Literature review

Friday night in Montreal, with the streets being almost blocked by a snowstorm that left us half a meter of snow between our door and the outside world. It is also a night in which my next-door neighbours have decided to test the full extent of their speaker’s bass capabilities — currently playing a remix of Lana Del Rey’s Blue Jeans. It’s impressive to hear that they are accompanying the song during its high notes; perhaps it’s time to throw that neighbourhood karaoke party after all.

What’s also impressive is that this note has diverged so early on, from its first paragraph. This note is actually about a literature review of a set of pattern recognition and robotics papers, whose existence I think is important to know. I wrote this review as a partial requirement for my upcoming Ph.D. comprehensive exam at McGill. The purpose of the literature review is to summarize ~20 papers related to your research area, and the committee is free to ask whatever question they wish around these papers and their references, trying to see exactly what are the boundaries of the student’s knowledge and how this knowledge has been acquired: by memorization or by actual understanding. If the student is unclear about a method or an algorithm, this committee, which consists of some of the most experienced professors in the department, will most likely detect it and dig deep enough with questions to see exactly what the student does not understand and why.

My review document covers some methods in these broad topics:

  • Bayesian filtering and estimation
  • Sampling-based path planning
  • Clustering
  • Classification and ensemble methods
  • Active vision

The summary is quite dense, and admittedly one could spend an entire document focusing on one of these areas. I though it was going to be more fun though to write a compact document. I should clarify that this document has not been reviewed yet by the committee, so I haven’t received much feedback on it, and I don’t know exactly how many errors it contains. Notwithstanding that, I thought it is useful enough to share.




The American Scholar

Richard Hamming’s “You and your research” is an essay that is often recommended reading for graduate students, as a means of advice from an accomplished scientist. I agree with most of the points he raised: work on the important problems in your field, get the courage to try to solve them, be prepared, be patient and positive etc. There is one point that he made though, which has really bothered me from the day I first read the transcription of his speech:

I had incipient ulcers most of the years that I was at Bell Labs. I have since gone off to the Naval Postgraduate School and laid back somewhat, and now my health is much better. But if you want to be a great scientist you’re going to have to put up with stress. You can lead a nice life; you can be a nice guy or you can be a great scientist.

This really doesn’t make much sense to me. Why does willingness to compromise your health make you a more dedicated scientist? Is a sick or dead scientist better than a healthy one? Is constant stress and fear a prerequisite for being productive and creative? I don’t think they are.

I think I prefer Emerson’s “The American Scholar“.

Let the games begin

This week was my first week of classes at McGill and my second in Montreal. Being a grad student is quite enjoyable at the moment: I have more freedom in my course selection than when I was an undergrad, I get to explore the topics I’m studying in a greater depth than before, and generally the whole feeling of starting at McGill as a new student is quite exciting. Living independently in Montreal has its perks: a very vibrant and creative European-like city with a large student population, plenty of interesting activities to engage in and lots of opportunities to learn French. For now, I want to write about something other than Montreal, though, which I am sure is going to come up frequently in future posts.

My coursework for this semester, which I hope I have chosen so that it is neither too easy nor extremely time-demanding looks like this:

  • Computer graphics. There is no required textbook in this course either. The OpenGL Programming Guide is a recommended one, and I suspect it’s going to be quite necessary for our assignments, but I think I should also consult Fundamentals of Computer Graphics and some online articles and tutorials because I feel that the lecture notes leave me with many questions.

Needless to say that all of these courses require careful preparation and equal attention. I should also keep in mind that I’ll have to start reading a couple of papers each week to familiarize myself with the work that is being done at the Mobile Robotics Lab. I’m still not sure whether I should spend the rest of the time learning French or experimenting with something I haven’t tried before — I’ll just have to see how the following couple of weeks will be.