Agilent Science Futures – An Interview With Alexandra Richardson
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The Science Futures project is an initiative designed by Agilent Technologies to gain views and perspectives from a roundtable of PhD students on the impact that the collaborative relationship between academia and industry has on their work. In this instalment, we hear from Alexandra Richardson.
Alex is a student at King’s College London, where she is studying pharmaceutical compounds in the environment using a passive sampler device. Her research is part of a four-year funded PhD program, from the London Interdisciplinary Biosciences Consortium (LIDo) industrial case (iCase) studentship in partnership with Agilent Technologies.
In this interview, Alex tells us more about her research, the role that technology is playing in it and how important industry partnerships have been.
Can you tell us a little about your research and what you hope to achieve?
Alex Richardson (AR): The purpose of my research is to replace invertebrate testing using an artificial device such as a passive sampler. In our river systems there, unfortunately, is pollution from pharmaceuticals, pesticides, and other chemicals we use in our everyday life. These contaminants enter the rivers through the treated discharge from sewage and wastewater treatment plants. We can fairly easily measure the concentration of the contaminants in water using various techniques, but what is more tricky is measuring the contaminant concentration in the animal itself. At the moment, the way we do this involves catching said animal, killing it, processing it and a long list of other steps which are fairly complicated and frankly unpleasant.
What I am trying to do, is use an artificial device, such as a passive sampler, which can be left in the river for about seven days. Over that time the passive sampler acts like a chemical sponge and soaks up some of the contaminants in the water. At the end of the seven days, we can collect the passive sampler from the river and extract off what was soaked up. From there, the plan is to use machine learning tools to predict the concentration in the animal from what and how much was absorbed by the passive sampler. We can then perform rapid assessment of pharmaceutical and other contaminants in the animals, without having to go out and catch the little guys.
For this research, I am looking at a tiny invertebrate called Gammarus pulex, it’s a very small shrimp which is the basis of most food webs in rivers and streams in Europe and the UK. With the hope that if this work is successful, we can scale it up across taxa to larger vertebrates such as fish.
Is there a particular problem or issue that your research faces that you are addressing?
AR: There have been a couple of challenges I have encountered in carrying out this research. One that comes to mind is wrapping my head around machine learning and trying to figure out how to apply the right one to my research question. Machine learning models are great tools and an exciting field, but I initially struggled with understanding the math behind how they worked, math has never been my strongest subject. This was partially driven by my desire to understand what was actually going on inside a model, rather than just hand-waving it away as a black box that just works. What was a bit of surprise to me was that the field of machine learning is much larger than I originally thought, encompassing “everyday” algorithms such as regression and PCA (principal component analysis) as well as neural networks and deep learning. An upcoming challenge that I will have to deal with is selecting the right model for the job of predicting from the passive sampler to the animal. To be honest I don’t really have an idea on which model will work, so my current plan is to try a couple, see what works and go from there. Another challenge of working with and creating useful models is that a model is only as good as the data you use to develop it. So, care must be taken to ensure that the data I collect and use to train my models is of a high quality in order to create a robust prediction tool. On top of that, these algorithms require a couple of different inputs or features in order to do the prediction, such as the weight of the molecule or the number of carbons it has etc. Selecting which of those features to use and what combination is an entirely different kettle of fish to selecting the type of model and I suspect I will fall down a deep rabbit hole of feature/input selection before the end of my PhD. Though I am looking forward to the challenge of trying out different combinations of models and features to find the best fit, it will be like solving a big puzzle.
What are the most important outcomes of your research?
AR: I love the environment and I want to help preserve these amazing habitats for future generations to enjoy. By developing tools that can be used to better assess chemical pollution both in the water and in the animals that live there I hope to play a small part in protecting and sustaining these ecosystems. On top of that, I find the application of machine learning to environmental problems fascinating. So, the possibility of being able to model a living organism from an artificial device, such as my passive sampler, is pretty exciting and could possibly have applications outside of my field.
What global or societal challenges does your research address?
AR: The global and societal challenge is pharmaceutical pollution and emerging contaminants in the environment. This research will provide a way to rapidly monitor these contaminants in the ecosystem and in the animal. At the moment we have policies for “safe” levels of these contaminants in our water system but if the “safe” level is actually lethal or poses significant risk to the animal, that level is not very effective at preserving that environment. By having this technology and knowledge, we can potentially identify which contaminants pose a risk to the environment and find ways to better scrub these from the wastewater.
What role has technology played in your research?
