The View from the Foothills

I'm finally there. Five years of hard work, first as an undergraduate and then as a Masters student, have paid off. Last Monday, I was granted my rightful place in academia... on the lowest rung of the ladder.

Yes, PhD students are a dime a dozen, even in my small institute, and despite the excitement of starting research in earnest, I can't help but feel slightly apprehensive. This may just be the result of reading too many PhD comics, but a tinge of anxiety is setting in. What if my advisor turns out to be a workaholic? What if I can't finish in the required 3 1/2 years before my funding runs out? What if my office mates are insane (they're not, I think) or my experiments all fail?

Then I remember that a million students have survived their PhD just fine before me and a million will again. I may not have the prettiest office (in fact, drab is not an inaccurate description), but at least I'm not sharing with 14 other people like my flatmate. My supervisor has only been nice to me, despite the bollocking that he gave his other PhD student last week. And my project, even though it looks daunting from here, will rest on the foundations of my Masters project, meaning that I have a reasonable idea of where to start.

So, the base camp has been established in the foothills of Mt. PhD. Only the future will show if I scale the summit triumphantly, or freeze to death in a crevice somewhere. Boy, that metaphor took a bleak turn, didn't it?

Research is Easier if You Make It Up

Yes, I know I haven't written in a good few months. In my defence, I have been kept quite busy by the research for my MSc project. Now that it is done, however, I'd like to share a few thoughts on my first real experience with research.

For this first post, I want to talk about what was perhaps the most humbling experience, and that was how tempting it was to cheat.

Like many research projects, my research was beset with problems. There were contradictory results, vague results, results that were the opposite of what we expected, without any indication why this happened. And often, when I got these results, I would think: "Gee, wouldn't it be nice if I could make up the results I wanted instead."

Now before you cast the first stone, let me be very clear: I did not fake any results, nor will I hopefully ever do so. But it got me thinking. How easy would it really be to fake results? For my MSc project, it would have been really easy. We do not have to hand in the code (although it is possible that the markers may ask for the code if they smell a rat, but let's assume for the sake of the argument that the faked results are completely convincing), so I would not even have to write the programs. I knew how the different experiments were supposed to work, so generating some convincing results would have been easy. The only people to see the results are my supervisor and a second marker. Of those two, only my supervisor could possibly spot fake results, because the second marker is not an expert in the field. If I had gotten any fake results past my supervisor, I would basically be home free.

You might be thinking that that's all very well for a Masters project, but surely in real research faked data would be spotted. But would it really? I agree that you would probably have difficulty faking a whole project: You'd be hard-pressed to answer questions from reviewers of your paper, and anyone trying to repeat the experiments would obviously get very different results. But what about just tweaking that one experiment that's poking a hole in your theory? That would again be very easy and would probably not be spotted unless somebody decides to repeat that exact experiment. If somebody later disproves your theory, well, you got a paper out of it, and nobody can really blame you for not spotting the flaw when all of your experiments were confirming the hypothesis.

Cheating can get even more subtle (choosing your experiments, skimping on controls, omitting results) and harder to spot. So the question is, given how easy it would be to cheat, what, other than personal integrity, is keeping scientists honest?

I believe curiosity and ambition are big factors. If you get results that contradict your hypothesis, you don't just say "Aw, crud", you get excited, because there's another problem to solve. Maybe this new problem will lead to an even bigger discovery than the one you were hoping to make. If you just fake the result, you'll probably never do really ground-breaking science. Worse yet, you might set back other scientists who will not pursue their theories because your "results" seem to have disproved them.

There's also training and your research environment. Never underestimate social conformity, which in this case is a good thing. If everybody around you is excited about research, as most scientists will be, you'll find it very hard to be the cheater, even if you're the only one who knows that your results weren't real. You'll want to be just as good as the rest, and if they can deal with contradictory results, then so can you.

Of course, this only applies if people the people in your research environment let you know about the problems they were having. They may be competitive people who feel that talking about struggling with research is equivalent to showing weakness. If that is the case, I recommend reading some of the many excellent blogs from scientists who are not afraid to talk about their research issues.

One thing that is clear is that you cannot just assume that every result that is published is automatically set in stone. If you think you have a better theory, test it, and if necessary repeat an experiment that has already been done. If enough people do that there might actually be a chance of demasking the cheaters. And that would be another great incentive not to cheat in the first place.

Conference Noises

Would you say that a scientists first conference is like his first kiss*, a unique experience, never forgotten despite the fumbling and nervousness? Or is it more like the first time you went to a McDonald's: Sure, it's exciting and colourful, but after you've been a few dozen times you notice that they're all the same.

