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Dr. Azadeh Ghari-Neiat is a Senior Lecturer in Software Engineering at the University of Queensland. Her research sits at the intersection of the Internet of Things (IoT), mobile computing, crowdsourcing, federated learning, and cybersecurity, with a particular focus on building trustworthy, privacy-preserving intelligent systems deployed at scale. Her current work explores federated fine-tuning of large language models (LLMs), enabling collaborative model adaptation across distributed environments without compromising data privacy, with emerging interests in the security and robustness of autonomous AI agents – bridging her long-standing expertise in distributed computing with the evolving challenges of trustworthy generative AI.

She completed her PhD in Computer Science at RMIT University in 2018. Prior to joining UQ, she was a Senior Lecturer at Deakin University, where she received two teaching awards and an innovation award from the School of IT in 2023 in recognition of her excellence in teaching, student engagement, and innovation, and previously held positions at the University of Sydney as a postdoctoral research fellow and casual lecturer.

At UQ, she coordinates COMP2701 (Generative AI) and lectures in DECO3800/7380 (Design Computing Studio 3), supervises postgraduate research students in areas including trustworthy LLM systems, and serves as program convenor of the Master of Software Engineering programs.

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.