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• Background
• Tutorial and Review Articles
• Current Priority Areas
  • Optimisation
  • Pulse Processing
  • Machine Learning
  • Time-series Prediction
• Selected Contributions
  • Geometric Signal Processing
  • Networked and Distributed Systems
  • Channel Identification and Affine Precoders
  • Statistical Signal Processing and Stochastic Filtering
  • Systems Biology
  • Neuroscience
  • Robust Stability

Although we work on a broad range of projects, including transdisciplinary projects with neuroscientists and biologists, our current research priority areas are Optimisation, Pulse Processing, Machine Learning and Time-series Prediction.

The common theme in our research is the application of mathematics and statistics to the study of systems and networks of systems. Whether these systems are living, social or engineered, the same fundamental principles apply.

The theoretical foundations underpinning our research often come from the following disciplines:

• Mathematical Systems Theory, including Signal Processing and Optimisation
• Geometry and Topology, both Differential and Algebraic
• Learning and Computation, including Systems Biology, Neuroscience and Machine Learning.

• Manton, J. H., Groves, J. R. J. and Hua, Y. (2000). On Properties of the Solutions of Systems of Polynomial Equations. Third Asian Control Conference, Shanghai, China, 6 pages.
• Moran, W. and Manton, J. H. (2004). Group Theory in Radar and Signal Processing. In Computational Noncommutative Algebra and Applications, Kluwer Academic Publishers. pp. 339–362.
• Su, X., Chan, S. and Manton, J. H. (2010). Bandwidth Allocation in Wireless Ad Hoc Networks: Challenges and Prospects. IEEE Communications Magazine, 48(1), pp. 80–85.
• McDonnell, M. D., Ikeda, S. and Manton, J. H. (2011). An Introductory Review of Information Theory in the Context of Computational Neuroscience. Biological Cybernetics, 105(1), pp. 55–70.
• Manton, J. H. (2013). A Primer on Stochastic Differential Geometry for Signal Processing. Journal of Selected Topics in Signal Processing, 7(4), pp. 681–699.
• Manton, J. H. and Amblard, P.-O. (2015). A Primer on Reproducing Kernel Hilbert Spaces. Now Foundations and Trends, 138 pages.
• Manton, J. H. (2020). Geometry, Manifolds and Nonconvex Optimization: How Geometry Can Help Optimization. IEEE Signal Processing Magazine, 37(5), pp. 109–119.

Optimisation

We are interested in fast (online) optimisation problems with guaranteed performance. In 2013 we proposed an Optimisation Geometry framework for such problems. We are now looking to apply this framework to specific problems. We are also pursuing the hypothesis that the nicest optimisation problems are not convex ones but certain classes of problems on compact manifolds.

