Lan Boon Leong

Assoc Professor

Accepting PhD Students

PhD projects

Using low-intensity ultrasound to kill cancer cells

1990 …2023

Research activity per year

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Personal profile


A/Prof Lan’s main research interests are in Biomedical Engineering and the Dynamics of Quantum Systems. His research has been funded by FRGS grants and highlighted in the media. Some of the media coverage include NST, Phys Org and The Star. He was awarded a TWAS-Unesco Associateship at the University of the Republic in Uruguay, where he was a visiting professor. For education, he received the Engineering Dean’s Award for Excellence in Teaching, the Vice-Chancellor’s Distinguished Teaching Award - Special Commendation, and the Fuller Fund Award from the American Association of Physics Teachers.

Research interests

Biomedical Engineering: mechanism and early warning of critical transition to disease state (cardiac arrhythmia, seizure); gait dynamics in neuro-degenerative disease; fall detection using AI; noise-enhanced balance

Nonlinear and Statistical Physics: dynamics of complex physical and biological systems

Quantum Physics: accuracy of standard approximations to relativistic quantum mechanics; relativistic quantum dynamics in novel materials

Monash teaching commitment

  • PHS1002 - Physics for Engineering

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 7 - Affordable and Clean Energy
  • SDG 14 - Life Below Water

Education/Academic qualification

Physics, PhD, Georgia Institute of Technology

Award Date: 1 Sep 1990

Physics, Master, Georgia Institute of Technology

Award Date: 1 Sep 1989

Physics and Mathematics, BA, Hanover College, IN, USA

Award Date: 1 Jun 1986

Research area keywords

  • quantum dynamics
  • quantum foundations
  • complex systems
  • disease detection
  • critical transition
  • biosignal processing
  • machine learning


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