Andrew Allison

Dr Andrew Allison


School of Electrical and Mechanical Engineering

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

I am currently employed as a lecturer in the School of Electrical & Electronic Engineering. my main professional interests are teaching and research.

My Research can be grouped under five main themes:

1/ Energy Conversion and Electrical Power: I have worked with transformers, rotating machines, solar panels, chemical storage batteries, and switching electronics. I regularly teach in these areas.

2/ Fundamental analysis of Electrical Machinery: I am interested in the fundamental modelling of electrical machines, in terms of the underlying electromagnetic fields, and the storage and flow of different types of energy, leading to variational techniques. I am interested in applying the techniques of Euler, Lagrange and Hamilton to electrical machinery. The aim is to arrive at a general mathematical language for describing many machines of very different types.

3/ Statistics and Probability: My PhD. Thesis was in the area of analysis of stochastic processes. I am very interested in applying probability models to practical electrical machinery, and using the associated statistics, and measurements, to obtain rigorous estimates of parameters.

4/ Statistical Signal Processing, and Control: The identification of signals in the presence of noise is an important application of probability and statistics. I have been applying techniques of statistical signal processing to biomedical signals, to help clinicians to determine the appropriate timing and dosage of treatment.

5/ The application of unusual Mathematical techniques: My first degree was in Mathematical Sciences. I am interested in applying unusual mathematical techniques, and computational algorithms to solve difficult practical problems. This includes, complex optimization (based in ideas from Wirtinger), geometric algebra (based on ideas from Clifford and Grassmann), and the study of diffusive systems (based on ideas from Weierstrass). Some problems are very difficult to solve if we cannot expand the range of available tools.

  • Current Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2022 Co-Supervisor Mathematical Modelling and Numerical Simulation of Neural Dendritic Integration Doctor of Philosophy Doctorate Full Time Mr Tamour Zubair
    2020 Principal Supervisor An algebraic model of an agent-centric synthetic environment applied to sense-making from acoustic sources within the context of Information Superiority Doctor of Philosophy Doctorate Part Time Mr Paul Jager
  • Past Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2022 - 2024 Co-Supervisor Renewable generation and multi-timescale storage requirements—case study of Australia Doctor of Philosophy Doctorate Full Time Mr Raheel Ahmed Shaikh
    2017 - 2021 Co-Supervisor A Mathematical and Engineering Approach to the Investigation of C-Reactive Protein as a Cancer Biomarker and the Evolution of Cancer Networks Doctor of Philosophy Doctorate Full Time Mr Mohsen Dorraki
    2013 - 2017 Co-Supervisor On the Use of Stochastic Systems for Sensing and Security Doctor of Philosophy Doctorate Full Time Lachlan James Gunn
    2004 - 2009 Co-Supervisor Option Pricing Using Path Integrals Doctor of Philosophy Doctorate Full Time Dr Frederic Daniel Bonnet
    2003 - 2007 Co-Supervisor A Complex Systems Approach to Important Biological Problems Doctor of Philosophy Doctorate Full Time Dr Matthew Berryman
  • Position: Lecturer
  • Phone: 83135283
  • Email:
  • Fax: 83134360
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor 3
  • Org Unit: Electrical and Electronic Engineering

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