Tag: JGI Team

The Royal Statistical Society Annual Conference 2024

Posted on by kerry.turcsi

The Royal Statistical Society meets annually for their internationally attended conference. It serves as the UK’s annual showcase for statistics and data science. This year they met in Brighton for a conference attended by over 600 attendees from around the world, including JGI Data Scientist Dr Huw Day.

The conference had over 250 presentations, including contributed talks, rapid-fire talks, and poster presentations. At any one time, there could be as many as 6 different talks going on, so it was impossible to go to everything but below are some of Huw’s highlights of the conference.

Pre-empting misunderstandings is part of trustworthy communication

From left to right; Dr Huw Day, Professor Sir David Spiegelhalter and Dr Simon Day
From left to right; Dr Huw Day, Professor Sir David Spiegelhalter and Dr Simon Day (RSS Fellow and Huw’s dad) at the RSS International Conference 2024.

As part of a session on communicating data to the public, Professor Sir David Spiegelhalter talked about his experiences trying to pre-bunk misinformation when displaying data.

Data in June 2021 showed that the majority of COVID deaths are in the vaccinated group. The Brazilian president President Jair Bolsonaro used this data to support claims that Covid vaccines are killing people. Spiegelhalter and his colleague Anthony Masters tried explaining why this wasn’t a sign the vaccine was bad in an article in The Observer “Why most people who now die with Covid in England have had a vaccination”.

Consider the following analogy: most car passengers who die in car accidents are wearing seatbelts. Intuitively, we understand that just because these two variables are associated, it doesn’t mean that one causes the other. Having a story like that means you don’t have to talk about base rates, stratification or even start to use numbers in your explanations.

We should try to make the caveats clearer of data before we present them. We should be upfront from what you can and can’t conclude from the data.

Spiegelhalter pointed to an academic paper: “Transparent communication of evidence does not undermine public trust in evidence” where participants were shown either persuasive or balanced messages about the benefits of Covid vaccines and nuclear power. It’s perhaps not surprising to read that those who already had positive opinions about either topic continued to have positive views after reading either messages. Far more interesting is that the paper concluded that “balanced messages were consistently perceived as more trustworthy among those with negative or neutral prior beliefs about the message content.”

Whilst we should pre-empt misconceptions and caveats, being balanced and more measured might prove to be an antidote to those who are overly sceptical. Standard overly positive messaging is actively reducing trust in groups with more sceptical views.

Digital Twins of the Human Heart fueled Synthetic 3D Image Generation

Digital twins are a digital replica/simulator of something from the real world. Typically it includes some sort of virtual model which is informed by real world data.

Dr Dirk Husmeiser at the University of Glasgow has been exploring the application of digital twins of the human heart and other organs to investigate behaviour of the heart during heart attacks, as well as trying to use ultrasound to measure blood flow to estimate pulmonary blood pressure (blood pressure in the lungs). Usually, measuring pulmonary blood pressure is an extremely invasive procedure, so using ultrasound methods has clear utility.

One of the issues of building a digital twin is having data about what you’re looking at. In this case, the data looks like MRI scans of the human heart, taken at several “slices”. Because of limitations in existing data, Dr Vinny Davies and Dr Andrew Elliot, (both colleagues of Husmeiser at the University of Glasgow)have been attempting to develop methods of making synthetic 3D models of the human heart, based on their existing data. They broke the problem down into several steps, working to generate synthetic versions of the slices of the heart (which are 2D images) first.

The researchers were using a method called Generative Adversarial Networks (GANs), where two systems compete against each other. The generator system generates the synthetic model and the discriminator system tries to distinguish between real and synthetic images. You can read more about using GANs for synthetic data generation in a recent JGI blog about Chakaya Nyamvula’s JGI placement.

Slide on “Generating Deep Fake Left Ventricle Images for Improved Statistical Emulation”.
A slide from Dr Vinny Davies and Dr Andrew Elliot’s talk on “Generating Deep Fake Left Ventricle Images for Improved Statistical Emulation”. The slide depicts how progressive GANs work, where the generator learns how to generate smaller, less detailed images first and gradually improves until it can reproduce 2D slices of MRIs of the human heart.