AR: Technology has played a bigger role than we expected in my PhD. Originally for passive sampling, we were going to use one of the commercially available passive samplers. Through exchanging an idea with one of my colleagues, I decided to develop my own passive sampler using 3D printing. Having access to a 3D printer in our lab has allowed me to take my PhD in a slightly different direction than expected, but with the same broad idea. Access to open-source information through the internet has been extremely useful in my research. From online courses in 3D printing, to open journal access and to the abundance of online documentation for R and Python code. This has been most helpful when trying something new or debugging my scripts.
To keep momentum going, in this field, and to keep pushing these advancements, we need access to the capabilities and technology, whether that be through open-source journals or through encouraging collaboration between industry and academia.
How important has mentorship been throughout your research?
AR: For me, mentorship has been extremely important in all facets of my research both when I was just getting started and even now a couple of years in. I would say that some of my biggest support comes from the mentorship of not only my primary supervisor, Dr. Leon Barron, but other PhD students, post-docs, academics, and professionals I have come into contact with throughout my PhD. These people have been wonderful in offering support and guidance in whatever way they can, from training me on new instruments and techniques to asking me difficult questions that force me to stop and think.
What is brilliant for me is at Kings College London we have what is called a Thesis Progress Committee. Which is a group of experts that help guide you through your PhD. For me this has been very helpful as they aren’t as close to my work as I am and can help me see the bigger picture and keep everything in perspective. At Agilent, the support of Drs David Neep, as my industry supervisor based at the Church Stretton site, and Marcus Chadha up in Cheadle has been invaluable. From getting my hands on consumables to helping me navigate the world of mass spectrometry and Agilent software.
My whole supervisory team both at KCL and Imperial College have been great, and it’s good to know I can knock on any of their doors and say “I’m too deep in my own brain, can I talk to you about this” or “so I have an idea, but am not sure how to make it work” and they will make the time to help me.
Have you been given opportunities to interact with industry and companies to progress your research?
AR: My PhD research is part funded by Agilent Technologies and they have been extremely supportive in a lot of ways. From providing consumables, to training me on their instruments and supporting my attendance to conferences and workshops. Aside from Agilent I have had the opportunity to collaborate with Natural Resources Wales and other academic groups on a couple of projects that are very interesting and have yielded some great results, some of which I have already published and hopefully will have some more papers coming out in the future.
I think that collaboration between industry and academia is very important to the future of science in all fields, as the frontline research and ideas from academia can be supported by the practical experience and know-how of industry to better develop and refine the product or process, we are all working on together.
Can you tell us more about the skills gap coming into industry from academia?
AR: From my past experience, there was definitely a skills gap moving from my masters studies into industry in terms of hands-on experience, practical know-how and understanding the industry process. What I found was that I understood the theory behind some of the processes I was doing on the job but applying that knowledge in a practical scenario was very different. I can see this being true for other students who maybe did not have the same masters experience that I did. Who have maybe touched an analytical instrument once during their masters or undergraduate and then moving to industry where they are almost expected, depending on the workspace, to know and understand why certain things are done the way they are. From an industry perspective, the way that gap could be bridged would be to introduce more of an “introduction to industry” training program, like an inter-collated year that medics do, but in industry, so you are not just walking into industry, first job after a masters and get asked to set up an instrument you only touched once.
As a result of your studies and research work, what do you envisage your career destination as being?
AR: To be honest, I have not quite decided myself, I see myself still in research but am flexible whether that’s in academia, industry or civil service. For now, I want to do a one or two year post-doc project in order to better experience the broader academic world that, as a PhD student, you are still quite sheltered from. That being said, I know that wherever I end up, I do want ties with industry because they are the people that are using the technology that academia develops and are applying it every day.
What challenges do you face as a PhD student in understanding your options at the end of a PhD?
AR: For me, the biggest challenge is indecision. I have an idea of where I want to go, but I don’t quite know what that is yet. In terms of options, as said before, I want to do a post-doc before moving onto something else. I don’t know if I am ready to start my own lab just yet, but that’s another idea for the future.
I am trying to keep my options as open and flexible as possible. Not to say I am just sitting back and waiting for something to come at me, but I have learned that if I mentally set something in stone, often as not, a curve ball will come at you and disrupt your plan. For example, originally, I was only supposed to spend one and a half years in the UK getting my master’s degree, before heading back to Australia, and I’m still here almost six years later doing a PhD and having spent two years in industry post masters.
How prepared do you consider yourself to be for real-world achievement?
AR: I would say over the course of my PhD, I have given myself a crash course in skills which are very applicable to my future employment. In terms of readiness for the real world, I wouldn’t say I am 100% ready yet, however I am learning the skills and building my repertoire that will make me ready for real world achievement. The access to technology through working with industry partners has opened up new career horizons for me and I am excited to decide which direction I will take post PhD.
Keep an eye out for the next instalment of Science Futures, where Max Lennart Feuerstein will be sharing his experiences.
Find out about some of the common themes to come out of the project here.