• Manton, J. H. (2002). Optimisation Algorithms Exploiting Unitary Constraints. IEEE Transactions on Signal Processing, 50(3), pp. 635–650.
• Manton, J. H. (2004). On the Various Generalisations of Optimisation Algorithms to Manifolds. Sixteenth International Symposium on Mathematical Theory of Networks and Systems, Katholieke Universiteit Leuven, Belgium, 6 pages.
• Manton, J. H. (2004). A Globally Convergent Numerical Algorithm for Computing the Centre of Mass on Compact Lie Groups. Eighth International Conference on Control, Automation, Robotics and Vision, Kunming, China, 6 pages.
• Brace, I. and Manton, J. H. (2006). An Improved BFGS-on-Manifold Algorithm for Computing Weighted Low Rank Approximations. Seventeenth International Symposium on Mathematical Theory of Networks and Systems, Kyoto, Japan, 4 pages.
• Trumpf, J., Manton, J. and Mahony, R. (2006). Curvature Based Updates for Optimization Problems on a Hypersurface. Seventeenth International Symposium on Mathematical Theory of Networks and Systems, Kyoto, Japan, 4 pages.
• Krakowski, K. A., Huper, K. and Manton, J. H. (2007). On the Computation of the Karcher Mean on Spheres and Special Orthogonal Groups. Workshop on Robotics and Mathematics (RoboMat), Coimbra, Portugal, 6 pages.
• Ji, H., Huang, M., Moore, J. B. and Manton, J. H. (2007). A Globally Convergent Conjugate Gradient Method for Minimizing Self-Concordant Functions with Application to Constrained Optimisation Problems. American Control Conference, New York, USA, 6 pages.
• Ji, H., Manton, J. H. and Moore, J. B. (2008). A Globally Convergent Conjugate Gradient Method for Minimizing Self-Concordant Functions on Riemannian Manifolds. 17th IFAC World Congress, Seoul, Korea, 6 pages.
• Lageman, C., Helmke, U. and Manton, J. H. (2008). The nonsmooth Newton method on Riemannian manifolds. 18th International Symposium on Mathematical Theory of Networks and Systems, Virginia, USA, 8 pages.
• Manton, J. H. (2013). Optimisation Geometry. In Mathematical System Theory — Festschrift in Honor of Uwe Helmke on the Occasion of his Sixtieth Birthday, CreateSpace. pp. 261–274.
• Manton, J. H. (2015). A Framework for Generalising the Newton Method and Other Iterative Methods from Euclidean Space to Manifolds. Numerische Mathematik, 129(1), pp. 91–125.
• Pauley, M. and Manton, J. H. (2017). Optimisation Geometry and its Implications for Optimisation Algorithms. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, 4 pages.
• Pauley, M. and Manton, J. H. (2018). Global Optimisation for Time of Arrival-Based Localisation. IEEE Statistical Signal Processing Workshop (SSP), Freiburg, Germany, 5 pages.
• Selvaratnam, D. D., Shames, I., Manton, J. H. and Zamani, M. (2018). Numerical Optimisation of Time-Varying Strongly Convex Functions Subject to Time-Varying Constraints. IEEE Conference on Decision and Control (CDC), Miami Beach, Florida, USA, 6 pages.
• Said, S. and Manton, J. H. (2019). The Riemannian Barycentre as a Proxy for Global Optimisation. Geometric Science of Information, Toulouse, France, 8 pages.
• Shames, I., Selvaratnam, D. and Manton, J. H. (2019). Online Optimization Using Zeroth Order Oracles. IEEE Control Systems Letters, 4(1), pp. 31–36.
• Lefevre, J., Bouchard, F., Said, S., Le Bihan, N. and Manton, J. H. (2021). On Riemannian and non-Riemannian Optimisation, and Optimisation Geometry. 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020), Cambridge, UK, 6 pages.
• Lefevre, J., Le Bihan, N. and Manton, J. H. (2020). An Optimisation Geometry Framework for the Rayleigh Quotient. 28th European Signal Processing Conference (EUSIPCO 2020), Amsterdam, The Netherlands, 5 pages.
• Manton, J. H. (2020). Geometry, Manifolds and Nonconvex Optimization: How Geometry Can Help Optimization. IEEE Signal Processing Magazine, 37(5), pp. 109–119.
• Said, S., Le Bihan, N. and Manton, J. H. (2021). From Bayesian Inference to MCMC and Convex Optimisation in Hadamard Manifolds. International Conference on Geometric Science of Information, Paris, France, 9 pages.
• Maass, A. I., Manzie, C., Nesic, D., Manton, J. H. and Shames, I. (2022). Tracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation. SIAM Journal on Optimization.

Pulse Processing

Identifying the locations and amplitudes of slowly-decaying pulses in a noisy signal is difficult at high pulse-generation rates because two pulses sufficiently close together are indistinguishable from a single pulse. We are interested in deriving faster and more accurate algorithms as well as understanding theoretical limitations that must apply to all pulse-processing algorithms.

• McLean, C., Pauley, M. and Manton, J. H. (2017). Performance Metrics for Energy Spectrum Estimation. IEEE GlobalSIP General Symposium, Montreal, Canada, 5 pages.
• Manton, J. H., McLean, C. and Scoullar, P. (2018). Method and Apparatus for Identifying Pulses in Detector Output Data. US Patent Office.
• McLean, C., Pauley, M. and Manton, J. H. (2018). Limitations of Decision Based Pile-up Correction Algorithms. IEEE Statistical Signal Processing Workshop (SSP), Freiburg, Germany, 5 pages.
• McLean, C., Pauley, M. and Manton, J. H. (2020). Non-parametric Decompounding of Pulse Pile-up Under Gaussian Noise With Finite Data Sets. IEEE Transactions on Signal Processing, 68(1), pp. 2114–2127.

