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Dr Morteza Namvar

Senior Lecturer

School of Business
Faculty of Business, Economics and Law
m.namvar@business.uq.edu.au
+61 7 344 31211

Overview

Dr. Morteza Namvar is a senior lecturer at UQ Business School, specializing in Business Information Systems. With a background in computer science and IT engineering, he brings valuable expertise to his research on Machine Learning (ML) and Natural Language Processing (NLP) in business settings. Morteza is passionate about exploring the applications of ML and NLP in organizational contexts. In his research program, he mentors several PhD and HDR students in leveraging these technologies to drive innovation and efficiency across various business domains. He has successfully secured funding for multiple ML and NLP projects and has disseminated his findings through publications in esteemed journals and conferences in IS and computer science. Dedicated to cultivating the next generation of ML enthusiasts, Morteza’s teaching focuses on ML development using Python and cloud computing, equipping students with the skills and confidence needed to thrive in the dynamic field of ML.

Research Interests

  • Effective use of ML
    Machine Learning (ML) is the new frontier of technology development and has enormous potential for value creation. Information systems research presents ample evidence of ML’s positive business impacts and organisational performance. However, there is limited understanding of how organisations engage in the process of generating data-driven insights. The main goal of this theme of my research is to investigate the interaction process between different stakeholders when they attempt to use ML and big data.
  • Using the existing ML and NLP techniques for social media analytics
    While in the first theme of my research, I investigate how ML should be used in organisations, in the second one, I aim to use ML techniques for improved decision-making. I use NLP techniques to provide insights for organisations. As our online interactions continue to grow rapidly, the success of enterprises and governments hinges on their ability to analyse the vast amount of text collected through various sources. In this research theme, I aim to develop new methods for analysing unstructured data, which can potentially assist a better understanding of hidden patterns in social media.
  • Improving and optimising ML and NLP techniques
    ML and NLP have great potential for helping researchers and practitioners understand public’ opinion toward crucial business and societal issues. This potential is thwarted by their purely unsupervised nature, which often leads to ML models that are not entirely explainable. In this theme of my research, I develop novel algorithms to enhance the ML results to provide more explanations about them. I assess the validity and applicability of the proposed method by investigating critical phenomena such as contact tracing mobile applications post-adoption during a time of the pandemic.

Research Impacts

Morteza has led teams at UQ in several machine learning (ML) projects with industry partners, including Medical Protection Society (MPS). In these projects, he applied the NLP techniques he has developed in his research to the text data to help the industry in improving their strategies. He had succeeded in winning the prestigious UQKx&T grant.

Publications

View all Publications

Grants

View all Grants

Supervision

  • Exploring the Impact of Generative AI on the Workplace through Natural Language Processing

    Doctor Philosophy

  • Enhancing Patient Satisfaction through Natural Language Processing (NLP): Unveiling the Depth and Uniqueness of Clinician Notes in Teleconsultation Platforms

    Doctor Philosophy

  • Text Analysis of Immigration Tweets in Australia Using Machine Learning and Natural Language Processing

    Master Philosophy

View all Supervision

Available Projects

  • Uncovering the depth of the available content in the social media

  • Uncovering the impacts of social media on cryptocurrency value

  • Optimising machine learning techniques to enhance social media monitoring algorithms

View all Available Projects

Publications

Journal Article

Conference Publication

PhD and MPhil Supervision

Current Supervision

  • Exploring the Impact of Generative AI on the Workplace through Natural Language Processing

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Enhancing Patient Satisfaction through Natural Language Processing (NLP): Unveiling the Depth and Uniqueness of Clinician Notes in Teleconsultation Platforms

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Text Analysis of Immigration Tweets in Australia Using Machine Learning and Natural Language Processing

    Master Philosophy — Principal Advisor

    Other advisors:

  • Combining Qualitative and Machine Learning Techniques Towards Effective Regulation of Data Privacy and Cybersecurity

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Using Natural Language Processing in improving clinical notetaking and providing AI-enabled recommendations to improve patients experience

    Doctor Philosophy — Principal Advisor

    Other advisors:

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Uncovering the depth of the available content in the social media

  • Uncovering the impacts of social media on cryptocurrency value

  • Optimising machine learning techniques to enhance social media monitoring algorithms

  • The effective use of machine learning (ML)

This staff member is a UQ Expert for media in the following fields:

machine learning, text analytics

They are happy to lend their expertise to your articles or broadcasts and share their research discoveries and insights with the community via media channels.

For additional assistance with story ideas, general advice and information or help with seeking further experts, please email the UQ Media Team or telephone (07) 3365 1120.

Links

ResearcherID: P-8160-2018

ORCID: 0000-0002-1988-2192

Scopus ID: 35795841800

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