Overview
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 strong 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:
- Available for supervision
- Media expert
Fields of research
Research interests
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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.
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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.
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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.
Works
Search Professor Morteza Namvar’s works on UQ eSpace
2022
Journal Article
Hybrid metaheuristics for QoS-Aware Service Composition: a systematic mapping study
Naghavipour, Hadi, Soon, Tey Kok, Idris, Mohd Yamani Idna Bin, Namvar, Morteza, Salleh, Rosli Bin and Gani, Abdullah (2022). Hybrid metaheuristics for QoS-Aware Service Composition: a systematic mapping study. IEEE Access, 10, 12678-12701. doi: 10.1109/access.2021.3133505
2021
Conference Publication
Utilitarian, hedonic and monetary motivations of using mobile learning apps: Opinion mining using big data text analytics
Gholizadeh, Mehran, Akhlaghpour, Saeed, Teixeira Isaias, Pedro and Namvar, Morteza (2021). Utilitarian, hedonic and monetary motivations of using mobile learning apps: Opinion mining using big data text analytics. Australasian Conference on Information Systems 2021, Sydney, NSW Australia, 6-10 December 2021. Association for Information Systems.
2021
Journal Article
Sensegiving in organizations via the use of business analytics
Namvar, Morteza, Intezari, Ali and Im, Ghiyoung (2021). Sensegiving in organizations via the use of business analytics. Information Technology & People, 34 (6), 1615-1638. doi: 10.1108/itp-10-2020-0735
2021
Conference Publication
Knowledge Identity (KI): A New Approach to Integrating Knowledge Management into Enterprise Systems
Intezari, Ali, Namvar, Morteza and Taghinejhad, Ramin (2021). Knowledge Identity (KI): A New Approach to Integrating Knowledge Management into Enterprise Systems. Hawaii International Conference on System Sciences (HICSS), Hawaii, United States, 5-8 January 2021. Honolulu, HI United States: HICCS. doi: 10.24251/hicss.2021.594
2021
Conference Publication
Moderating effects of time-related factors in predicting the helpfulness of online reviews: a deep learning approach
Namvar, Morteza, Boyce, James, Sarna, Jatin, Zheng, Yuanyuan, Chua Yeow Kuan, Alton and Ameli, Sina (2021). Moderating effects of time-related factors in predicting the helpfulness of online reviews: a deep learning approach. Hawaii International Conference on System Sciences 2021, Kauai, HI, United States, 5- 8 January 2021. IEEE Computer Society. doi: 10.24251/hicss.2021.092
2021
Conference Publication
Wise data-driven decision-making
Namvar, Morteza and Intezari, Ali (2021). Wise data-driven decision-making. Conference on e-Business, e-Services and e-Society, Galway, Ireland, 1-3 September 2021. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-85447-8_10
2020
Conference Publication
A novel approach to predict the helpfulness of online reviews
Namvar, Morteza (2020). A novel approach to predict the helpfulness of online reviews. Hawaii International Conference on System Sciences , Maui, HI, United States, 7-10 January 2020. Honolulu, HI, United States: Hawaii International Conference on System Sciences. doi: 10.24251/hicss.2020.348
2018
Conference Publication
Developing a block-chained knowledge management model (BCKMM): beyond traditional knowledge management
Akhavan, Peyman, Philsoophian, Maryam, Rajabion, Lila and Namvar, Morteza (2018). Developing a block-chained knowledge management model (BCKMM): beyond traditional knowledge management. 19th European Conference on Knowledge Management, ECKM 2018, Padua, Italy, 6-7 September 2018. Sonning Common, United Kingdom: Academic Conferences and Publishing International.