Machine Learning

We are interested in analysing neural networks from a mathematical perspective using tools from both information theory and analysis. We are also interested in how to impose certain geometric constraints on a neural network, such as invariance or equivariance.

• Manton, J. H. and Amblard, P.-O. (2015). A Primer on Reproducing Kernel Hilbert Spaces. Now Foundations and Trends, 138 pages.
• Lu, J., Kazmierczak, E., Manton, J. H. and Sinclair, R. (2015). Machine Learning Methods for Segmenting Psoriatic Lesions from 2D Images. In Frontiers of Medical Imaging, World Scientific. pp. 121–149.
• Manton, J. H. and Le Bihan, N. (2017). On some Global Topological Aspects of Manifold Learning. IEEE International Conference on Image Processing, Beijing, China, 5 pages.
• Xu, Z., Sun, C., Ji, T., Manton, J. H. and Shieh, W. (2019). Computational Complexity Comparison of Feedforward/Radial Basis Function/Recurrent Neural Network-based Equalizer for a 50-Gb/s PAM4 Direct-detection Optical Link. Optics Express, 27(25), pp. 36953–36964.
• Chakraborty, R., Bouza, J., Manton, J. and Vemuri, B. C. (2019). A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging. International Conference on Information Processing in Medical Imaging, The Hong Kong University of Science and Technology, Hong Kong, 13 pages.
• Wu, X., Manton, J. H., Aickelin, U. and Zhu, J. (2020). Information-theoretic Analysis for Transfer Learning. IEEE International Symposium on Information Theory, Los Angeles, California, USA, 6 pages.
• Xu, Z., Sun, C., Ji, T., Manton, J. H. and Shieh, W. (2020). Cascade Recurrent Neural Network-Assisted Nonlinear Equalization for a 100 Gb/s PAM4 Short-Reach Direct Detection System. Optics Letters, 45(15), pp. 4216–4219.
• Xu, Z., Sun, C., Ji, T., Manton, J. H. and Shieh, W. (2020). Feedforward and Recurrent Neural Network-Based Transfer Learning for Nonlinear Equalization in Short-Reach Optical Links. Journal of Lightwave Technology, 39(2), pp. 475–480.
• Wu, X., Manton, J. H., Aickelin, U. and Zhu, J. (2021). Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge. IEEE International Symposium on Information Theory, Melbourne, Australia, 6 pages.
• Xu, Z., Sun, C., Manton, J. H. and Shieh, W. (2021). Joint Equalization of Linear and Nonlinear Impairments for PAM4 Short-Reach Direct Detection Systems. IEEE Photonics Technology Letters, 33(9), pp. 425–428.
• Xu, Z., Dong, S., Manton, J. H. and Shieh, W. (2022). Low-Complexity Multi-Task Learning Aided Neural Networks for Equalization in Short-Reach Optical Interconnects. Journal of Lightwave Technology, 40(1), pp. 45–54.
• Chakraborty, R., Bouza, J., Manton, J. H. and Vemuri, B. C. (2022). ManifoldNet: A Deep Neural Network for Manifold-valued Data with Applications. Transactions on Pattern Analysis and Machine Intelligence, 44(2), pp. 799–810.

Time-series Prediction

Building on our background in Statistical Signal Processing and Stochastic Filtering, we wish to return to a topic we looked briefly at over two decades ago: financial time-series analysis. We are interested in understanding how much information can be extracted from past observations, and how best to use past observations to determine the best course of action.

• Manton, J. H., Muscatelli, A., Krishnamurthy, V. and Hurn, S. (1998). Modelling Stock Market Excess Returns by Markov Modulated Gaussian Noise. The University of Glasgow, 9806. 13 pages.

