Turing Fellow Project Presentations 2021

The session will be delivered online and covers the following research areas:

• Data-centric Engineering / Statistics
• Experimental Psychology
• Urban Analytics

Fellows presenting during this virtual session will also share how being a TF has benefited their research to the Turing community and other researchers. There will be an opportunity to ask questions after each presentation. Please sign up via the link provided.

Speakers 

Professor Serge Guillas: Uncertainty quantification of multi-scale and multi-physics computer models

What possible tsunamis will hit the west coast of India? What are the uncertainties in future predictions of global warming over Europe? 'Uncertainty Quantification' (UQ) can help answer these questions by analysing the propagation of uncertainties in complex simulators, such as climate or tsunami computer models that run on supercomputers and require multiple scales and physical processes to be combined. Typically UQ makes use of surrogate models that are much faster to run than simulators, in order to sample uncertainties efficiently. These are often 'Gaussian processes' that need to be fitted using a smart design of computer experiments. Building a UQ workflow that integrates heterogeneous models (both in scale and in nature) is a challenge, and the corresponding designs also have to be investigated. We describe in this talk the range of statistical and computational advances, as well as impact and collaborations that resulted from this project, along with future steps ahead.

Professor Brad Love: Large-scale embeddings from human behaviour

How do people think about everyday objects, whether they be products in the supermarket or natural images? We find that people organise everyday objects around goals (e.g., items needed to cook a stir-fry). Moreover, we derive an image embedding (size 50k) of the ImageNet validation set and find that recent deep learning models organise the images in a manner at odds with human judgment.

Steven Gray: nAvIgate - understanding urban navigation through Deep Reinforcement Learning

nAvIgate aims to further our understanding of urban navigation by training agents to navigate simulated cities via deep reinforcement learning. As well as being helpful in understanding human navigation, development of agents that can robustly navigate urban spaces is useful in a number of different areas, such as agent-based models for urban simulation, robotics, and video-games. In this presentation we share our findings from the nAvIgate project, and the challenges we encountered in applying deep reinforcement learning to urban navigation. We will also talk about how we used existing tools to streamline the creation of our environment and creation of our agent and the next steps of the project.