Sheng Xu is an undergraduate student studying mathematics and statistics at the University of Sydney. Although now he can not imagine studying anything else, the path he’s on today was not always so clear.
Back in primary school, he was interested in a different branch of science – astronomy. He fondly remembers telling his grandparents about neutron stars and black holes over dinner, impressing them with the amount of information he managed to regurgitate from the small astronomy book he had.
Near the end of primary school, his interests shifted towards video games. Not maths, but video games. Perhaps because elementary algebra was rather dull and playing around with equations was like a game, except not a very fun one. It was vastly inferior to the unimaginably large collection of games that the internet had in store for me.
It was not until around year 9 or 10 that he became really interested in maths. Learning to apply this little game of numbers to solving real, tangible problems, he suddenly felt drawn to it and quickly became hooked, and it was not long before he decided that maths was what he wanted to pursue. He was uncertain where this road would take him, but nonetheless he was determined to take it.
Fast forward to the present. He’s about to complete an undergraduate degree in maths and stats. Along the way, he has met many wonderful people, and discovered his passion for probability theory and applied maths. He is currently undertaking a research project on stochastic approximation and specifically, the acceleration of convergence of the adaptive importance sampling parameter in Monte Carlo simulations.
Acceleration Of Stochastic Approximation Parameter Search In Adaptive Monte Carlo Simulation
The goal of this summer project is to construct and analyze acceleration methods for the convergence of the importance sampling parameterto induce a faster convergence of Monte Carlo simulation.