Geometric Signal Processing

• Mahony, R. and Manton, J. H. (2002). The Geometry of the Newton Method on Non-Compact Lie Groups. Journal of Global Optimization, 23(3), pp. 309–327.
• Manton, J. H. (2002). The Convex Geometry of Sub-Channel Attenuation Coefficients in Linearly Precoded OFDM Systems. IEEE Transactions on Information Theory, 48(5), pp. 1203–1206.
• Nikpour, M., Manton, J. H. and Hori, G. (2002). Algorithms on the Stiefel Manifold for Joint Diagonalisation. IEEE International Conference on Acoustics, Speech and Signal Processing, Orlando, Florida, 4 pages.
• Manton, J. H., Mahony, R. and Hua, Y. (2003). The Geometry of Weighted Low Rank Approximations. IEEE Transactions on Signal Processing, 51(2), pp. 500–514.
• Manton, J. H. (2005). On the Role of Differential Geometry in Signal Processing. International Conference on Acoustics, Speech and Signal Processing, Philadelphia, USA, 4 pages.
• Manton, J. H. (2006). A Centroid (Karcher Mean) Approach to the Joint Approximate Diagonalisation Problem: The Real Symmetric Case. Digital Signal Processing, 16(5), pp. 468–478.
• Said, S., Le Bihan, N., Lageman, C. and Manton, J. H. (2010). Decompounding on Compact Lie Groups. IEEE Transactions on Information Theory, 56(6), pp. 2766–2777.
• Said, S. and Manton, J. H. (2012). Extrinsic Mean of Brownian Distributions on Compact Lie Groups. IEEE Transactions on Information Theory, 58(6), pp. 3521–3535.
• Said, S. and Manton, J. H. (2012). Brownian Processes for Monte Carlo Integration on Compact Lie Groups. Stochastic Analysis and Applications, 30(6), pp. 1062–1082.
• Manton, J. H. (2013). A Primer on Stochastic Differential Geometry for Signal Processing. Journal of Selected Topics in Signal Processing, 7(4), pp. 681–699.
• Manton, J. H. and Amblard, P.-O. (2015). A Primer on Reproducing Kernel Hilbert Spaces. Now Foundations and Trends, 138 pages.
• Flamant, J., Le Bihan, N., Martin, A. V. and Manton. J. H. (2016). Expansion-maximization-compression Algorithm with Spherical Harmonics for Single Particle Imaging with X-ray Lasers. Physical Review E, 93(5), 14 pages.
• Le Bihan, N., Chatelain, F. and Manton, J. H. (2016). Isotropic Multiple Scattering Processes on Hyperspheres. IEEE Transactions on Information Theory, 62(10), pp. 5740–5752.
• Le Bihan, N., Flamant, J. and Manton, J. H. (2016). Density Estimation on the Rotation Group using Diffusive Wavelets. Journal of Advances in Information Fusion, 11(2), pp. 173–185.
• Said, S., Bombrun, L., Berthoumieu, Y. and Manton, J. H. (2017). Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices. IEEE Transactions on Information Theory, 63(4), pp. 2153–2170.
• Said, S., Le Bihan, N. and Manton, J. H. (2021). Hidden Markov Chains and Fields With Observations in Riemannian Manifolds. 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020), Cambridge, UK, 6 pages.
• Said, S. and Manton, J. H. (2021). Riemannian Barycenters of Gibbs Distributions: New Results on Concentration and Convexity. Information Geometry, 4, pp. 329–362.
• Manton, J. H. (2020). Geometry, Manifolds and Nonconvex Optimization: How Geometry Can Help Optimization. IEEE Signal Processing Magazine, 37(5), pp. 109–119.
• Said, S., Le Bihan, N. and Manton, J. H. (2021). From Bayesian Inference to MCMC and Convex Optimisation in Hadamard Manifolds. International Conference on Geometric Science of Information, Paris, France, 9 pages.
• Maass, A. I., Manzie, C., Nesic, D., Manton, J. H. and Shames, I. (2022). Tracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation. SIAM Journal on Optimization.