I couldn't say yet which of these is a better description, since I've only just experienced my first conference. Conference might be saying a bit much: It was a one-day symposium, and I didn't even have to leave the city.

Still, there were some memorable experiences to be made. Some were of the mundane variety: It seems that even in Britain, coffee break means coffee break, and not tea break. And don't even dare ask for water. Also, pinning your badge to your shirt is a fashion faux-pas; the correct place is discreetly on your belt.

The poster session was different from what I expected, because there were really only posters. Somehow, I always expected the poster creators to be standing next to them with proud smiles, eager to explain their science to anyone passing by. Not so here: There were posters, there were people reading the posters, and that was it.

The talks ranged from the fascinating to the mystifying. I've always been better at learning things from papers than at picking them up in lectures, so it's no surprise that I couldn't follow some of the more complicated topics. Listing to those lectures was not a waste of time, though, since at least now I know those topics exist and I can find out more about them (by reading papers!) if I want to.

The quality of the speakers varied (doesn't it always?) but some of them were very good, even inspirational. There are so many unsolved problems in bioinformatics, but these speakers were pointing the way to solving many of them.

Now for the more disappointing part of the symposium. No, not the food, that was alright. This is something that I'm willing to be not many attendees even noticed, but it's actually a huge statistical fluke if it was random: Out of 15 speakers, not a single one was female. I'm used to gender bias in my field, especially on the informatics side, but 0 out of 15? Seriously? You're telling me that there's not a single female professor that you could have invited to talk about her research?

At least many of the people in the audience were female, but jeez!

*With the first conference occasionally preceding the first kiss by a while.

Talking the Talk

And here's another post that I'm stealing from Of Two Minds: How to Give a Bad Science Presentation.

Of course, the advice they give applies to presentations given to fellow scientists, with the objective of introducing your work to them. And in that particular scenario, I probably agree with everything they say.

However, what if the aim of the presentation is not to inform, but to educate? In other words, what if you're giving a lecture? This is very topical for me, as I've just finished a course where students were giving presentations on papers, and I've had to do one of the presentations myself. We disregarded most of the rule they came up with. Were we right to do so? Well, let's look at the rules:

- Be able to give the presentation without support of the slides.

That one's a tricky one, because we were explaining a technique. In my part, I was heavily relying on examples to explain what was happening, and those examples were all on the slides. Could I have done it on the blackboard? Probably, but not without taking considerably more time. Still, we did rehearse a few times, so I think we could have brought the point across even without the slides. Overall, this rule holds.

- No outlines on the slides

Now this I can't completely agree with. Sure, giving an outline is slightly superfluous when you're repeating what it says on the slides. But if you're trying to get an unfamiliar topic across to an audience, reinforcement helps. During the presentations by other groups, I often found myself referring back to the slides when I hadn't caught what they were saying. I think outlines have their place in lecture slides.

- The less text the better

Two problems with this one: The first is the point that I just raised that it helps to refer back to the slide if you missed or were confused by what the speaker was saying. The second is that sometimes, the slides are made available to the audience as a study help before or after the talk. They effectively double as lecture notes, and so it is helpful if they contain enough detail so that you can understand them without the help of the speaker.

On the other hand, too much text can indeed be distracting during the presentation. So I'd advocate a compromise solution here: Keep the slides sparse, but provide detailed lecture notes at the end. Unless you're confident that your speaking ability is good enough to allow your audience to follow along easily and take notes while they do.

- Let us see the data

No argument there: Figures should be clear and big enough so that the audience can get a sense of what it is you're trying to show.

Woe is PhD

So I'm looking for a PhD place. Ideally, I'd like it to be at my current university, as the other universities in the UK that are good in my field are mostly located in London, and I don't like that city much. Nor can I really afford to live there.

I'd like it to involve Bioinformatics, but not require any wetlab work that I would have to do myself. There should also be scope for applying Machine Learning techniques. There's two areas of research that interest me. One is work in genetics, such as gene regulation modelling, or protein structure and function prediction. The other is to model biological systems at the macro level and predict how changes in the environment influence animal population size or plant growth.

I could also see myself doing a straight Machine Learning project without any Bioinformatics involved, but with slightly less enthusiasm.

I'd like to have a supportive supervisor, who I can talk to before I start my PhD and who will advise me on how to write up my project proposal. He or she doesn't need to be an academic superstar, but a fair number of puplications and at least some amount of recognition in either Bioinformatics or Machine Learning would not go amiss.

I'd like to get the chance to teach during my PhD, either tutorials or even lectures.

