Professor Jonathan Manton

  • Room: Level: 05 Room: 5.3
  • Building: Electrical and Electronic Engineering
  • Campus: Parkville

Research interests

  • Differential Geometry
  • Optimisation
  • Signal Processing
  • Systems Biology
  • Systems Neuroscience

Personal webpage


Professor Jonathan Manton holds a distinguished Chair at the University of Melbourne with the title Future Generation Professor. He is also an Adjunct Professor in the Mathematical Sciences Institute at the Australian National University. He is a Fellow of the Australian Mathematical Society (FAustMS) and a Fellow of the Institute of Electrical and Electronics Engineers (FIEEE).

Professor Jonathan Manton received his Bachelor of Science (mathematics) and Bachelor of Engineering (electrical) degrees in 1995 and his Ph.D. degree in 1998, all from the University of Melbourne, Australia. From 1998 to 2004, he was with the Department of Electrical and Electronic Engineering at the University of Melbourne. During that time, he held a Postdoctoral Research Fellowship then subsequently a Queen Elizabeth II Fellowship, both from the Australian Research Council. 

In 2005 he became a full Professor in the Department of Information Engineering, Research School of Information Sciences and Engineering (RSISE) at the Australian National University. From July 2006 till May 2008, he was on secondment to the Australian Research Council as Executive Director, Mathematics, Information and Communication Sciences. Professor Jonathan Manton's traditional research interests range from pure mathematics (e.g. commutative algebra, algebraic geometry, differential geometry) to engineering (e.g. signal processing, wireless communications). Recently though, led by a desire to participate in the convergence of the life sciences and the mathematical sciences, he has started to apply his expertise to research in neuroscience. Professor Jonathan Manton also has extensive experience in software development. 

Prof. Jonathan Manton received a 2009 Future Summit Australian Leadership Award in recognition of "outstanding achievements and commitment to playing a leading role in shaping the future of Australia."

Recent publications

  1. Shames I, Selvaratnam D, Manton J. Online Optimization Using Zeroth Order Oracles. IEEE Control Systems Letters. 2020, Vol. 4, Issue 1. DOI: 10.1109/LCSYS.2019.2921593
  2. Chakraborty R, Bouza J, Manton J, Vemuri BC. A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging. 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings. Springer Verlag. 2019, Vol. 11492 LNCS. DOI: 10.1007/978-3-030-20351-1_9
  3. Li X, Tolmachev P, Pauley M, Manton J. A Distributed Transmission Scheduling Algorithm for Wireless Networks Based on the Ising Model. 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE. 2018. DOI: 10.1109/SSP.2018.8450715
  4. Deng G, Manton J, Wang S. Fast Kernel Smoothing by a Low-Rank Approximation of the Kernel Toeplitz Matrix. JOURNAL OF MATHEMATICAL IMAGING AND VISION. Springer. 2018, Vol. 60, Issue 8. DOI: 10.1007/s10851-018-0804-2
  5. Pauley M, Manton J. Global Optimisation for Time of Arrival-Based Localisation. 2018 IEEE Statistical Signal Processing Workshop, SSP 2018. 2018. DOI: 10.1109/SSP.2018.8450751
  6. McLean C, Pauley M, Manton J. Limitations of Decision Based Pile-Up Correction Algorithms. 2018 IEEE Statistical Signal Processing Workshop, SSP 2018. 2018. DOI: 10.1109/SSP.2018.8450835
  7. Tolmachev P, Dhingra R, Pauley M, Dutschmann M, Manton J. Modeling the respiratory central pattern generator with resonate-and-fire Izhikevich-Neurons. International Conference on Neural Information Processing. Springer Verlag. 2018, Vol. 11301 LNCS. DOI: 10.1007/978-3-030-04167-0_55
  8. Selvaratnam D, Shames I, Manton J, Zamani M. Numerical Optimisation of Time-Varying Strongly Convex Functions Subject to Time-Varying Constraints. 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC). Institute of Electrical and Electronics Engineers. 2018.
  9. Pauley M, Manton J. The Existence Question for Maximum-Likelihood Estimators in Time-of-Arrival-Based Localization. IEEE SIGNAL PROCESSING LETTERS. IEEE - Institute of Electrical and Electronic Engineers. 2018, Vol. 25, Issue 9. DOI: 10.1109/LSP.2018.2855567
  10. Selvaratnam D, Shames I, Dimarogonas DV, Manton J, Ristic B. Co-operative Estimation for Source Localisation using Binary Sensors. 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). Institute of Electrical and Electronics Engineers. 2017.
  11. Chatelain F, Le Bihan N, Manton J. Density Estimation for Compound Cox Processes on Hyperspheres. 3rd International SEE Conference on Geometric Science of Information (GSI). Springer Verlag. 2017, Vol. 10589. Editors: Nielsen F, Barbaresco F. DOI: 10.1007/978-3-319-68445-1_79
  12. Khatibi S, Zhu H, Wagner J, Tan C, Manton J, Burgess A. Mathematical model of TGF-beta signalling: feedback coupling is consistent with signal switching. BMC SYSTEMS BIOLOGY. Biomed Central. 2017, Vol. 11, Issue 1. DOI: 10.1186/s12918-017-0421-5
  14. Manton J, Le Bihan N. ON SOME GLOBAL TOPOLOGICAL ASPECTS OF MANIFOLD LEARNING. 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP). Institute of Electrical and Electronics Engineers. 2017, Vol. 2017-September. DOI: 10.1109/ICIP.2017.8296276
  15. Pauley M, Manton J. Optimisation Geometry and Its Implications for Optimisation Algorithms. 2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP). IEEE. 2017.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile