Tag Archives: John Wright

John Told Me …

… the thing that i’m trying to convince you of is threefold:

(i) in terms of ability and work ethic, you have everything you need to be successful doing theory research (or any other kind of research)  — I’m happy to hear that

(ii) if you want to be successful doing theory work, you’re going to have to modify somewhat the  standard that you set for yourself, when you judge whether you know something or not … you have to be willing not just to know about things, but to put in the effort to understand them down to the core … to demand complete clarity of yourself … a good test is whether you can teach the material, say to your labmates.

(iii) if you set that standard for yourself, and follow up on it, the progress that you make will be extremely rapid (probably much faster than you think) — the reason for this is that when you learn one thing thoroughly, you also learn about how to learn — you’ll eventually be able to pick up new ideas and understand them thoroughly much more quickly.


Thanks to John for your encouragement!

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Quick Updates

First of all, I’m doing fine at MSRA, Beijing. I’ve started to look at theoretic side of compressive sensing, matrix completion, and dictionary learning, facing a rather steep learning curve with math of probability and matrix analysis… John is a born researcher and teacher! We’re holding daily meeting and discussing a lot.

My ICDM 2010 submission was rejected days ago, with one reviewer saying that (kind of) “the paper has nothing to do with data mining”. Gosh! That’s perhaps the worst news for people doing data clustering. I have listed the paper manuscript and a partially revised version under my preprint section and also sent the manuscript to the arXiv server. Recently I shall write an expository blog article and explain the technical content inside the paper.

Before that, let’s test the great tool written by “in theory” (well, not his true name) LaTeX2WP, which has received the honorable mention in Terry Tao’s blog and seems to have helped Terry a lot. I’m planning to use this a lot for forthcoming blog articles, and let’s test about the most famous equation by Einstein (in fact, this whole piece of words today are converted by LaTex2WP):

\displaystyle  E = MC^2. \ \ \ \ \ (1)

Last but not the least, it’s soon the Mid-Autumn Day, a great day for Chinese people to gather and reunify. Looking forward to the planned party of my high-school classmates on 23rd Sep!

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