Because the job of the generator is far harder than that of the discriminator (consider the task of reproducing a famous painting, versus spotting the difference between an original painting and a version drawn by an amateur), it’s important to find ways to make the generator’s job easier early on, and the discriminator’s job harder so that the two can improve together.

The researchers used a method called a Progressive GAN. Initially they gave the generator the task of drawing a lower resolution version of the image. This is easier and so the generator did easier. Once the generator could do this well, they then used the lower resolution versions as the new starting point and gradually improved the correlation. Consider trying to replicate a low resolution image – all you have to do is colour in a few squares in a convincing way. This then naturally makes the discriminator job’s harder, as it’s tasked with telling the difference between two, extremely low resolution images. This allows the two systems to gradually improve in proficiency.

The work is ongoing and the researchers at Glasgow are looking for a PhD student to get involved with the project!

Data Hazards

On the last day of the conference, Huw alongside Dr Nina Di Cara from the School of Psychology at the University of Bristol presented to participants about the Data Hazards project.

Participants (including Hadley Wickam, keynote speaker and author of the famous R package tidyverse) were introduced to the project, shown examples of how it has been used and then shown an example project where they were invited to take part in discussions about which different data hazards might apply and how you might go about mitigating for those hazards. They also discussed the importance of focussing on which hazards are most relevant and prominent.

Dr Huw Day (left) and Dr Nina Di Cara in front of a screen that says 'Data Hazards Workshop'
Dr Huw Day (left) and Dr Nina Di Cara (right) about to give their Data Hazards workshop talk at the RSS International Conference 2024.

All  the participants left with their own set of the Data Hazard labels and a new way to think about communicating hazards of data science projects, as well as invites to this term’s first session of Data Ethics Club.

Chakaya Nyamvula’s JGI Placement 

Posted on by kerry.turcsi

Hi, I’m Chakaya. I am currently pursuing my MSc in AI and Data Science at Keele University and working as a Business Intelligence Analyst at iLabAfrica at Strathmore University in Nairobi, Kenya. This summer, thanks to the partnership between iLabAfrica and JGI, I had an amazing opportunity to work with JGI for my Master’s placement. I wanted to immerse myself in a research environment and connect with people in academia to help figure out my future career path. Working under the guidance of Dr Huw Day, I gained valuable insights into the world of research and expanded my professional network, all while experiencing life in the UK. 

Chakaya Nyamvula in front of a body of water
Chakaya Nyamvula, JGI Intern

What was the project about? 

Previously for a JGI funded Seedcorn project Mark Mumme, Eleanor Walsh, Dan Smith, Huw Day, and Debbie Johnson had surveyed researchers on their thoughts on how they might want to use synthetic data to help with their research. 

Synthetic data is when you take an existing dataset and create a synthetic (i.e. fake) version of it. You might want to do this so you can share something that looks like the data but preserves the privacy of individuals in it, whilst still having a flavour of what the data looks like and what statistical patterns might be present within it. This is useful for writing data pipelines whilst you go through necessary ethics checks to access sensitive data, amongst other things. 

For my summer placement with JGI, I worked with the MIMIC IV dataset of electronic health records and explored methods of generating synthetic versions of some of this data. It was also important to understand how you could measure or benchmark how successful your synthetic data generation has been, based on how well you had preserved privacy or how well the statistics of your synthetic data emulated those of your real data. 

What else did you do as part of your placement? 

Alongside my main work, I attended JGI Data Science meetings and learnt about some of the data science projects at the JGI including a project on antimicrobial resistance and another on 3D image analysis of CT scanned zebrafish to study bone development. 

For some of the more computationally demanding aspects of the project, I got taught how to make use of the JGI’s server (known within the office as “Jeeves”). 

I also had the opportunity to meet some PhD students at the University of Bristol, ask them about their research, and get advice on applying for PhDs in the future. 

Left to right, Huw Day, Elena Fillola Mayoral, Yujie Dai and Chakaya Nyamvula sat at a table at an ice cream shop
From left to right: Huw Day (JGI Data Scientist), Elena Fillola Mayoral (PhD student in AI for Climate), Yujie Dai (CDT in Digital Health) and Chakaya Nyamvula (JGI Intern) discussing PhDs over ice cream

What did you learn about? 

