Mathematics is often seen as a school subject full of formulas and rules, but for me it has always been something more personal. It is a way of thinking about problems, patterns, and decisions in everyday life. That same mindset led me to my summer research project, where I explored how mathematicians and statisticians use simulations to solve problems that are too complex for standard methods.
I fell in love with mathematics very early in life. One of my earliest memories is helping solve a maths assignment for my older brother before I could even write properly myself. Since then, maths has never felt like just another subject at school. It became the way I naturally approach the world.
I see maths everywhere. When I cook, I think about timing and efficiency: what should go into the pan first, and how can I make everything finish at the same time? When I play tennis, I think about patterns, angles, and decision-making under pressure. Even everyday choices, like saving money or managing time, feel like small optimisation problems. Maths taught me that behind many situations, there is structure waiting to be understood.
That way of thinking is what drew me to my summer research project in simulation-based inference, or SBI. SBI is a set of methods used when a statistical problem is too complicated to solve directly. Instead of writing down an exact answer, we simulate many possible outcomes and learn from them. It is a bit like trying to understand the rules of a game by playing it over and over again.
My project focused on a fairness issue in an important SBI benchmark paper. The benchmark compared several methods, including modern neural approaches, but it did not include regression adjustment for Approximate Bayesian Computation (ABC). Regression adjustment is a well-known improvement that can significantly strengthen ABC, so leaving it out made the comparison feel incomplete.
This became the starting point for our project: implement regression-adjusted ABC, add it to the benchmark, and test whether the comparison changed. In other words, we wanted to ask a simple question: are we comparing these methods fairly?
For me, that is one of the most beautiful things about mathematics. It is not only about getting answers. It is about asking better questions.
Stephen Dang
Queensland University of Technology
