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.
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.
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.
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.
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.