One deep learning method we used was something called a Generative Adversarial Network (GAN). Prior to this project, I had never worked with GANs before, so diving into this methodology was both challenging and exciting.  

A GAN works by having two competing neural networks, a generator and a discriminator. The generator’s job in this case was to take the original data and generate synthetic versions of that data. The discriminator’s job is to try and spot the difference between the real and the synthetic data that has been generated. One of the advantages of such a system is that you have two outputs: 1) a neural network which can generate synthetic data based on some training data and 2) a second neural network which can discriminate between real and synthetic data. This has advantages for applications where people might maliciously generate synthetic data, for example deep fake images. 

A good analogy for GANs is two people learning chess by playing against one another. If both start at similar skill levels, then as one person improves, the other slowly improves too. If you lose a chess game, you know you made a mistake and you might be able to work out how to improve for the next time. If you win, then you know you were doing something right.  

However, if you pit a chess grandmaster against a complete beginner, then the beginner will lose every time and will struggle to understand where they are going wrong, making it difficult to improve. Because the task of making synthetic data is quite complicated, when we began the process of training the GAN, the generator was frequently getting it wrong and wasn’t really able to figure out how to improve. 

To combat this, we did two things. First, you can handicap the discriminator a bit to give the generator a head start (imagine making your grandmaster play blindfolded). This helped, but still wasn’t enough. 

One of the pair plots showing generated vs real data a epoch 0
One of the pair plots showing generated vs real data a epoch 25000
Pair plots showing how well the real and the synthetic data matches by comparing each column. Real data is in blue, synthetic data is in red. The diagonal plots show histogram density plots of each column and how it compares between real and synthetic data. The off diagonal show scatter plots between pairs of variables. The left pair plot shows the output at the start of training, where the synthetic generator just randomly samples a scatter of points. You can see that this is not a good match for the original data. The right pair plot shows that after training, the generator does a lot more of a convincing job at emulating the real data. It is still not perfect, but it is particularly good at identifying clumps of data.

Secondly, you can start to think about how you inform your neural networks whether or not they were successful. Imagine if instead of “win” or “lose” as your outcome of the chess games, you got a measure of how well you performed, say a measure of how many good moves you made. With this more specific information, it becomes easier to decipher why you lost and how you might improve.  

To Be Continued? 

To finish my placement, I shared my experience with my placement supervisors at Keele University through a presentation and a report. I then had the opportunity to present my work to the Data Science Seminar at the University of Bristol, with several lecturers from the data science community in attendance, alongside JGI Data Scientists and some friends I made along the way.  

Additionally, all the code we worked on can be found in a public GitHub repository for other researchers to use and experiment with can be found on Chakaya’s Github.

Chakaya Nyamvula and Huw Day standing in front of a projector presenting at the Data Science Seminar. The projector has a slide on it that says 'Introduction to synthetic data' 
Chakaya Nyamvula (left) and Huw Day (right) presenting at the Data Science Seminar 

Reflecting on my placement at JGI, I can confidently say it was an incredible learning experience. I had the privilege of working with a fantastic supervisor, Dr Huw Day, who provided guidance throughout the project. Co-working with the talented data scientists at JGI was both inspiring and rewarding, and I thoroughly enjoyed networking with professionals in academia. The challenges I faced particularly working with GANs for the first time, pushed me to grow and expand my skill set.  Overall, this experience not only deepened my technical expertise but also solidified my interest in pursuing a career that bridges research and data science. 

New Turing Liaison Officers join the JGI team

Posted on by kerry.turcsi

As an active member of the Turing University Network, we have appointed a Turing Liaison Manager and two Turing Liaison Academics to support and enhance the partnership between Alan Turing Institute and the University of Bristol. These roles will be focusing on increasing engagement from Turing, developing external and internal networks around data science and AI, and supporting relevant interest groups, Enrichment students and Turing Fellows at the University of Bristol.

Turing Liaison Manager, Isabelle Halton and Turing Academic Liaisons, Conor Houghton and Emmanouil Tranos, are keen to build communities around data science and AI, providing support to staff and students who want to be more involved in Turing activity.

