DrBahadorreza Ofoghi

With nearly two decades of teaching experience across multiple Australian universities, I have demonstrated a sustained commitment to educational excellence by designing and leading diverse curricula ranging from foundational programming and database technologies to advanced capstone projects, artificial intelligence, and data analytics. I possess a deep interest and extensive experience delivering high-quality curriculum across the computer science spectrum. My teaching portfolio spans foundational subjects, including programming, object-oriented development, and database technologies, to specialized, advanced units in predictive analytics, natural language processing, and artificial intelligence. This expertise extends to leading critical undergraduate and postgraduate capstone units, where I have mentored emerging professionals through project management frameworks and served as both an acting company director and a product owner for industry-aligned team projects. My commitment to pedagogical excellence and leadership in unit design across all levels of study was formally recognized with the 2025 Teacher of the Year award for the Faculty of Science, Engineering, and Built Environment at Deakin University, distinguishing my contribution among the Faculty’s 400+ staff members.

I currently supervise three PhD students and have previously supervised three PhD candidates to timely completion, supporting their successful transitions into academic and industry careers. Beyond doctoral supervision, I actively work with student researchers across various levels, having guided numerous students through dedicated research units. I also provide hands-on mentorship and financial support through my active research program, which has attracted three consecutive SEBE Associate Dean (Research) Summer Research Awards ($2,000 each) since 2023. These competitive awards have funded targeted student projects at the cutting edge of applied natural language processing and healthcare, including:

• 2025: From rule bases to language models: A comprehensive review of medical concept normalization methods.
• 2024: Data curation and feasibility study of large language models for medical information retrieval and synthesis.
• 2023: An analysis of large language model capabilities in synthesizing retrieval-augmented generation of medicine-related responses.