# Advice to PhD applicants

**Update** (28 January 2024): I am no longer taking new PhD students.

**For students who are interested in doing a PhD at Monash under my supervision.**

First, check that you satisfy the following criteria:

- You must have completed a degree in statistics that involved some research component (e.g., an honours or masters thesis). A degree in computer science, mathematics or econometrics might be acceptable if it contained a substantial amount of statistics. A degree in any other field is not sufficient background to work with me.
- You should have obtained the equivalent of first class honours in an Australian degree. How that translates depends on the country and university. But if you weren’t in the top two students in your cohort, then you probably won’t get a scholarship at Monash University.
- It is essential that you have studied some matrix algebra, multivariate calculus and optimization.
- You should be capable of programming with R to the point where you can write your own functions; if you can write in Python and C++ as well, even better. I don’t expect students to be whiz programmers, but I do expect them to know about for loops, if statements, local variables and functions, and I assume they have some idea about nonlinear optimization.
- You should be familiar with reproducible research practices including using github and Rmarkdown.
- You must be interested in research topics that interest me. If you want to work in finance or economics or electrical engineering, find a different supervisor. I mostly work on forecasting, analysing large collections of time series, anomaly detection, computational statistics, data visualization, exploratory data analysis, and energy analytics. Poke around my website to see the sorts of topics I work on. There’s no point asking to do a PhD with me if you want to do research on something I don’t know much about.

If you think you satisfy all of the above, then read the instructions on how to apply.

I do not expect that you have studied specific topics close to my research such as time series analysis, forecasting, nonparametric smoothing, machine learning, etc. If you have a solid background in statistics and mathematics, then you’ll pick up the necessary material.

Much of the first year of a PhD is spent in reading the relevant background literature and developing some necessary research skills. Most students have not produced anything publishable after one year, but they will usually have developed good research skills, have read a lot of papers and will be ready to start doing some research of their own.

Most students will need a scholarship. Applications for PhD scholarships at Monash close on 31 August each year. Check out the instructions for scholarship applications. Scholarships are highly competitive and we receive many applications from students around the world. You would normally need first class honours from an excellent university to be in the running for a scholarship. International students will also need to have satisfied the English language requirements.

If you’re thinking of applying in the next round, use the time between now and then to prepare — improve your R skills, revise your mathematics, read some research papers, and prepare a research proposal.

If you’ve read this far, and think that you have the background described above, then please send me an email including your CV, a transcript of your results, and a few sentences about the sorts of research topics which interest you.