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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