data Mining

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Fall - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 5341-001 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Summer - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 5309-001 OBJECT-ORIENTED BUSINESS PROGRAMMING

        Java object oriented programming introductory course.

        Summer - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 5339-002 Business Data Mining

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 6392-001 SELECTED TOPICS IN INFORMATION SYSTEMS

        This doctoral-level course will cover the foundations of Artificial Intelligence (AI) and heuristic search and learning techniques We will cover some of the important problem-solving tools & techniques from AI including search strategies, expert systems, machine learning, data mining and move onto the more advanced topics involving agents. We will also be interested in agent-based modeling of contexts of strategic interaction (CSI) (like prisoner’s dilemma). Consequently, we shall learn effective agent learning techniques, including evolutionary methods and reinforcement learning. We will also be using a fair amount of mathematics. Familiarity with linear algebra and mathematical notations will be helpful.

        Spring - Regular Academic Session - 2016 Download Syllabus Contact info & Office Hours
      • INSY 3305-001 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • INSY 5341-001 Information Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 PRINCIPLES OF BUSINESS DATA MINING

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • INSY 3300-002 OBJECT-ORIENTED BUSINESS PROGRAMMING

        Java object oriented programming introductory course.

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • INSY 5309-001 OBJECT-ORIENTED BUSINESS PROGRAMMING

        Java object oriented programming introductory course.

        Spring - Regular Academic Session - 2015 Download Syllabus Contact info & Office Hours
      • INSY 5341-001 Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Fall - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 Business Data Mining

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Fall - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • INSY 3305-001 Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Spring - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 Business Data Mining

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Spring - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • INSY 5341-001 Systems Analysis and Design

        This course will provide students with an opportunity to systematically study systems analysis and design. Both technical and practical knowledge will be emphasized. A variety of topics will be covered to familiarize the students with the concepts and techniques underlying analysis and design. Specifically, three primary areas will be covered:

        (1) Project Initiation and Requirements Determination;

        (2) Analysis Modeling; and

        (3) Design Modeling.

        Spring - Regular Academic Session - 2014 Download Syllabus Contact info & Office Hours
      • INSY 5339-001 Business Data Mining

        This course will cover the foundations of business data mining. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Finding patterns, trends, and anomalies in these datasets, and summarizing them with simple quantitative models, is one of the grand challenges of the information age.

        This course will examine tools and techniques from the fields of machine learning (AI) and statistics used in practical data mining for finding, and describing, structural patterns in data. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

        Fall - Regular Academic Session - 2013 Download Syllabus Contact info & Office Hours
      • INSY 6392-001 Agent-based Informatin Systems

        This doctoral-level course will cover the foundations of Artificial Intelligence (AI) and heuristic search and learning techniques We will cover some of the important problem-solving tools & techniques from AI including search strategies, expert systems, machine learning, data mining and move onto the more advanced topics involving agents. We will also be interested in agent-based modeling of contexts of strategic interaction (CSI) (like prisoner’s dilemma). Consequently, we shall learn effective agent learning techniques, including evolutionary methods and reinforcement learning. We will also be using a fair amount of mathematics. Familiarity with linear algebra and mathematical notations will be helpful.

        Fall - Regular Academic Session - 2013 Download Syllabus Contact info & Office Hours
      • INSY 4305-001 Advanced Application Development
        No Description Provided.
        Spring - Regular Academic Session - 2013 Download Syllabus
      • INSY 5341-001 Information Systems Analysis and Design
        No Description Provided.
        Spring - Regular Academic Session - 2013 Download Syllabus
      • INSY 3305-001 Information Systems Analysis and Design
        No Description Provided.
        Spring - Regular Academic Session - 2013 Download Syllabus
      • INSY 5352-001 Advanced Application Development
        No Description Provided.
        Spring - Regular Academic Session - 2013 Download Syllabus