Isabelle Halton
Isabelle Halton
Turing Liaison Manager
Connor Houghton
Connor Houghton
Turing Academic Liaison
Emmanouil Tranos
Emmanouil Tranos
Turing Academic Liaison

Isabelle previously worked in the Professional Liaison Network in the Faculty of Social Sciences and Law. She has extensive experience in building relationships and networks, project and event management and streamlining activities connecting academics and external organisations.

Conor is a Reader in the School of Engineering Mathematics and Technology, interested in linguistics and the brain. Conor is a Turing Fellow and a member of the TReX, the Turing ethics committee.

Emmanouil is currently a Turing Fellow and a Professor of Quantitative Human Geography, specialising primarily on the spatial dimensions of the digital economy.

If you’re interested in becoming more involved with Turing activity or have any questions about the partnership, please email Isabelle Halton, Turing Liaison Manager via the Turing Mailbox

Ask JGI Student Experience Profiles: Rachael Laidlaw

Posted on by kerry.turcsi

Rachael Laidlaw (Ask-JGI Data Science Support 2023-24) 

I first came into contact with the Jean Golding Institute last year at The Alan Turing Institute’s annual AI UK conference in London, and then again in the early stages of the DataFace project in collaboration with Cheltenham Science Festival. This meant that before I officially joined the team back in October, I already knew what a lovely group of people I’d be getting involved with! Having nice colleagues, however, was not my only motivation for applying to be an Ask-JGI student. On top of that, I’d decided that whilst starting out in my ecological computer-vision PhD niche, I didn’t want to forget all of the statistical skills that I’d developed back in my MSc degree. Plus, it sounded really fun to keep myself on my toes by exercising my mind tackling a variety of data-oriented requests from across the university’s many departments. 

Rachael Laidlaw in centre with two JGI staff members to the left and one JGI staff member to the right pointing towards a Data pin board at the JGI stall
Rachael Laidlaw (centre), second-year PhD student in Interactive Artificial Intelligence, and other JGI staff members at the JGI stall

During the course of my academic life, I’ve taken the plunge of changing disciplines twice, moving from pure mathematics to applied statistics and then again to computer science, and I liked the idea of supporting others to potentially do the same thing as they looked to enhance their work by delving into data. Through Ask-JGI, I kept my weeks interesting by having something other than my own research to sometimes switch my focus to, and it felt very fulfilling to be able to offer useful technical advice to those who were in the same position that I myself had been in not so long ago too! I therefore got stuck in with anything and everything, from training CNNs for rainfall forecasting or performing statistical tests to compare the antibiotic resistance of different bacteria, to modelling the outcomes of university spinouts or advising on the ethical considerations and potential bias present when designing and deploying a questionnaire-based study. And, of course, by exposing myself to these problems (alongside additional outreach initiatives and showcase events), I also learned a lot along the way, both from my own exploration and from the rest of the team’s insights. 

One especially exciting query revolved around automating the process of identifying from images which particular underground printing presses had been used to produce various historical political pamphlets, based on imperfections in the script. This piqued my interest immediately as it drew parallels with my PhD project, highlighting the copious amount of uses of computer vision and how it can save us time by speeding up traditionally manual processes: from the monitoring of animal biodiversity to carrying out detective work on old written records. 

All in all, this year has broadened my horizons by giving me great consultancy-style work experience through the opportunity to share my expertise and help a wide range of researchers. I would absolutely encourage other curious PhD students to apply and see what they can both give to and gain from the role! 

From Data to Discovery in Biology and Health

Posted on by kerry.turcsi

ELLIS Summer School on Machine Learning in Healthcare and Biology – 11 to 13 June 2024  

Huw Day, Richard Lane, Christianne Fernée and Will Chapman, data scientists from the Jean Golding Institute, attended the European Laboratory for Learning and Intelligent Systems (ELLIS) Summer School on Machine Learning for Healthcare and Biology at The University of Manchester. Across three days they learned about cutting edge developments in machine learning for biology and healthcare from academic and industry leaders.

