(Data Science and Artificial Intelligence MSc, 2021)
The unique situation of completing a masters during a pandemic seemed to bring my coursemates and I together; our course, being a conversion MSc, was the start of a big change for all of us, each coming from a distinct, non-computer science background.
Tell us a bit about your time at Queen Mary, including any special or favourite memories.
My MSc took place over perhaps one of the most unusual years for Queen Mary, or for any university in the world for that matter! I had just returned to London after a backpacking trip - the last leg of which involved spending six weeks in lockdown in New Zealand. So, it came as little surprise that my course mates and I spent the whole year learning from home. From the very start, though, that unique situation seemed to bring us together; I believe it also had to do with the fact that our course, being a conversion MSc, was the start of a big change – a new levelling up, if you will – for all of us, each coming from a distinct, non-computer science background. So, there was a real sense of a special kind of camaraderie and willingness to learn as a team from the start, even if we didn’t actually meet at the pub until after our final exams! I’m keen to see where they’re all headed when we see each other at graduation.
I also had the pleasure of working with Dr Ildar Farkhatdinov and Dr Stuart Miller on a research report based on a series of interviews to understand the evolving technology needs of wheelchair users in the communities surrounding Queen Mary in east and southeast London. It was a great opportunity to put my developing skills into service and be exposed to more of the diversity of lived experience in our city. I do hope Queen Mary continues to nurture such links to its community and grows further as a powerful agent for inclusion.
Why did you choose to study MSc Data Science and Artificial Intelligence at Queen Mary?
During my lockdown in New Zealand, I developed a real passion for programming. I was already in a process of re-orientation and at that moment resolving to build a data science skill set just felt right as I had wanted to re-activate my analytical side for some time. About two months into intensive self-study, I knew I was serious about this and was looking for the next level, so the decision to pursue a masters came quite naturally to me. My brother had really enjoyed his MSc in Banking at Queen Mary some years prior, so it was one of the first universities I thought of, and it was good fortune that the university was piloting the conversion MSc in Data Science and Artificial Intelligence that same year.
Which modules did you most enjoy and did anything surprise you during your studies?
I particularly enjoyed Natural Language Processing with Dr Julian Hough. The importance of NLP across domains cannot be understated, and the area is by nature a uniquely fun combination of linguistics and computer science. We worked on problems like detecting fake Amazon reviews and identifying soap opera characters by their script lines, but above all the module worked well as a hands-on, in-at-the-deep-end bootcamp on data exploration, cleaning, pre-processing and feature engineering right through to machine learning model development and evaluation. There were certainly highlights across other modules, like for example the thorny issues we grappled with each week in the Data Ethics module – I could be here all day listing my memories! The dissertation module itself was also a crucial highlight. It is an entirely self-directed learning experience intended to give you a taste of the primary purpose of top-level academia. For me it was extremely rewarding and gave me a strong signal that a PhD was the right next step for me.
I had the pleasure of working with Dr Ildar Farkhatdinov and Dr Stuart Miller on a research report based on a series of interviews to understand the evolving technology needs of wheelchair users in the communities surrounding Queen Mary in east and southeast London.
Did any academics have a strong influence on shaping your time and studies here?
I found virtually all my instructors very approachable and had some fruitful discussions with a few of them as I explored the various possible doctoral study routes throughout the year. My dissertation supervisor Dr Athen Ma guided my interest in systems and networks towards graph machine learning; Prof Ginestra Bianconi, who quite literally wrote the book on multilayer networks, and whom I never actually met, was also a great inspiration. Dr Raul Mondragon took the time to advise me on writing a research proposal and encouraged me to continue applying for PhDs despite my initial efforts being unsuccessful. Dr William Marsh and Dr Emmanouil Benetos were my instructors in the statistics and data mining modules and provided references for each of my applications – their support has been absolutely instrumental, and I will always be grateful to them.
Were you a member of any societies or volunteering groups during your time at Queen Mary? If so which and what did you gain from them?
I joined many of the talks and discussions hosted by Queen Mary’s interdisciplinary Institute of Applied Data Science. The weekly lecture series is run by a group of enthusiastic PhD students, and they regularly invite top academics and industry practitioners from the world of AI and data science. It is an invaluable community for students wanting to understand the landscape and keep up to date with the latest research and application trends.
Huge congratulations on securing a fully funded PhD at Sheffield University. What inspired you to do a PhD and what inspired your PhD thesis?
I was already considering doctoral studies at the very start of my course. By then it was clear that data science was a perfect discipline for me to combine an orientation towards real-world problems with experimentation, which I had found lacking in my previous studies and work. So, by the end of the first semester, I was aiming for an industry-partnered PhD as an ideal scenario. My PhD topic of interest – machine learning on biomedical graph databases - developed from my dissertation focus on ecological networks. The company I’m now collaborating with, Evotec, happened to be building their own biomedical graph database to explore this new direction in AI-powered drug research. One must always appreciate the element of luck, along with the hard work, that is involved in great opportunities!
Based on the knowledge you have gained from your master’s degree at Queen Mary, can you predict any outcomes of your PhD research at this early stage?
I hope to contribute to developing novel graph data mining and machine learning approaches to help reduce the cost and speed up the process of drug development. This can be via identifying new uses for medications already on the market or shedding light on previously unknown mechanisms of disease that therapeutic substances can be designed to target. Pharmaceutical development is a very expensive process whose economics have been worsening for a number of years and the bill ends up being covered by public health services like the NHS here in the UK and by patients themselves. It would be amazing to see my research make an impact to counter this trend.
The high expectations set in the modules ensured that I developed confidence and a habit for background preparation when approaching challenging scientific topics or discussions with academics who are leaders in their field – of which Queen Mary has a few!
How has your master’s degree and your time at Queen Mary prepared you for your current PhD studies?
The MSc was an intense experience where a deadline (or an exam!) was always on the horizon. So perhaps first and foremost it was a thorough exercise in self-motivation and time management. Planning out the time needed for assignments, knowing when to move on to the next bit of work and having the belief that if you get stuck you will find a solution – even it isn’t the one you originally expected – are all abilities I know will be key in my PhD studies. The high expectations set in the modules also ensured that I developed confidence and a habit for background preparation when approaching challenging scientific topics or discussions with academics who are leaders in their field – of which Queen Mary has a few!
What are your career plans for the future? How are you planning on using the research gained during your PhD to contribute to a particular field?
Following my PhD, I will look to transition into a research position within industry. Modern biotechnology is a fascinating field, and the latest experimental techniques are producing massive amounts of very detailed data, which means an ever-growing need for powerful computational methods to extract the insights about human health hiding in the data. Not a bad industry for promising start-ups either!
What advice would you give to a prospective student considering studying MSc Data Science and Artificial Intelligence at Queen Mary?
Be sure that you’re having fun with the subject. If you can geek out and you don’t get easily frustrated when the computer isn’t doing what you expect, I’d say you’ve already got a foundation for a fruitful year. But also take some time from the start to consider what you want to do with your degree. There are definitely good job opportunities straight out of it, and there’s also the PhD route; both are very competitive. If you want a job, learn what skills will make you stand out besides your course work, and understand how the jobs you’re aiming for impact company bottom lines, or how they contribute to policy objectives, if in the public sector. If you want to go into research, do your best to find an area you’re very interested in, then home in on as sharp a research problem as you can. If your dissertation topic is related to it then that’s a big plus. And don’t forget to talk to Queen Mary academics! You never know what or who you’ll get to know.
If you would like to get in touch with Terence or engage them in your work, please contact the Alumni Engagement team at alumni@qmul.ac.uk.