Close

We use cookies to improve your experience of our website. Privacy Policy

Skip to main content

Institute of Applied Data Science

Search
Menu

Events

Queen Mary University of London Turing Fellow Project Presentations Showcase

Image:

Date: 16 September 2021   Time: 10:00 - 12:00


Queen Mary University of London Turing Fellow Project Presentations Showcase

Queen Mary University of London Institute of Applied Data Science cordially invites you to attend our Turing Fellow Project Presentations Showcase Event. This event will showcase successes and research outputs from The Alan Turing Institute funded projects, led by Queen Mary University of London Turing Fellows.

Agenda:

  • 10:00-10:05 Introduction (Professor Greg Slabaugh)
  • Presentation #1: Realising the Potential for Learning from Electronic Health Records using Synthetic Data by Dr William Marsh
  • Presentation #2: Robot-human tool handover in an intelligent framework of tactile interaction by Professor Kaspar Althoefer
  • Presentation #3: Can large biomedical datasets be interpreted automatically? by Professor Conrad Bessant
  • Presentation #4: Making sense of cancer evolution with mathematical models and machine learning by Professor Trevor Graham
  • 12pm: Closing Remarks (Professor Greg Slabaugh)

Date and time: Thursday, 16 September 2021, 10:00 - 12:00

Format: Online via Zoom

Registration is required! Register in advance for this event by clicking this link. After registering, you will receive a confirmation email containing information about joining the event.

Presentation #1: Realising the Potential for Learning from Electronic Health Records using Synthetic Data by Dr William Marsh

The clinical data held in Electronic Health Records (EHR) offers excellent opportunities for researchers to improve the treatment and management of patients. However, so far it has been difficult to realise this potential. There is a need to provide more information about the meaning of the data to the data analyst. We analyse this problem, explaining why the database schema is not very informative. We explain the data journey, which is the series of transformation from the original data to the data suitable for analysis, and why this needs to be explicit. We also suggest that synthetic data could be used to accelerate access to data and we report on the performance achieved using Bayesian Networks to create synthetic data.

Presentation #2: Robot-human tool handover in an intelligent framework of tactile interaction by Professor Kaspar Althoefer

Purposefully handling objects comes very naturally to humans – we instinctively understand how to pick up a tool and rearrange it in our hands so that we can use the tool to conduct a specific task or pass it on to someone else to conduct a task themselves. We are also adept at anticipating when and how a tool will be received when passed to us by someone. Our project aims to create intelligent methods for natural and intuitive human-robot interaction. Robots are to be equipped with the required intelligence to anticipate the handing over of tools and to actively support the human completing it as part of activities performed in different work environments, including surgery, manufacturing and nuclear waste decommissioning.

An important aspect of the work is to capture tactile information during the handover action. We hypothesise that principal motion parameters are recognised by humans through their tactile sensors within their fingers when an object moves from one hand to another - essential for increasing the chances of a successful handover.

In my presentation, I will provide an overview of our research in tactile and force sensing. I will highlight our advancements concerning the integration of tactile sensors with robot hands for improved manipulation capabilities and research efforts towards thin-layer tactile sensors to be worn by humans allowing us to study close-up the interaction dynamics that human hands experience during tool handovers. Going beyond the biological role model, we also develop proximity sensors that when integrated with robot hands allow us to estimate useful distance information when approaching the object to be handled as well as the object's stiffness during handling.

Presentation #3: Can large biomedical datasets be interpreted automatically? by Professor Conrad Bessant

Modern biomedical studies often involve the measurement of tens of thousands of molecular variables from relatively few (often <100) samples. The dimensionality, heterogeneity and missingness of the resulting datasets make them ill-suited to traditional statistical techniques and machine learning. Furthermore, pulling out novel discoveries from these data is only possible in the context of a large body of prior knowledge. Using high throughput phosphoproteomics data as an example, this talk will explore the potential of logic programming for the automated interpretation of biomedical data. We will demonstrate the extent to which this approach can automatically explain observed results and generate novel hypotheses suitable for laboratory validation.

Presentation #4: Making sense of cancer evolution with mathematical models and machine learning by Professor Trevor Graham

Cancer research is full of Big Data, foremost genome sequencing data. Typically these datasets have been analysed with sophisticated statistical methods that find patterns in the data, with biological interpretation of the patterns added post hoc. We have pursued an alternative approach where we propose mathematical models of cancer evolutionary dynamics up front - these models are derived from biological first principles - and then use statistical inference is used to match models to data. This approach gives us mechanistic insight into how cancers grow, and offers a direct route to predicting the future disease course.

12pm: Closing Remarks (Professor Greg Slabaugh)

Location:  Online via Zoom
Contact:  Dr Elisa PIccaro
Email:  elisa.piccaro@qmul.ac.uk
Website:  https://qmul-ac-uk.zoom.us/meeting/register/tZIpc-CpqjIpHNP_t2-vOO6Y8t0wWKYpZJwu

Updated by: Kashim Barick