Machine Learning Lab
The Machine Learning Laboratory, was launched on the 22nd March 2018.
The vision of the Machine Learning Lab is to develop autonomous decision-making systems, which close the perception-action-learning loop while learning from small amounts of data.
Therefore, the laboratory aims to promote and lead scientific advances in data-efficient machine learning, i.e., the ability to learn in complex domains without requiring large quantities of data. Research areas that fall into this category include probabilistic modelling, incorporation of domain or structural prior knowledge, transfer learning, semi-supervised learning, active learning, Bayesian optimization and reinforcement learning.
To support the launch of the new Machine Learning Lab, the DSI announced a £100k seed fund for pilot projects in the area of probabilistic modelling for DSI Fellows.
We were delighted with the response we got and are pleased to announce the four projects that have been awarded initial funding for 5 months, to foster basic research in Machine Learning.
The four project funded are:
|The project entitled “A fully probabilistic approach to infer intra-urban air quality from limited monitoring stations using Bayesian nonparametrics”, is led by Dr Ke Han from the Department of Civil and Environmental Engineering in collaboration with Dr Shahram Heydari and Dr Audrey de Nazelle - both from the Centre for Environmental Policy.|
|Dr Marc Deisenroth has been awarded for his project on “Distributional robust adversarial training for natural language processing”.|
|Dr Ruth Misener has received funding for her work on “Dynamic Design of Experiments for Model Discrimination”.|
The fourth project to receive funding is led by Professor Simon Schultz, from the Department of Bioengineering, working jointly with Dr Seth Flaxman (Department of Mathematics) and Dr Stephen Brickley (Department of Life Sciences). They will work on “Developing machine learning approaches to reveal changes in whole-brain connectivity during ageing and neurodegeneration”.
To learn more about the winners and their research read Anna Cupani's article.
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