A scholarship would be helpful. If I don't get one, my parents could help, but I'd like to be able to support myself for once.

And tomorrow, I will meet with a potential supervisor who might be able to offer the place that has most of these characteristics. (I'm not sure about the teaching yet.) Fingers crossed!

Reviewing Fun

One of the required courses for my Masters is a literature review on a topic which we can choose ourselves. So I've been reading lots and lots of papers (on Bayesian networks for modelling gene regulation, in case you want to know), and the more I read, the more I can see certain common themes emerge. Not common themes about the topic, mind you, but just about papers in general.

First of all, most papers can be summarised pretty easily. However, the summary I would come up with almost never matches with the abstract that the authors wrote. I realise that this is a function of their desire to show every aspect of the paper in their abstract, while I would summarise the most important ones (which might be subjective), but I'm still left with the feeling that most abstracts are not reflective of the gyst of the paper.

Secondly, too many papers overuse references. I've read papers where there's two pages of text and three pages of references. What especially ticks me off is when the mentions a topic and then gives five references for it. We don't need five references, we need one good reference. Maybe two if there are two particularly good papers and you can't decide. Five is just overkill.

Thirdly, and finally, I've noticed a distinct lack of detail in some explanations. Now this is something I can understand if you're trying to boil down a paper to two or three pages for publication. But if you're going to gloss over something, at least say that you're doing so. Also, since this is the 21st century, how about providing a link to your webpage where more detailed information can be found?

Sandra Porter over at Discovering Biology in a Digital World has some computer woes from her Bioinformatics class to relate. All I can say is, I'm glad our Bioinformatics course is run by the Informatics department. Although so far, computer use for that course has been minimal.

What's So Special about the Humanities?

The school finally showed some mercy and moved us to a new lecture theatre for the Probabilistic Modelling class. Not only does this mean that we no longer have to suffocate in a small room, but the new venue is also in one of the old buildings of the University.

I've very seldom been in the old buildings, with the exception of special occasions like exams and graduations. Mostly, these buildings only house the Humanities as well as the School of Law and the School of Medicine.

It's a complete change of scenery. Where we get ugly buildings from the seventies and eighties, with sparsely furnished, functional lecture theatres, the other schools are housed in huge stone buildings with marble arches, balconies and skylights. I mean, I get it, they have been around longer than Science and Engineering, but seriously, would it kill the University to at least give us some lectures in nice surroundings?

Assignment Time

I just heard that the first assignment for this year will be about spam detection. Ironically, spam detection is exactly the topic that I was looking at late last year when choosing a project for the Google Summer of Code. Now if I had actually got off my behind, put together a proposal and done that project, this first assignment might be a breeze. Oh well.

How Quaint

I don't know about you, but I barely remember web searching without Google. So it's kind of quaint to see the humble beginnings of Google in this paper from around 1997, The Anatomy of a Large-Scale Hypertextual Web Search Engine:

Search engine technology has had to scale dramatically to keep up with the growth of the web. In 1994, one of the first web search engines, the World Wide Web Worm (WWWW) [McBryan 94] had an index of 110,000 web pages and web accessible documents. As of November, 1997, the top search engines claim to index from 2 million (WebCrawler) to 100 million web documents (from Search Engine Watch). It is foreseeable that by the year 2000, a comprehensive index of the Web will contain over a billion documents. At the same time, the number of queries search engines handle has grown incredibly too. In March and April 1994, the World Wide Web Worm received an average of about 1500 queries per day. In November 1997, Altavista claimed it handled roughly 20 million queries per day. With the increasing number of users on the web, and automated systems which query search engines, it is likely that top search engines will handle hundreds of millions of queries per day by the year 2000. The goal of our system is to address many of the problems, both in quality and scalability, introduced by scaling search engine technology to such extraordinary numbers.

What is this "Altavista" thing they keep talking about?

Some Thoughts About Recaps

So today's course on Probabilistic Modelling started, not surprisingly, with a recap of basic probability theory. I'm not objecting to that, and there were clearly people in the class who have not done probability before. However, I had the same recap last year for a Modelling and Simulation course, and the year before that for an Artificial Intelligence course.

The repetitiveness of it got me thinking: Why waste time on all these separate recaps? Wouldn't a much more elegant solution be to organise one class of one or two hours a year which recaps probability theory, and everybody who needs it could go there?

Sure, there might be scheduling problems, but it still seems better than to subject everyone to the same recap, and force three different lecturers to teach the same material at roughly the same time. And I'm sure probability theory is not the only subject that recurs frequently in recaps.