Networked and Distributed Systems

• Huang, M. and Manton, J. H. (2007). Stochastic Approximation for Consensus Seeking: Mean Square and Almost Sure Convergence. IEEE Conference on Decision and Control, Louisiana, USA, 6 pages.
• Huang, M. and Manton, J. H. (2007). Stochastic Lyapunov Analysis for Consensus Algorithms with Noisy Measurements. American Control Conference, New York, USA, 6 pages.
• Huang, M. and Manton, J. H. (2007). Stochastic Double Array Analysis and Convergence of Consensus Algorithms with Noisy Measurements. American Control Conference, New York, USA, 6 pages.
• Huang, M. and Manton, J. H. (2008). Stochastic Consensus Seeking with Measurement Noise: Convergence and Asymptotic Normality. American Control Conference, Seattle, Washington, USA, 6 pages.
• Huang, M. and Manton, J. H. (2009). Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior. SIAM Journal on Control and Optimization, 48(1), pp. 134–161.
• Li, L., Li, X., Scaglione, A. and Manton, J. H. (2010). Decentralized Subspace Tracking via Gossiping. In Distributed Computing in Sensor Systems, Springer. pp. 130–143.
• Huang, M. and Manton, J. H. (2010). Stochastic Consensus Seeking with Noisy and Directed Inter-Agent Communication: Fixed and Randomly Varying Topologies. IEEE Transactions on Automatic Control, 55(1), pp. 235–241.
• Huang, M., Dey, S., Nair, G. N. and Manton, J. H. (2010). Stochastic Consensus over Noisy Networks with Markovian and Arbitrary Switches. Automatica, 46(10), pp. 1571–1583.
• Li, L., Scaglione, A. and Manton, J. H. (2011). Distributed Principal Subspace Estimation in Wireless Sensor Networks. Journal of Selected Topics in Signal Processing, 5(4), pp. 725–738.