Service to the Profession

  • Appointed
    • Oct 2005 to  Present Senior Editor, Journal of Information Systems and e-Business Management
      • Senior Editor, Journal of Information Systems and e-Business Management, Springer-Verlag.
    • Oct 2005 to  Present Editorial Board , Journal of Database Management, Idea Group Publishing

      Editorial Board , Journal of Database Management, Idea Group Publishing

    • Oct 2006 to  Present Editorial Board, International Journal of Intelligent Information Technologies, Idea Group Publishing

      Editorial Board, International Journal of Intelligent Information Technologies, Idea Group Publishing

    • Oct 2002 to  Present President and Co-Founder, Special Interest Group on Agent-Based Information Systems, Sponsored by the Association on Information Systems

      President and Co-Founder, Special Interest Group on Agent-Based Information Systems, Sponsored by the Association on Informatio

    • Jan 2013 to  Aug 2013 Program Committee, Workshop on e-Business, AMCIS Annual Meeting, Chicago, IL, Aug, 2013

      Program Committee, Workshop on e-Business, AMCIS Annual Meeting, Chicago, IL, Aug, 2013

    • Dec 2012 to  Dec 2012 Session Chair, Multi-Agent Systems Track, Workshop on e-Business, ICIS Annual Conference, Orlando, FL, Dec, 2012

      Session Chair, Multi-Agent Systems Track, Workshop on e-Business, ICIS Annual Conference, Orlando, FL, Dec, 2012

    • Aug 2014 to  Dec 2014 Program Committee, Workshop on eBusiness, pre-ICIS, Auckland, NZ, Dec. 2014

      Program Committee, Workshop on eBusiness, Auckland, NZ, Dec. 2014

    • Aug 2014 to  Dec 2014 Program Committee, 24th Workshop on Information Technologies and Systems, Auckland, NZ, Dec. 2014

      Program Committee, 24th Workshop on Information Technologies and Systems, Auckland, NZ, Dec. 2014

    • Aug 2013 to  Dec 2013 Program Committee, 23rd Workshop on Information Technologies and Systems, Milan, Italy, Dec. 2013

      Program Committee, 23rd Workshop on Information Technologies and Systems, Milan, Italy, Dec. 2013

    • Aug 2012 to  Dec 2012 Program Committee, Workshop on e-Business, ICIS Annual Conference, Orlando, FL, Dec, 2012

      Program Committee, Workshop on e-Business, ICIS Annual Conference, Orlando, FL, Dec, 2012

  • Volunteered
    • Oct 2012 to  Oct 2013 Reviewer

      Reviewer for ISR, WITS, ISeB, Electronic Commerce and Research Applications, and AMCIS

Service to the University

  • Appointed
    • Sept 2013 to  Present Chair, INSY AOL Committee, College of Business, UT-Arlington

      Chair, INSY AOL Committee, College of Business, UT-Arlington

    • Sept 2013 to  Present Strategic Planning Committee, Community Connections-Education, College of Business, UT-Arlington

      Strategic Planning Committee, Community Connections-Education,

                              College of Business, UT-Arlington

    • Sept 2010 to  Present Grade Appeals Committee, College of Business, UT-Arlington

      Grade Appeals Committee, College of Business, UT-Arlington

    • Sept 2009 to  Present Traffic and Parking Appeals Panel, University of Texas at Arlington

      Traffic and Parking Appeals Panel, University of Texas at Arlington

    • Oct 2003 to  Oct 2007 Library Appeals Committee, University of Texas at Arlington

      Library Appeals Committee, University of Texas at Arlington

    • Oct 2010 to  Present INSY AOL Committee, College of Business, UT-Arlington

      INSY AOL Committee, College of Business, UT-Arlington

    • Oct 2006 to  Present INSY Promotion & Tenure Committee, University of Texas at Arlington

      INSY Promotion & Tenure Committee, University of Texas at Arlington

    • Sept 2005 to  Aug 2006 INSY Revitalization Task Force, College of Business, UT-Arlington

      INSY Revitalization Task Force, College of Business, UT-Arlington

    • Oct 2002 to  Present INSY Curriculum Committee, University of Texas at Arlington

      INSY Curriculum Committee, University of Texas at Arlington

    • Oct 2002 to  Present INSY Executive Committee, University of Texas at Arlington

      INSY Executive Committee, University of Texas at Arlington

  • Volunteered
    • Sept 2008 to  Aug 2010 Faculty Development Leave Committee, University of Texas at Arlington

      Faculty Development Leave Committee, University of Texas at Arlington

  • Elected
    • Oct 2006 to  Oct 2011 Faculty Senate, University of Texas at Arlington

      Faculty Senate, University of Texas at Arlington

Other Service Activities

  • Uncategorized
    • Dec  Positions in Professional Organizations
      • 2001-present: Co-Chair, Special Interest Group on Agent-Based Information Systems,Sponsored by the Association on Information Systems
      • 1994-present: Chair, Special Interest Group on Enterprise Integration, INFORMS College on AI
    • Dec  Editorial Service
      • Senior Editor, Journal of Information Systems and e-Business Management, Springer-Verlag
      • Co-Editor of a Special Issue on Agent-based Information System, Journal of Information Systems and e-Business Management, Springer-Verlag, in press, 2005.
      • Editorial Board , Journal of Database Management, Idea Group Publishing.
      • Editorial Board, International Journal of Intelligent Information Technologies, Idea Group Publishing.
      • Editorial Board, International Journal of Computational Intelligence & Organizations,Lawrence Erlbaum and Associat
    • Dec  Professional Membership
      Institute for Operations Research and Management Science (INFORMS) American Association of Artificial Intelligence (AAAI) Association of Computing Machinery (ACM) IEEE Computer Society Association for Information Systems (AIS) Decision Sciences Institute (DSI)
    • Dec  Professional Service at Conferences and Meetings

      • Associate Editor, International Conference on Information Systems (ICIS), Phoenix, AZ, Dec. 2009.

      • Chair, Mini-track on Artificial Intelligence and Data Mining, AMCIS Annual Meeting, San Francisco, August 2009.

      • Program Committee, International Conference on Information Technology, Bhubaneswar, India, Dec. 17-20, 2008.

      • Co-Chair, Workshop on Data Mining and Health Informatics, INFORMS Annual Meeting, Washington, D.C., Oct. 2008.

      • Review Committee Co-Chair, Workshop on Artificial Intelligence and Data Mining, INFORMS Annual Meeting, Seattle, Nov. 2007.

      • Session Chair, “Advances in Learning,” Workshop on Artificial Intelligence and Data Mining, INFORMS Annual Meeting, Pittsburgh, Nov. 2006.

      • Review Committee Co-Chair, Workshop on Artificial Intelligence and Data Mining, INFORMS Annual Meeting, Pittsburgh, Nov. 2006.

      • Session Co-Chair, “Agent-Based Information Systems,” Fourth Workshop on e-Business, ICIS, Las Vegas, Dec. 2005.

      • Cluster Chair, Cluster on AI, INFORMS National meeting, San Francisco, Nov. 2005.

      • Session Chair, Multi-Agent Systems Application, INFORMS National meeting, San Francisco, Nov. 2005

