My teaching spans data science, artificial intelligence, signal processing, and applied mathematics, with a strong emphasis on conceptual clarity, practical relevance, and rigorous thinking. I aim to equip students with both theoretical foundations and the ability to apply methods to real-world problems in neuroscience, engineering, and digital health. Across all levels, my teaching philosophy emphasizes active learning, critical reasoning, and the responsible use of data-driven methods.
Current Teaching (Monash University)
I currently teach and contribute to undergraduate and postgraduate units within the Faculty of Information Technology and related programs at Monash University. These units cover core and advanced topics in data science, artificial intelligence, and computational methods, and are designed to prepare students for both research and industry pathways.
Teaching responsibilities include curriculum design, lecturing, assessment development, and supervision of student projects, with a focus on maintaining academic rigor while supporting diverse student backgrounds.
Program Leadership and Curriculum Development
In addition to teaching individual units, I have held leadership roles in curriculum and program development. As Director and Deputy Director of the Master of Artificial Intelligence program at Monash University, I was responsible for academic leadership, accreditation processes, curriculum renewal, and strategic growth of the program.
During this period, the program experienced substantial growth in enrolments and successfully achieved accreditation, reflecting a strong alignment between curriculum design, industry relevance, and academic standards.
Previous Teaching Experience
Prior to my current role, I taught a range of subjects in engineering, applied mathematics, and signal processing at several institutions. This experience has informed a teaching approach that balances mathematical rigor with intuition, and theory with application.
Teaching has included lectures, tutorials, laboratories, and project-based supervision across undergraduate and postgraduate levels.
Supervision and Project-Based Learning
A central component of my teaching involves supervision of Honours, Master’s, and PhD research projects. Supervision is structured to support students in developing strong problem formulation skills, methodological competence, and clear scientific communication.
Students are encouraged to engage with open datasets, reproducible workflows, and collaborative research environments, often contributing to ongoing research programs in epilepsy, anaesthesia, and neural systems.
Teaching Philosophy
My teaching philosophy is guided by several core principles:
• Build strong conceptual foundations before introducing complex tools
• Encourage critical thinking rather than rote application of methods
• Integrate theory with real-world data and case studies
• Support independent thinking while providing structured guidance
These principles aim to prepare students not only for assessments, but for long-term careers in research, industry, and professional practice.
Opportunities for Students
Students interested in research-oriented projects or advanced study are encouraged to explore opportunities described on the Team page. Many teaching activities are closely connected to active research projects, allowing motivated students to transition smoothly from coursework into research.