2018
Journal Article
Simplifying sensemaking: concept, process, strengths, shortcomings, and ways forward for information systems in contemporary business environments
Namvar, Morteza, Cybulski, Jacob L., Phang, Cynthia Su Chen, Ee, Yaw Seng and Tan, Kevin Tee Liang (2018). Simplifying sensemaking: concept, process, strengths, shortcomings, and ways forward for information systems in contemporary business environments. Australasian Journal of Information Systems, 22 1654. doi: 10.3127/ajis.v22i0.1654
2016
Journal Article
Using business intelligence to support the process of organizational sensemaking
Namvar, Morteza, Cybulski, Jacob L. and Perera, Luckmika (2016). Using business intelligence to support the process of organizational sensemaking. Communications of the Association for Information Systems, 38 (1) 20, 330-352. doi: 10.17705/1CAIS.03820
2014
Conference Publication
BI-based organizations: a sensemaking perspective
Namvar, Morteza and Cybulski, Jacob (2014). BI-based organizations: a sensemaking perspective. 35th International Conference on Information Systems, Auckland, New Zealand, 14-17 December 2014. Association for Information Systems.
2013
Journal Article
Exploring the role of intellectual capital in the development of e-business models: evidence from the Iranian carpet industry
Namvar, Morteza and Khalilzadeh, Pejman (2013). Exploring the role of intellectual capital in the development of e-business models: evidence from the Iranian carpet industry. International Journal of Commerce and Management, 23 (2), 97-112. doi: 10.1108/10569211311324902
2011
Journal Article
An approach to optimised customer segmentation and profiling using RFM, LTV, and demographic features
Namvar, Morteza, Khakabimamaghani, Sahand and Gholamian, Mohammad Reza (2011). An approach to optimised customer segmentation and profiling using RFM, LTV, and demographic features. International Journal of Electronic Customer Relationship Management, 5 (3-4), 220-235. doi: 10.1504/IJECRM.2011.044688
2011
Conference Publication
Exploring the role of human capital on firm's structural capital in Iranian e-business industry
Namvar, Morteza, Fathian, Mohammad, Gholamin, Mohammad R. and Akhavan, Peyman (2011). Exploring the role of human capital on firm's structural capital in Iranian e-business industry. 3rd International Conference on Information and Financial Engineering (ICIFE 2011), Shanghai, China, 19-21 August 2011 . Singapore: IACSIT Press.
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
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
2010
Conference Publication
A two phase clustering method for intelligent customer segmentation
Namvar, Morteza, Gholamian, Mohammad R. and KhakAbi, Sahand (2010). A two phase clustering method for intelligent customer segmentation. UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation, Liverpool, United Kingdom, 27-29 Jan 2010. Los Alamitos, CA USA: IEEE Computer Society. doi: 10.1109/ISMS.2010.48
Supervision
Availability
- Dr Morteza Namvar is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Available projects
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Exploring the Applications of LLM in Pediatric Hospitals
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Uncovering the impacts of social media on cryptocurrency value
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Optimising machine learning techniques to enhance social media monitoring algorithms
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The effective use of machine learning (ML)
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Exploring the Applications of LLM in Pediatric Hospitals
This research focuses on the innovative use of LLMs within pediatric hospitals. We will explore how NLP and LLMs can be applied to enhance patient care, streamline administrative tasks, and support medical staff in making informed decisions. Students will have the opportunity to investigate the potential of AI-driven solutions to improve healthcare outcomes for children, offering a hands-on experience in a critical and evolving area of medical technology.
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Leveraging NLP and LLMs for Enhanced Misinformation Detection
This research delves into the use of NLP and LLMs for detecting misinformation. We will explore how these advanced AI techniques can be applied to analyze and identify false or misleading information across various digital platforms. Students will engage in developing and fine-tuning models to enhance the accuracy of misinformation detection, contributing to a crucial area of study in today's information-driven world
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Optimizing Call Center Speech Detection and Resource Allocation Using LLM-Driven Chatbots in Healthcare
In this research project, we explore the integration of LLMs to enhance call center operations in the healthcare sector. By leveraging advanced LLM-driven chatbots, we aim to improve speech detection accuracy and optimize resource allocation, ultimately leading to more efficient and responsive patient support systems. This study offers students an opportunity to engage with cutting-edge AI technologies and their practical applications in healthcare
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Exploring the Applications of LLM in Pediatric Hospitals
This research focuses on the innovative use of LLMs within pediatric hospitals. We will explore how NLP and LLMs can be applied to enhance patient care, streamline administrative tasks, and support medical staff in making informed decisions. Students will have the opportunity to investigate the potential of AI-driven solutions to improve healthcare outcomes for children, offering a hands-on experience in a critical and evolving area of medical technology.
