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James Saunderson

Dr

 https://orcid.org/0000-0002-5456-0180

Accepting PhD Students

PhD projects

I am currently looking for PhD students who are interested in doing foundational research in mathematical optimisation and its applications. Possible projects could range from developing efficient low-memory optimisation algorithms that scale to enormous datasets and have provable performance guarantees, to developing the foundations of hyperbolic programming (a far-reaching generalization of linear programming), to devising new computational methods for problems in signal processing, statistics, quantum information, or machine learning.

20082020

Research activity per year

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Personal profile

Biography

James Saunderson is currently a lecturer in the Electrical and Computer Systems Engineering department at Monash University.

James received the Bachelor of Engineering and Bachelor of Science degrees (both with Honours) from the University of Melbourne in 2008. He then completed MS and PhD degrees in Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT) in 2011 and 2015 respectively. Before joining Monash, he spent a year as a Postdoctoral scholar jointly in Electrical Engineering at Caltech and the University of Washington.

Monash teaching commitment

ECE4132 Control System Design, Semester 2, 2018, 2019, 2020

ECE2111 Signals and Systems, Semester 2, 2017, 2018, 2019, 2020

ECE3062 Electronics and Control (control part), Semester 2, 2016, 2017

 

Research interests

James' research is focused on mathematical optimisation, especially methods based on convex optimisation and semidefinite programming, and their application broadly in science and engineering. He is particularly interested in understanding algebraic structure in optimisation, and exploiting it to develop improved computational methods, with performance guarantees, for important classes of optimisation problems arising in practice.

Research area keywords

  • Convex Optimization
  • Semidefinite Programming
  • Signal Processing
  • Machine Learning
  • Mathematics
  • Applied Mathematics - Operations Research
  • Applied Mathematics
  • Computational Mathematics
  • Optimization

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