Channel Identification and Affine Precoders

• Manton, J. H. and Hua, Y. (1999). A Frequency Domain Deterministic Approach to Channel Identification. IEEE Signal Processing Letters, 6(12), pp. 323–326.
• Manton, J. H., Mareels, I. M. Y. and Hua, Y. (2000). Affine Precoders for Reliable Communications. IEEE International Conference on Acoustics, Speech and Signal Processing, Istanbul, Turkey, 4 pages.
• Manton, J. H. and Hua, Y. (2001). Robust Frequency Domain Precoders. IEEE Communications Letters, 5(2), pp. 40–42.
• Manton, J. H. (2001). Optimal Training Sequences and Pilot Tones for OFDM Systems. IEEE Communications Letters, 5(4), pp. 151–153.
• Manton, J. H. (2001). Dissecting OFDM: The Independent Roles of the Cyclic Prefix and the IDFT Operation. IEEE Communications Letters, 5(12), pp. 474–476.
• Manton, J. H. (2002). An Improved Least Squares Blind Channel Identification Algorithm for Linearly and Affinely Precoded Communication Systems. IEEE Signal Processing Letters, 9(9), pp. 282–285.
• Manton, J. H. (2002). Finite Alphabet Source Recovery in Polynomial Systems. Systems and Control Letters, 47(4), pp. 279–286.
• Manton, J. H. (2002). An OFDM Interpretation of Zero Padded Block Transmissions. Systems and Control Letters, 47(5), pp. 393–399.
• Manton, J. H. and Neumann, W. D. (2003). Totally Blind Channel Identification By Exploiting Guard Intervals. Systems and Control Letters, 48(2), pp. 113–119.
• Manton, J. H. (2003). Design and Analysis of Linear Precoders Under A Mean Square Error Criterion, Part I: Foundations and Worst Case Designs. Systems and Control Letters, 49(2), pp. 121–130.
• Manton, J. H. (2003). Design and Analysis of Linear Precoders Under A Mean Square Error Criterion, Part II: MMSE Designs and Conclusions. Systems and Control Letters, 49(2), pp. 131–140.
• Pham, D. H. and Manton, J. H. (2003). A Subspace Algorithm for Guard Interval Based Channel Identification and Source Recovery Requiring Just Two Received Blocks. IEEE International Conference on Acoustics, Speech and Signal Processing, Hong Kong, 4 pages.
• Rahbar, K., Reilly, J. P. and Manton, J. H. (2004). Blind Identification of MIMO FIR Systems Driven by Quasi-Stationary Sources Using Second Order Statistics: A Frequency Domain Approach. IEEE Transactions on Signal Processing, 52(2), pp. 406–417.
• Shahbazpanahi, S., Gershman, A. B. and Manton, J. H. (2004). Closed-Form Blind Decoding of Orthogonal Space-Time Block Codes. IEEE International Conference on Acoustics, Speech and Signal Processing, Montreal, Canada, 4 pages.
• Manton, J. H., Neumann, W. D. and Norbury, P. T. (2005). On the Algebraic Identifiability of Finite Impulse Response Channels Driven by Linearly Precoded Signals. Systems and Control Letters, 54(2), pp. 125–134.
• An, S., Manton, J. H. and Hua, Y. (2005). A Sequential Subspace Method for Blind Identification of General FIR MIMO Channels. IEEE Transactions on Signal Processing, 53(10), pp. 3906–3910.
• Shahbazpanahi, S., Gershman, A. B. and Manton, J. H. (2005). Closed-Form Blind MIMO Channel Estimation for Orthogonal Space-Time Block Codes. IEEE Transactions on Signal Processing, 53(12), pp. 4506–4517.
• An, S., Hua, Y., Manton, J. H. and Fang, Z. (2005). Group Decorrelation Enhanced Subspace Method for Identifying FIR MIMO Channels Driven by Unknown Uncorrelated Colored Sources. IEEE Transactions on Signal Processing, 53(12), pp. 4429–4441.
• Sun, H., Samra, H., Ding, Z. and Manton, J. H. (2005). Constrained Capacity of Linear Precoded ARQ in MIMO Wireless Systems. IEEE International Conference on Acoustics, Speech and Signal Processing, Philadelphia, USA, 4 pages.
• Pham, D. H. and Manton, J. H. (2005). Orthogonal Superimposed Training on Linear Precoding: A New Affine Precoder Design. IEEE International Workshop on Signal Processing Advances in Wireless Communications, New York, USA, 5 pages.
• Varma, A. R., Athaudage, C., Andrew, L. L. H. and Manton, J. H. (2006). Optimal Superimposed Pilot Training for OFDM Channel Estimation. Signal Processing Advances in Wireless Communications, Cannes, France, 5 pages.
• Sun, H., Manton, J. H. and Ding, Z. (2006). Progressive Linear Precoder Optimization for MIMO Packet Retransmissions. IEEE Journal on Selected Areas in Communications, 24(3), pp. 448–456.
• Zia, A., Reilly, J. P., Manton, J. and Shirani, S. (2007). An Information Geometric Approach to ML Estimation With Incomplete Data: Application to Semi-Blind MIMO Channel Identification. IEEE Transactions on Signal Processing, 55(8), pp. 3975–3986.
• Sun, H., Shi, Z., Zhao, C., Manton, J. H. and Ding, Z. (2008). Progressive Linear Precoder Optimization for MIMO Packet Retransmissions Exploiting Channel Covariance Information. IEEE Transactions on Communications, 56(5), pp. 818–827.
• Wang, S. and Manton, J. H. (2009). Blind Channel Estimation for Non-CP OFDM Systems Using Multiple Receive Antennas. IEEE Signal Processing Letters, 16(4), pp. 299–302.

