Dr Morteza Namvar
Dr

Morteza Namvar

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+61 7 344 31211

Background

Dr. Morteza Namvar is a Senior Lecturer at the UQ Business School and a member of Future of health - Business School - University of Queensland. He specializes in Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs) in business contexts. With a foundation in computer science and IT engineering, he brings interdisciplinary expertise to his research, focusing on the application of ML-driven solutions in organizational and healthcare settings.

Morteza is deeply committed to advancing ML, NLP, and LLM research in business and healthcare, mentoring PhD and HDR students in leveraging these technologies to drive innovation, automation, and efficiency across various industries. He has successfully secured competitive funding for multiple ML and NLP projects and has published extensively in leading IS and computer science journals and conferences.

Beyond research, Morteza is passionate about educating the next generation of ML practitioners. His teaching focuses on hands-on ML development using Python, equipping students with the technical skills and confidence needed to excel in the rapidly evolving field of machine learning.

Availability

Dr Morteza Namvar is:
Not available for supervision
Media expert

Fields of research

Adversarial machine learning Business analytics Business systems in context Commerce, Management, Tourism and Services Data management and data science Deep learning Graph, social and multimedia data Information and Computing Sciences Machine learning Machine learning not elsewhere classified

Research interests

  • Leveraging NLP and LLMs for Enhanced Theory Building in IS Research

    In the first theme of research I explore how NLP and LLM technologies can enhance theory building and testing in IS research. As the role of textual data in IS research grows, the ability to effectively analyze this data is critical for theoretical advancement. In this research theme, I aim to leverage NLP and LLMs to systematically analyze unstructured text data, which can provide deeper insights into complex phenomena and support the development of robust theoretical constructs. By applying these advanced techniques, I strive to enhance the rigor and relevance of theory building and testing in the field of IS research, ultimately contributing to a more nuanced understanding of the dynamic interactions between technology and society.

  • Text Feature Engineering using NLP and LLM

    In this research theme, I focus on enhancing the capabilities of machine learning models through advanced text feature engineering using NLP and LLMs. As the complexity of textual data increases, the need for sophisticated feature extraction methods becomes critical to capture the nuanced patterns and meanings embedded in text. By leveraging the deep contextual understanding and language modeling capabilities of NLP and LLMs, I aim to develop innovative techniques for transforming unstructured text into structured, meaningful features that can be effectively utilized by machine learning algorithms. This research theme seeks to bridge the gap between raw textual data and actionable insights, facilitating improved performance in various applications such as sentiment analysis, semantic search, and misinformation detection.

  • Personalization and User Experience Enhancement

    In this research theme, I focus on using NLP and LLMs to personalize and enhance user experiences across various platforms and applications. By leveraging the contextual understanding capabilities of NLP and LLMs, I aim to develop algorithms that can deliver personalized content and recommendations, providing a more engaging and tailored user experience. This research includes exploring how these models can improve natural language understanding, conversational AI, and adaptive interfaces, which are critical in the era of personalized digital interactions.

Research impacts

Morteza has led teams at UQ in several machine learning (ML) projects with industry partners, including Medical Protection Society (MPS) and PA hospital. 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.

Search Professor Morteza Namvar’s works on UQ eSpace

42 works between 2010 and 2025
All (42) Journal Article (16) Conference Publication (25) Other Outputs (1)

41 - 42 of 42 works

2010

Journal Article

Exploring the impacts of intellectual property on intellectual capital and company performance: The case of Iranian computer and electronic organizations

Namvar, Morteza, Fathian, Mohammad, Akhavan, Peyman and Gholamian, Mohammad Reza (2010). Exploring the impacts of intellectual property on intellectual capital and company performance: The case of Iranian computer and electronic organizations. Management Decision, 48 (5), 676-697. doi: 10.1108/00251741011043876

Exploring the impacts of intellectual property on intellectual capital and company performance: The case of Iranian computer and electronic organizations

2010

Conference Publication

Data mining applications in customer churn management

KhakAbi, Sahand, Gholamian, Mohammad R. and Namvar, Morteza (2010). Data mining applications in customer churn management. UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2010, Liverpool, United Kingdom, 27-29 January 2010. Piscataway, NJ, United States: IEEE. doi: 10.1109/ISMS.2010.49

Data mining applications in customer churn management

Past funding

  • 2021 - 2022
    Developing a context-specific social media monitoring tool to empower Australian small business
    UQ Knowledge Exchange & Translation Fund
    Open grant
  • 2021
    Enhancing Education Development: Analysing Members' Feedback Using Machine Learning Techniques
    Medical Protection Society Limited
    Open grant
  • 2021
    Investigating the effective use of data and analytics in Medical Protection Systems
    Medical Protection Society Limited
    Open grant

Availability

Dr Morteza Namvar is:
Not available for supervision

Supervision history

Current supervision

Completed supervision

Enquiries

Contact Dr Morteza Namvar directly for media enquiries about:

  • machine learning
  • text analytics

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