Blogs about research

If you find this blog helpful (or even if you don’t but you’re interested in blogs on research issues and tools), there are a few other blogs about doing research that you might find useful. Here are a few that I read.

I’ve created a bundle so you can subscribe to all of these in one go.

Of course, there are lots of statistics blogs as well, and blogs about other research disciplines. The ones above are those that concentrate on generic research issues.

Advice to PhD applicants

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

First, read the instructions on how to apply.

Second, 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. In particular, please note that I’m not really interested in finance or economics. There are some excellent researchers at Monash on both topics, but I’m not one of them.

If you’re still interested, here is what I normally expect. You should have a strong background in statistics or econometrics (at least honours or Masters level) along with some mathematics and computing. It is essential that you have studied some matrix algebra, multivariate calculus and optimization. You should be capable of programming with a high level language such as R or Matlab; if you can write in C as well, even better.

Students who struggle either find they don’t know enough mathematics (or didn’t pay attention when they learned it), or they don’t know enough computing. 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.

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

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.

I expect all my PhD students to have read all of the archives of this blog (even the jokes page) and to subscribe to new posts. The primary purpose of the blog is to discuss research issues that students working with me should know about.

Most students will need a scholarship. Applications for PhD scholarships at Monash close on 31 October 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 — learn R, revise your mathematics, read some research papers, and prepare a research proposal.

How to fail a PhD

I read an interesting post today by Matt Might on “10 reasons PhD students fail”, and I thought it might be helpful to reflect on some of the barriers to PhD completion that I’ve seen. Matt’s ideas are not all relevant to Australian PhDs, so I have come up with my own list below.  Here are the seven steps to failure.

1. Wait for your supervisor to tell you what to do

A good supervisor will not tell you what to do. PhD students are not meant to be research assistants, and a PhD is not an extended undergraduate assignment. So waiting to be told what to do next will usually get you nowhere.

By the time you graduate with a PhD, you are supposed to be an independent researcher. That means having your own ideas, setting your own research directions, and choosing what to do yourself. In practice, your supervisor will usually need to tell you what to do for the first year, but eventually you need to set the research agenda yourself. By the third year you should certainly know more about your topic than your supervisor, and so are in a better position to know what to do next.

2. Wait for inspiration

Sitting around waiting for great ideas to pop into your ahead is unlikely to work. Most of my best ideas come after a lot of work trying different things and becoming totally immersed in the problem.

A good way to start is often to try to replicate someone else’s research, or apply someone’s method on a different data set. In the process you might notice something that doesn’t quite work, or you might think of a better way to do it. At the very least you will have a deeper understanding of what they have done than you will get by simply reading their paper.

Research often involves dead-ends, wrong turns, and failures. It’s a little like exploring a previously unmapped part of the world. You have no idea what you’ll find there, but unless you start wandering around you’ll never discover anything.

3. Aim for perfection

Perfection takes forever, and so students who are aiming for perfection never finish. Instead they spend years trying to make the thesis that little bit better, polishing every sentence until it gleams. Every researcher needs to accept that research involves making mistakes, often publicly. That’s the nature of the activity.

Don’t wait until your paper or thesis is perfect. Work through a few drafts, and then stop, recognizing that there are probably still some errors remaining.

4. Aim too high

Many students imagine they will write a thesis that will revolutionise the field and lead to wide acclaim and a brilliant academic career. Occasionally that does happen, but extremely rarely. A PhD is an apprenticeship in research, and like all apprenticeships, you are learning the craft, making mistakes, and you are unlikely to produce your best work at such an early stage in your research career.

It really doesn’t matter what your topic is provided you find it interesting and that you find something to say about it. Your PhD is a demonstration that you know how to do research, but your most important and high impact research will probably come later.

My own PhD research was on stochastic nonlinear differential equations and I haven’t touched them since. It showed I could do high level research, but I’d lost interest by the time I finished and I’ve moved onto other things. Few people ever cite the research that came out of my PhD, but it served its purpose.

5. Aim too low

My rule-of-thumb for an Australian PhD is about three to four pieces of publishable work. They don’t have to actually be published, but the examiners like to see enough material to make up three papers that would be acceptable in a reputable scholarly journal. Just writing 200 pages is not enough if the material is not sufficiently original or innovative to be publishable in a journal. Pointing out errors in everyone else’s work is usually not enough either, as most journals will expect you to have something to say yourself in addition to whatever critiques you make of previous work.

6. Follow every side issue

Just because you use a maximum likelihood method, doesn’t mean you have to read the entire likelihood literature. Of course you will learn something if you do, but that isn’t the point. The purpose of a PhD is not so that you can learn as much as you can about everything. A PhD is training in research, and researchers need to be able to publish their findings without having to be expert in every area that is somehow related to their chosen topic.

Of course, you do need to read as much of the relevant literature as possible. A key skill in research is learning what is relevant and what is not. Ask your supervisor if you are not sure.

7. Leave all the writing to the end

In some fields it seems to be standard practice to have a “writing up” phase after doing the research. Perhaps that works in experimental sciences, but it doesn’t work in the mathematical sciences. You haven’t a hope of remembering all the good ideas you had in first and second year if you don’t attempt to write them down until near the end of your third year.

I encourage all my students to start writing from the first week. In the first year, write a series of notes summarizing what you’ve learned and what research ideas you’ve had. It can be helpful to use these notes to show your supervisor what you’ve been up to each time you meet. In the second year, you should have figured out your specific topic and have a rough idea of the table of contents. So start writing the parts you can. You should be able to turn some of your first-year notes into sections of the relevant chapters. By the third year you are filling in the gaps, adding simulation results, tidying up proofs, etc.

Research supervision workshop

Today I gave a workshop for supervisors of postgraduate students. Mostly I talked about creating a team environment for postgraduate students rather than the traditional model (at least in statistics and econometrics) of each student working in isolation.

The slides are available here in presentation form or in handout form. Actually, these are an edited version of the slides as I accidentally left out a couple of the photographs in the workshop, and I’ve omitted slides that I didn’t end up covering in the workshop.

An important part of my research group is this blog. So if you haven’t been here before, please take a look around.

For those people who attended, feel free to add comments below if you would like to provide feedback on the workshop.

Supervision award

Last night I received the Vice-Chancellor’s postgraduate supervision award at a function at Government House. I am deeply honoured that my students thought to nominate me for the award.  I think I was as surprised as anyone to win, and some people have asked me what I did to deserve it. Actually, I’m not sure that I did deserve it, but I can tell you what I told the award committee who chose me.

I was asked to write a document explaining my approach to supervision. My students and colleagues also had to contribute comments, which are not reproduced here. This is what I wrote . . .

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