Judy Kingswolf-Yamakawa (MSc Computer Science (Conversion), 2025)
'The quality of research in the department is unmatched. Not only were my lecturers passionate about their subjects, and most importantly motivated to teach them to the highest standard, they are truly the academic experts of their niches.'
Why did you choose to study your programme?
I chose to study Computer Science because I wanted to understand how technology can shape smarter, fairer financial systems. Coming from a finance background, I was fascinated by how data, algorithms, and automation could change the way we invest, build sustainable portfolios, and measure impact. I had already used programming and database management in my career as the needs arose, usually a skillset not advertised for when dealing with valuation models and pitch decks. But I saw an exciting opportunity to pursue a career that combined my expertise with data manipulation, corporate finance and investor relations with the highly technical side of machine learning and AI. Most of all, I was seeking an ambitious challenge to force me to change the way I viewed tech investments, and to think more critically about the macro and technical sides of computer science.
Why did you choose to study at Queen Mary?
The quality of research in the department is unmatched. Not only were my lecturers passionate about their subjects, and most importantly motivated to teach them to the highest standard, they are truly the academic experts of their niches. Machine learning is a complex umbrella and thus there is so much space to build something new. It was an important decision for me that I would have the opportunity to learn beyond my modules from lecturers that had open door policies and were happy to explain their research and reward curiosity.
What did you most enjoy about your course?
The MSc in Computer Science gave me the freedom to explore my own ideas and promoted success through collaboration as well as independence. Tech is a particularly entrepreneurial field, and the course strongly encouraged and supported this within us as students. Societies were also available to me, such as the Machine Learning Society hackathons, guest lectures, and other projects. In my experience of the year, the technical theory was always tied to real-world impact. Every project felt like it could become something meaningful beyond the classroom.
Can you share an example of a project, assignment, or activity that stood out to you?
The research project phase of my programme was the most academically challenging and rewarding aspect. Unlike other master-level dissertation projects, my course required deeper research, coding, papers, presentation and a final Viva – the final aspect being a great experience for those interested in further PhD studies. My supervisor was incredibly supportive and pushed me to evolve my early-stage research into something I can pursue for publication. In my case, as I transformed my quant finance skills into a machine learning application, I was able to design 4 different methodologies for sparse portfolio optimisation (including stochastic neural networks). The support went across two Queen Mary departments and included shared mentorship with fund managers operating within industry, a network that was enabled and encouraged by my course supervisor.
Which modules did you enjoy the most and were there any academics that had a strong influence on shaping your time and studies here?
I enjoyed a fantastic breadth of modules; however, I would highlight the ‘Security & Authentication’ (Professor Pasquale Malacaria) and ‘Risk and Decision-Making for Data Science and AI’ modules as being especially fruitful. You are not only left with a comprehensive cover of the topic, but practical workshops and coursework that push you to go beyond the parameters of the course materials and discover more yourself. I found that my professors were always encouraging when asking questions beyond the scope of the core module, and always had a plethora of additional resources and papers to recommend for further study. My supervisor, Dr Yongxin Yang, played a large role naturally, as he helped me form my thesis and create a merged tech and quant finance project.
Describe your career path since graduating.
Since graduating, I have been working in Mergers & Acquisitions focused on the technology industry. It has been great exposure to immerse myself in post-deal technology integration and value creation, as well as build connections globally with some fantastic professionals. Alongside this, I have continued my research in ESG and AI-driven investment analytics and have submitted research for publication, specifically on testing various data sources for financial applications and building stochastic neural networks for asset analytics; I am excited to continue collaborating in this area and expand the applications of such research into practical quantitative finance. This year has exposed me to the thriving startup scene in London, and I look forward to progressing my career with the venture capital, investor and founder connections I have made along the way as we build. The combination of technical depth from my MSc and the strategic exposure from my internship has helped me build a foundation for a career I am very excited for.
How has your degree remained relevant throughout your career and are there any particular areas of your degree that you use in your day-to-day job?
My degree has been deeply relevant to my career. It gifted me the technical and analytical foundation I now actively apply every day. I constantly apply the data-driven thinking I developed through modules in machine learning, optimisation, and systems design, and transform the technical into what I pitch in meetings. Building and testing algorithms during my dissertation taught me how to translate complex data into strategic insight, which is exactly what financial technology as an industry demands. I think it is vastly underestimated how many different streams and professionals use different languages and code for various regions, and just like learning a new human language, to be able to program and understand technical projects is paramount to bridging gaps and building global teams.
What's one piece of advice you'd offer to someone considering studying your programme at Queen Mary – especially international students?
Come in with curiosity and an open mind. The programme gives you the freedom to shape your own direction, however, the value you siphon out of it depends on how much you explore. Don’t be afraid to experiment, talk to your professors, and turn your ideas into projects, even if they feel ambitious. One must consider yourself an entrepreneur in your studies. Network, collaborate, and push yourself. Queen Mary has an incredible environment for students who take initiative; if you use that freedom well, you will leave with both the technical skills and confidence to build something meaningful.