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Leveraging NLP and LLMs for Enhanced Misinformation Detection
This research delves into the use of NLP and LLMs for detecting misinformation. We will explore how these advanced AI techniques can be applied to analyze and identify false or misleading information across various digital platforms. Students will engage in developing and fine-tuning models to enhance the accuracy of misinformation detection, contributing to a crucial area of study in today's information-driven world
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Optimizing Call Center Speech Detection and Resource Allocation Using LLM-Driven Chatbots in Healthcare
In this research project, we explore the integration of LLMs to enhance call center operations in the healthcare sector. By leveraging advanced LLM-driven chatbots, we aim to improve speech detection accuracy and optimize resource allocation, ultimately leading to more efficient and responsive patient support systems. This study offers students an opportunity to engage with cutting-edge AI technologies and their practical applications in healthcare
Supervision history
Current supervision
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Master Philosophy
Text Analysis of Immigration Tweets in Australia Using Machine Learning and Natural Language Processing
Principal Advisor
Other advisors: Associate Professor Saeed Akhlaghpour, Dr Marten Risius
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Master Philosophy
Text Analysis of Immigration Tweets in Australia Using Machine Learning and Natural Language Processing
Principal Advisor
Other advisors: Associate Professor Saeed Akhlaghpour, Dr Marten Risius
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Doctor Philosophy
An Integrated Model for Speech and Text Analysis from a Sociotechnical Perspective in Healthcare Context
Principal Advisor
Other advisors: Associate Professor Saeed Akhlaghpour, Dr Marten Risius, Associate Professor Andrew Staib, Professor Andrew Burton-Jones
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Doctor Philosophy
Combining Qualitative and Machine Learning Techniques Towards Effective Regulation of Data Privacy and Cybersecurity
Principal Advisor
Other advisors: Associate Professor Ali Intezari
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Doctor Philosophy
Personal Assistant Health Systems: Leveraging NLP and Machine Learning in eHealth Data Management for Personalized Care
Principal Advisor
Other advisors: Associate Professor Saeed Akhlaghpour
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Doctor Philosophy
Exploring the Impact of Generative AI on the Workplace through Natural Language Processing
Principal Advisor
Other advisors: Professor Andrew Burton-Jones, Associate Professor Saeed Akhlaghpour
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Doctor Philosophy
Combining Qualitative and Machine Learning Techniques Towards Effective Regulation of Data Privacy and Cybersecurity
Principal Advisor
Other advisors: Associate Professor Ali Intezari
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Doctor Philosophy
Applying Natural Language Processing (NLP) and Large Language Models (LLM) to Teleconsultation for Better Patient Outcomes
Associate Advisor
Other advisors: Associate Professor Saeed Akhlaghpour, Professor Marta Indulska
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Master Philosophy
Exploring the Ethical Concerns and Psychotherapeutic Usage of Prosocial Deepfake Technology
Associate Advisor
Other advisors: Dr Avijit Sengupta
Completed supervision
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2023
Doctor Philosophy
Towards Understanding Affordances of Mobile Learning Applications Through Applying Data-Driven Computationally Intensive Theory Development Approach to Online Reviews
Associate Advisor
Other advisors: Associate Professor Saeed Akhlaghpour
Media
Enquiries
Contact Dr Morteza Namvar directly for media enquiries about:
- machine learning
- text analytics
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