Statistical Signal Processing and Stochastic Filtering

• Manton, J. H., Krishnamurthy, V. and Poor, H. V. (1998). James-Stein State Filtering Algorithms. IEEE Transactions on Signal Processing, 46(9), pp. 2431–2447.
• Manton, J. H., Krishnamurthy, V. and Elliott, R. J. (1999). Discrete Time Filters for Doubly Stochastic Poisson Processes and Other Exponential Noise Models. International Journal of Adaptive Control and Signal Processing, 13(5), pp. 393–416.
• Manton, J. H. and Hua, Y. (1999). Rank Reduction and James-Stein Estimation. IEEE Transactions on Signal Processing, 47(11), pp. 3121–3125.
• Abed-Meraim, K., Xiang, Y., Manton, J. H. and Hua, Y. (2001). Blind Source Separation Using Second-Order Cyclostationary Statistics. IEEE Transactions on Signal Processing, 49(4), pp. 694–701.
• Manton, J. H. and Hua, Y. (2001). Convolutive Reduced Rank Wiener Filtering. IEEE International Conference on Acoustics, Speech and Signal Processing, Salt Lake City, Utah, 4 pages.
• Manton, J. H. and Vo, B.-N. (2001). Fisher Information Decision Directed Quantisation: A Fast Sub-Optimal Solution to Discrete Optimisation Problems with Applications in Wireless Communications. International Conference on Optimisation Theory and Applications, Hong Kong, 8 pages.
• Brace, I. and Manton, J. H. (2004). Fisher Information Decision Directed Discrete Optimisation. IEEE International Conference on Acoustics, Speech and Signal Processing, Montreal, Canada, 4 pages.
• Said, S. and Manton, J. H. (2013). On Filtering with Observation in a Manifold: Reduction to a Classical Filtering Problem. SIAM Journal on Control and Optimization, 51(1), pp. 767–783.
• Said, S., Amblard, P. O. and Manton, J. H. (2013). Stationary Random Fields Arising from Second Order Partial Differential Equations on Compact Lie Groups. IEEE Transactions on Information Theory, 59(6), pp. 3497–3513.
• Manton, J. H. (2013). A Primer on Stochastic Differential Geometry for Signal Processing. Journal of Selected Topics in Signal Processing, 7(4), pp. 681–699.
• Said, S. and Manton, J. H. (2014). Asymptotic Properties of Maximum Likelihood Estimation: Parametrised Diffusion in a Manifold. Stochastic Analysis and Applications, 32(2), pp. 298–327.
• Said, S. and Manton, J. H. (2015). Particle Filtering with Observations in a Manifold. IEEE International Conference on Acoustics, Speech, and Signal Processing, Brisbane, Australia, 5 pages.
• Boulanger, J., Said, S., Le Bihan, N. and Manton, J. H. (2016). Filtering from Observations on Stiefel Manifolds. Signal Processing, 122, pp. 52–64.
• Said, S. and Manton, J. H. (2016). Particle Filtering with Observations in a Manifold: A Proof of Convergence and Two Worked Examples. Journal of Advances in Information Fusion, 11(2), pp. 227–249.
• Chatelain, F., Le Bihan, N. and Manton, J. H. (2017). Density Estimation for Compound Cox Processes on Hyperspheres. International Conference on Geometric Science of Information, Paris, France, 8 pages.
• Pauley, M., McLean, C. and Manton, J. H. (2020). A Numerical Filtering Method for Linear State-space Models with Markov Switching. International Journal of Adaptive Control and Signal Processing, 34(7), pp. 813–838.

