Meet the speaker: Dr Giang Nguyen
Chess enthusiast Giang Nguyen will be at Summer School 2016, she spoke to us about her research interests, who influenced her decision to study mathematics and advice for students considering a career in the field.
1) What are the most interesting “big questions” in your field? And what kind of problems are you interested in broadly in the field? (maths as a whole)
My current research areas are under the umbrella of Applied Probability. Some of the big questions in this field include (a) developing numerical methods for stochastic differential equations and other continuous-time, continuous-space stochastic models, (b) rare-event simulations, (c) simulation optimisation, and (d) computing stationary performance measures of stochastic systems. Topics (a), (b) and (d) interest me the most.
What are some other areas of maths that are particularly interesting to you?
Besides Probability, Discrete Mathematics is my other great love, in particular Combinatorics and Graph Theory. I spent my Honours and PhD years studying the Hamiltonian Cycle Problem using tools from both Discrete Mathematics and Probability. Elegant and centuries-old, the HCP is connected to the Traveling Salesman Problem and, more broadly, to the unsolved P vs NP problem.
Why did you become a mathematician?
Growing up I got a lot of exposure to mathematics, as my father was a theoretical statistician. When it was time to decide my university major, I was fortunate enough to meet Professor Jerzy Filar, who encouraged and guided me through my undergraduate and postgraduate studies. Without my father and Professor Filar, I wouldn’t have become a mathematician.
Do you have any advice for future mathematicians?
When asked if he had any advice for up-and-coming chess players, the late Australian mathematician Greg Hjorth said, “Floss *before* brush.” I think that advice is good for future mathematicians as well.
Also, make sure you keep learning new tools while consolidating your expertise, because great results usually come from making unexpected combinations of insights from different areas.