Dr Hao Ni

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Dr Hao Ni

Former position

Turing Fellow

Partner Institution

UCL

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Research areas

Algorithms Complexity (Algorithms) Numerical (Algorithms) Statistical methods & theory Time series Probability Simulation Modelling (Statistical methods & theory) Non-parametric & semi-parametric methods High dimensional inference Theoretical mathematics Calculus & analysis Computer systems & architectures Human computer interface Artificial intelligence Neural networks Nonlinear dynamics Applied mathematics Dynamical systems & differential equations Numerical analysis Mathematical modelling Stochastic (Mathematical modelling) Dynamic/static (Mathematical modelling) Machine learning Reinforcement learning Supervised learning Speech recognition Natural language processing Deep learning Computer vision Applications (Machine learning)

Bio

Dr Hao Ni is a senior lecturer in financial mathematics at UCL since September 2016. Prior to this she was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from 2012/09 to 2013/05 and continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. She finished her D.Phil. in mathematics in 2012 under the supervision of Professor Terry Lyons at University of Oxford.

Research interests

Her research interests include stochastic analysis, financial mathematics and machine learning. More specifically she is interested in non-parametric modelling effects of data streams through rough paths theory and statistical models. Rough paths theory is a non-linear extension of classical theory of control differential equations to model highly oscillatory systems, and the core concept in rough paths theory is the signature of a path, which can be used as useful features for learning to summarize sequential data in terms of its effect. Moreover, she is also interested in its applications, e.g. online Chinese handwritten character and financial data streams.

Turing Events

Turing Research



Turing Publications

Research publications

Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative...

Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images, Yang, G., Chen J., Gao, Z, Zhang, H., Ni, H. Angelini, E., Mohiaddin R., Wong T., Keegan, J., Firmin, D.; Accepted by 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018.