Systems Biology

• Zaid, A., Mackay, L. K., Rahimpour, A., Braun, A., Veldoen, M., Carbone, F. R., Manton, J. H., Heath, W. R. and Mueller, S. N. (2014). Persistence of Skin Resident Memory T Cells within an Epidermal Niche. PNAS (Proceedings of the National Academy of Sciences of the United States of America), 111(14), pp. 5307–5312.
• Fernandez-Ruiz, D., Ng, W. Y., Holz, L. E., Ma, J. Z., Zaid, A., Wong, Y. C., Lau, L. S., Mollard, V., Cozijnsen, A., Collins, N., Li, J., Davey, G. M., Kato, Y., Devi, S., Skandari, R., Pauley, M., Manton, J. H., Godfrey, D. I., Braun, A., Tay, S. S., Tan, P. S., Bowen, D. G., Koch-Nolte, F., Rissiek, B., Carbone, F. R., Crabb, B. S., Lahoud, M., Cockburn, I. A., Mueller, S. N., Bertolino, P., McFadden, G. I., Caminschi, I. and Heath, W. R. (2016). Liver-Resident Memory CD8⁺ T Cells Form a Front-Line Defense against Malaria Liver-Stage Infection. Immunology, 45(4), pp. 889–902.
• Khatibi, S., Zhu, H.-J., Wagner, J., Tan, C. W., Manton, J. H. and Burgess, A. W. (2017). Mathematical Model of TGF-β signalling: Feedback Coupling Is Consistent with Signal Switching. BMC Systems Biology, 11(48), pp. 1–15.
• Khatibi, S., Babon, J., Wagner, J., Manton, J. H., Tan, C. W., Zhu, H.-J., Wormald, S. and Burgess, A. W. (2017). TGF-β and IL-6 Family Signalling Crosstalk: an Integrated Model. Growth Factors, 35(2-3), pp. 100–124.
• Kato, Y., Steiner, T. M., Park, H., Hitchcock, R. O., Zaid, Al., Hor, J. L., Devi, S., Davey, G. M., Vremec, D., Tullett, K. M., Tan, P. S., Ahmet, F., Mueller, S. N., Alonso, S., Tarlinton, D. M., Ploegh, H. L., Kaisho, T., Beattie, L., Manton, J. H., Fernandez-Ruiz, D., Shortman, K., Lahoud, M. H., Heath, W. R. and Caminschi, I. (2020). Display of Native Antigen on cDC1 That Have Spatial Access to Both T and B Cells Underlies Efficient Humoral Vaccination. The Journal of Immunology, 205(7), pp. 1842–1856.

Neuroscience

• Ikeda, S. and Manton, J. H. (2009). Capacity of a Single Spiking Neuron Channel. Neural Computation, 21(6), pp. 1714–1748.
• Hall, L. T., Beart, G. C. G., Thomas, E., Simpson, D. A., McGuinness, L. P., Cole, J. H., Manton, J. H., Scholten, R. E., Jelezko, F., Wrachtrup, J., Petrou, S. and Hollenberg, L. C. L. (2012). High Spatial and Temporal Resolution Wide-field Imaging of Neuron Activity Using Quantum NV-diamond. Scientific Reports, 2(401), 9 pages.
• Kuhlmann, L., Hauser-Raspe, M., Manton, J. H., Grayden, D. B., Tapson, J. and Van Schaik, A. (2014). Approximate, Computationally Efficient Online Learning in Bayesian Spiking Neurons. Neural Computation, 26, pp. 472–496.
• Skandari, R., Le Bihan, N. and Manton, J. H. (2015). C. Elegans Locomotion Analysis using Algorithmic Information Theory. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milano, Italy, 4 pages.
• Skandari, R., Iino, Y. and Manton. J. H. (2016). On an Analogue Signal Processing Circuit in the Nematode C. elegans. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, USA, 4 pages.
• Kuhlmann, L., Freestone, D. R., Manton, J. H., Heyse, B., Vereecke, H. E., Lipping, T., Struys, M. M. and Liley, D. T. (2016). Neural Mass Model-based Tracking of Anesthetic Brain States. Neuroimage, 133, pp. 438–456.
• Kuhlmann, L., Manton, J. H., Heyse, B., Vereecke, H. E. M., Lipping, T., Struys, M. M. R. F. and Liley, D. T. J. (2017). Tracking Electroencephalographic Changes using Distributions of Linear Models: Application to Propofol-based Depth of Anesthesia Monitoring. IEEE Transactions on Biomedical Engineering, 64(4), pp. 870–881.
• Tolmachev, P., Dhingra, R. R., Pauley, M., Dutschmann, M. and Manton, J. H. (2018). Modeling the Respiratory Central Pattern Generator with Resonate-and-Fire Izhikevich Neurons. International Conference on Neural Information Processing, Siem Reap, Cambodia, 13 pages.

Robust Stability

• An, S., Liu, W. and Manton, J. H. (2006). Extreme Point Results for Robust Stability of Nested Polynomial Families with Real Coefficients. International Journal on Statistics and Systems, 1(1), pp. 29–55.