Machine Learning Lab

The Machine Learning Lab headed by Dr Marc Deisenroth was launched on the 22nd March.

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 Machine Learning Lab 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 at the DSI in March, the DSI announced a £100k Seed Fund for pilot projects in the area of Probabilistic Modelling for members of the DSI Fellows scheme.  We were delighted with the response 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 winners are:

 Ke Han The first project awarded, “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. 


Marc Deisenroth

 Dr Marc Deisenroth has been awarded for his project on “Distributional robust adversarial training for natural language processing”.


Ruth Misener

Dr Ruth Misener  has received funding for her work on “Dynamic Design of Experiments for Model Discrimination”

Simon Schultz

The fourth project to receive funding is led by Prof 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 read Anna Cupani's article


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