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My interests in Computer Science began from the front-end, drawing simple shapes in Scratch. The sense of excitement was gained from the shapes created through my design of logical steps. I further studied other languages such as HTML and PHP for website-designs, SQL for databases and C/C++ and Java for back-end. Having grasped the fundamentals of front-end and back-end languages, I challenged myself with the first-ever project, making a shopping website in PHP with SQL database. By presenting the top 3 sellers in a table, I wanted my program to be a unique one from everyone else’s in my class. From taking hours and hours, I figured out how to list the goods in descending order within 50 lines of code. On the next day, I showed my code to my teacher with pride; however, my teacher solved it in one line by writing the SQL code “order by desc”. This kind of experience may disappoint others. However, interestingly, I was not disappointed or let down. Instead, this moment excited me and further ignited my curiosity in Computer Science. This taste of programming in real life situation confirmed my desire to study Computer Science. Nonetheless, this is just the beginning of the program that I want to create in the future. I am eager to understand and, why not, develop real-life impact programs such as AlphaGo, which effortlessly beat humans in ‘Go’, the game which has more possible positions than the number of atoms in the universe. In order to attempt creating such amazing software, I thought learning efficient algorithms is important because the resources given out are finite. I realised that there is is a difference between solving the question correctly and solving it efficiently. Using Cormen’s ‘Introduction to Algorithms’ as a guide, I ascertained creative ways of interpreting logical problems. For example, Activity-Selection Problem, to find the maximum subset of non-overlapping activities, was an acute revelation of how a simple modification to a method can result in decreased complexity and increased efficiency: the exhaustive search requires complexity of O(n^2), whereas you only need complexity of O(n log n) if the greedy method is used. Moreover, there are algorithms such as Dijkstra, Floyd-Warshall or Bellman-Ford which have a tremendously great impact on our lives these days, being used in different versions in all the big transport applications. On the other hand, there are areas I do not understand such as optimal network flow algorithms. The reason why changing the directions of the graph leads to the solution seems sensible, but I cannot fully understand the mathematical proof behind it. There are many other Computer Science algorithms and theories I likewise do not entirely understand and therefore I want to explore deeper. Although I like to play with computers, I am not a person who just learns in front of desks. Attending a semester of the second-year course, ‘Programming Principles’, at the Queensland University of Technology gave me a sense of Computer Science course in university, which I found that I can cope well at university. I achieved the first place in Regional Maths Team Challenges and International Competitions and Assessments for Schools Mathematics in Queensland for Mathematics. I participated in the Australian Informatics Olympiad and Computational Algorithmic Thinking Test for Computer Science. I also was a STEM ambassador in school who encourages and supports others to study Mathematics and Technologies. These experiences would allow me to study in a supportive and competitive environment. I enjoy creating programmes that work. I want to surround myself with the best people to compete and learn from each other to develop myself into a more complete computer scientist. I very much look forward to studying Computer Science in university and relive those moments of the excitement of learnings when I found how 50 lines of code became 1 line of code.
This is a successful statement written under the previous UCAS format. From 2026 entry, the personal statement is three separate answers. Below is a worked example — Economics — showing how a strong answer to each of the three questions reads:
Volunteering at a Saturday food bank in Croydon, I helped a single father of three who had moved into temporary accommodation after a zero-hours contract collapsed. What startled me was not the hardship but the bureaucracy — he had been waiting six weeks for Universal Credit because a payment glitch had reclassified him as self-employed. A Sutton Trust talk by Stephen Machin pushed me from that observation toward the literature on benefit take-up and conditional cash transfers — first Banerjee and Duflo's evidence from rural India and Mexico, then Anna Aizer's recent NBER work on the design of welfare-to-work programmes in the US. I want to study Economics because the question running through these readings — why some welfare systems escape the dependency trap while others reproduce it — has rigorous answers that combine empirical microeconomics, mechanism design, and political economy in a way no other discipline can.
Further Mathematics has been the single qualification that has reshaped how I read Economics. Working through the chapter on differential equations let me approach Solow's original 1956 paper rather than the textbook simplification — and seeing convergence as an asymptotic result rather than a guarantee made me sceptical of growth-theory papers that quote "China is converging to the US frontier" without specifying the underlying production-function assumptions. That scepticism became my EPQ. Building on Acemoglu and Robinson's Why Nations Fail and Elhanan Helpman's The Mystery of Economic Growth, I asked why the post-1978 Chinese growth episode fits neither the standard Solow convergence prediction nor the institutional pessimism of the extractive-institutions framework. My answer, framed around within-country variation in property-rights protection across coastal special economic zones, was the most rigorous piece of academic work I have produced. Economics A-level supplied the discipline-level vocabulary — perfect competition, Pareto efficiency, deadweight loss — but the model that has stayed with me is the prisoner's dilemma I first met in Maths Olympiad preparation, which I returned to repeatedly when reading Schelling's The Strategy of Conflict. In History A-level, the unit on the Bretton Woods system gave me the institutional context for Friedman's 1953 essay on flexible exchange rates — a connection that taught me how economic theory and political choice constantly co-evolve.
For the John Locke Institute 2025 Economics paper I argued — against my initial intuition — that personalised pricing is welfare-improving in aggregate, drawing on Pigou's first-degree price-discrimination framework, Hal Varian's 1989 paper on price discrimination, and the more recent empirical work by Dubé and Misra on ZipRecruiter's pricing experiments. Writing the essay forced me to take seriously the strongest version of the fairness objection — Sandel's argument in What Money Can't Buy that markets reshape the goods they price — and then to build a synthesis that conceded the moral worry while defending the consumer-surplus calculation. I came out of the process convinced that the welfare economics taught at undergraduate level is more subtle than the textbook indifference curves let on. Outside the essay competition, the book that has shaped me most is Katharina Pistor's The Code of Capital. Pistor's argument that property law constructs rather than merely protects economic value runs counter to the standard market-design literature I had been reading, and I have spent two terms working through her chapters on collateral, debt, and shareholder rights alongside the Acemoglu-Johnson critique she draws on. To test the framework against my own context, I built a small dataset on Hong Kong residential property using the publicly released Land Registry data and computed the share of the housing stock held by corporate (rather than personal) entities — a 32-line Python project that taught me more about institutional economics than any chapter I had read on it.
Up to 4,000 characters across the three answers, with a minimum of 350 each. See the official UCAS guidance.