Tahsien Al-Quraishi, PhD

Course Coordinator in Business Analytics with a PhD in Data Science and Analytics and fifteen years of experience spanning academia and industry. Proven track record leading advanced analytics programs in partnership with institutions such as UNSW and enterprise partners including the Commonwealth Bank, translating technical modelling into measurable business outcomes. Extensive experience delivering face-to-face and fully online teaching across undergraduate, postgraduate and executive education in AI, data science, cybersecurity and computing, with a strong record of curriculum development, capability building and accreditation alignment (AACSB, TEQSA). Recognised for high-impact research supervision, Q1/Q2 publications, and applied industry research in healthcare and financial services.

My research is united by a single question: how can predictive and generative AI models be made accurate enough to be useful, and transparent enough to be trusted, in domains where the cost of an unexamined error is high? I've pursued this across three connected settings — healthcare, financial services and secure systems — from doctoral work on clinical risk prediction through to applied, industry-embedded analytics and, currently, supervision of doctoral research in explainable and generative AI.

“I'm particularly interested in building evaluation frameworks that let organisations and clinicians assess not just whether a generative or predictive model is accurate, but whether its reasoning can be audited and trusted under the same scrutiny as the human decisions it supports or replaces.” — on future research direction: explainability and governance in generative AI

Actively seeking to extend existing collaborations with UNSW, Commonwealth Bank and health-sector partners into funded research, and welcomes cross-institutional collaboration in explainable AI, secure machine learning, and applied predictive modelling in health and finance.

Works indexed on Google Scholar, spanning healthcare AI, financial and industrial analytics, cybersecurity and decision science.

Students learn analytics and AI most deeply when they're asked to solve problems that matter. Every unit is built around real, messy datasets — banking, healthcare, marketing — moving through the full analytics lifecycle from exploration to communicating results to non-technical stakeholders, with model transparency and governance treated as a thread through every technical topic, not an add-on module.

Research tested against real operational data, not benchmark datasets alone.