A major theme of the event was how researchers create real knowledge about biological processes at the convergence of deep learning and causal inference techniques. Through this machine learning can be used to bridge the gap between well-controlled lab experiments and the messy real world of clinical treatments.

Advancing Medical Science using Machine Learning

Huw’s favorite talk was “From Data to Discovery: Machine Learning’s Role in Advancing (Medical) Science” by Mihaela van der Schaar who is a Professor of ML, AI and Medicine at the University of Cambridge and Director for the Cambridge Centre for AI in Medicine.

Currently, machine learning models are excellent at deducing the association between variables. However, Mihaela argued that we need to go beyond this to understand the variables and their relationships so that we can discover so-called “governing equations”. In the past, human experts have discovered governing equations with domain knowledge, intuition and insight to extract equations from underlying data.

The speaker’s research group have been working to deduce different types of underlying governing equations from black box models. They have developed techniques to extract explicit functions as well as more involved functional equations and various types of ordinary and partial differential equations.

On the left are three graphs showing temporal effects of chemotherapy on tumour volume for observed data, D-CODE and SR-T. On the right is the actual equations for the D-CODE and SR-T plots on the left.
Slide 39 from Mihaela van der Schaar’s talk, showing observed data of the effects of chemotherapy on tumour volume over time and then two examples of derived governing equations in plots on the left with the actual equations written out on the right

The implications for healthcare applications are immense if these methods are able to be reliability integrated into our existing machine learning analysis. On a more philosophical angle, it begs interesting questions about how many systems in life sciences (and beyond) have governing equations and what proportion of these equations are possible to discover.

Gaussian processes and simulation of clinical trials

A highlight for Will and Christianne was the informative talk from Ti John which was a practical introduction to Gaussian Processes (GP) which furthered our understanding of how GPs learn non-linear functions from a dataset. By assuming that your data are a collection of realisations of some unknown random function (or combination of functions), and judicious choice of kernel, Gaussian Process modelling can allow the estimation of both long-term trends from short-term fluctuations from noisy data. The presentation was enhanced with this interactive visualisation of GPs, alongside an analysis of how the blood glucose response to meals changes after bariatric surgery. Another highlight was Alejandro Frangi’s talk on in silico clinical trials in which he described how mechanistic modelling (like fluid dynamic simulations of medical devices) can be combined with generative modelling (to synthesise a virtual patient cohort) to explore how medical treatments may perform in patients who would never have qualified for inclusion in a real randomised controlled trial.

Causality

Richard’s favourite talk was by Cheng Zhang from Microsoft Research on causal models, titled “Causality: From Statistics to Foundation Models”. Cheng highlighted that an understanding of causality is vital for the intelligent systems that have a role in decision-making. This area is on the cutting edge of research in AI for biology and healthcare – understanding consequences is necessary for a model that should propose interventions. While association (statistics) is still the main use case for AI, such models have no model of the “true” nature of the world that they are reflecting which leads to hallucinations such as images with too many fingers or nonsensical text generation. One recipe proposed by Cheng was to build a causally-aware model to:

  • Apply an attention mechanism/transformer to data so that the model focuses only on the most important parts
  • Use a penalised hinge loss- the model should learn from its mistakes, and should account for some mistakes being worse than others
  • Read off optimal balancing weights + the causal effect of actions – after training, we need to investigate the model to understand the impact of different actions.

In essence, this is a blueprint to build a smart system that can look at a lot of complex data, decide what’s important, learn from its mistakes efficiently and can help us understand the impact of different actions. As an example, we might be interested in how the amount of time spent studying affects students’ grades. At the outset, it’s hard to say if studying more causes better grades because these students might also have other good habits, have access to more resources, etc. Such a model would try to balance these factors and give a clearer picture of what causes what- effectively, it would try to find the true effect of studying more on a student’s grade, after removing the influence of other habits.

This behaviour is desired for the complex models being developed for healthcare and biology; for example, we may be interested in engineering CRISPR interventions to make crops more resilient to climate change or developing brain stimulation protocols to help with rehabilitation. A model proposing interventions for these should have a causal understanding of which genes impact a trait, or how different patterns of brain activity affect cognitive function.

Recordings of all the talks can be found on here