      • Cluster Chair, Cluster on AI, INFORMS National meeting, New Orleans, Nov. 2005. Session Co-Chair, "Agent-Based Information Systems," Third Workshop on e-Business, ICIS, Washington, D.C., Dec. 2004.
      • Member, Program Committee, Third Workshop on e-Business, ICIS, Washington, D.C.,Dec. 2004.
      • Member, Program Committee, Ninth International Workshop on Evaluation of Modeling Methods in Systems Analysis and Design (EMMSAD'04), Riga, Latvia, June 2004.
      • Session Chair, "Agent-Based Information Systems," Second Workshop on e-Business, ICIS, Seattle, Dec. 2003.
      • Member, Program Committee, Second Workshop on e-Business, ICIS, Seattle, Dec. 2003.
      • Session Co-Chair, "Impact of New IS Capabilities on the Structure of B2B Relationships," INFORMS Annual Meeting, San Jose, Nov. 2002.
      • Member, Program Committee, 2002 Information Resources Management Association (IRMA) International Conference, Seattle, WA, May 2002.
      • Chair of DSS/AI/ES track, DSI Conference, San Francisco, CA, Nov. 2001.
      • Cluster Chair and Sessions Chair, Special cluster on Evolutionary Algorithms, INFORMS 2000 Spring Meeting, Salt Lake City, Utah, May 2000.
      • Session Chair, Genetic Algorithms and Applications, INFORMS meeting, Montreal, April 1998.
      • Cluster Chair, College of AI cluster, INFORMS, San Diego, May 1997.
    • Dec  Review Service
      • ISR (1997, 98, 2001, 02, 05)
      • Management Science (2003, 05)
      • IEEE Transactions on SMC (1993, 95, 2003, 04, 05 – 2 Reviews)
      • Electronic Markets (2005)
      • AMCIS (2005) – 3 Reviews
      • HICSS (2004) – 2 Reviews
      • IEEE Transactions on EM (2004)
      • Decision Support Systems (2004)
      • Swedish Research Council, Project Proposal Review (2003)
      • Decision Sciences Institute Annual Meeting (2003) ASME (2003)
      • IEEE Transactions of Evolutionary Computation (2002)
      • U.S. Civilian Research and Development Foundation for the Independent States of the Former Soviet Union (CRDF), Project Proposal Review (2002).
      • HICCS (2002 – 2003) – 3 Reviews
      • Information Systems and e-Business Management (2002) ICIS'2001
      • IEEE Transactions on Engineering Management (2001, 1997) - 2 Reviews
      • Journal of Database Management (2000 - 2003) – 5 Reviews
      • DSS/AI/ES Track of the Decision Sciences Institute Annual Meeting (2000) – 2 Reviews
      • Decision Support and Intelligent Systems, Turban and Aranson, 6th Edition, Prentice Hall (1999) – 1 Book review
      • Advances in Distributed Data Mining (1999) - 2 Book Chapter Reviews
      • Journal of Organizational Computing (1997) - 1 Review
      • International Journal of Computational Intelligence and Organizations (1997, 1996) - 2 Reviews
      • International Journal of Flexible Manufacturing Systems (1996, 1995) - 3 Reviews
      • Annals of Operations Research (1996) - 2 Reviews
      • Canadian Journal of Operational Research and Information Processing (INFOR) (1996) - 1 Review
      • Computers and Industrial Engineering (1994 - 1997) - 6 Reviews
      • National Science Foundation CAREER Award Program (1996) - 1 Review
      • NATO Advance Study Institute Proposal (1996) - 1 Review
    • Dec  University, College, and Department Level Service

      2010-2011: Grade Appeals Committee, College of Business, Univ. of Texas at Arlington

      2009-2011:Traffic and Parking Appeals Panel, University of Texas at Arlington

      2008-2010: Faculty Development Leave Committee, University of Texas at Arlington

      2008-current:Faculty Advisor, Indian Students Association, Univ of Texas at Arlington

      2006-2011: Faculty Senate, University of Texas at Arlington

      2003-2007:Library Appeals Committee, University of Texas at Arlington.

      2006-current: INSY Promotion & Tenure Committee, University of Texas at Arlington

      2005: INSY Revitalization Task Force
      2002-present: INSY Curriculum Committee, University of Texas at Arlington.
      2002-present: INSY Executive Committee, University of Texas at Arlington.
      2000-2001: Chair, Search Committee, IS Faculty Search, University of Illinois.
      1999-2000: Search Committee, Hoeft Chair of IS, University of Illinois.
      1998: Technology Content Committee, University of Illinois.
      1995-1997: Departmental Committee to review I&SE and Dual Graduate Programs,University of Michigan-Dearborn
      1996-1997: School of Engineering WWW Ad Hoc Committee, University of Michigan-Dearborn