Confessions of a researchaholic

March 6, 2016

About time management

Filed under: Real — liyiwei @ 4:27 pm
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A collaborator asked me about time management, in particular how to toggle between multiple projects.

I am not sure if I am any good at time management or more general if I am a normal person, but here is what I do.

Rule 1: do not block your collaborators.

Rule 2: do whatever you like otherwise.

During my NVIDIA days I belonged to a tight-knit group of around 40 to 50 GPU architects. GPU design was (and likely still is) tremendously complex, so anyone could potentially block everyone. When that happened, ATI got a chance to kill NVIDIA, and my colleagues got a chance to kill me. That is how and where I learned rule 1.
In general, prioritize your collaborators over yourself. It may sound counter-intuitive, but making others happier and more productive will make the entire team, including yourself, happier and more productive as well.

Rule 2 reflects my personal style: after making my collaborators happy, I make myself happy. I cannot be productive otherwise, and life is too short anyway. I believe most people over-plan, especially for creative and non-deterministic tasks like research. This is one main reason why I never schedule regular meetings or daily routines. If you have a hard time deciding what to do first, they are probably equally important, so just pick one and do it. Trust your instincts, and you will gradually learn how best to schedule your time (this could help, which I also learned from NVIDIA).

I am not familiar with other time management techniques like Pomodoro. I just took a quick look at that and found myself violating it badly. For example, if a distraction pops into my head, I follow it, to allow serendipitous encounters (unless I am right in front of a major deadline). My distraction stack is probably around 4 layers deep, i.e. I could recurse around 4 layers of inception without getting lost.

February 10, 2016

It might not be good to be a good student

Filed under: Real — liyiwei @ 5:12 pm
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It is usually not too hard for smart kids to perform well in schools; just excel in what you are told to do, such as taking courses.

This is a deterministic process with well-defined goals and tasks that reward smartness and hard working.

However, real world is chaotic and ambiguous. You have to figure out what to do, with shifting targets and ever-changing environments.

This is why school performance does not directly translate to real-life performance: the required mentality and skills are not the same.

This is also why being a good student might not be a good thing for you. You are so used to this deterministic input-output process that you might be very frustrated by the non-deterministic nature of the real world, when starting your first job or research project.

In contrast, not-so good students might adapt better to the real world, because they already have enough failure experiences and are not yet cast into conformity.

PS
I was lucky to be a student who was considered good in performance and bad in behavior.
🙂

February 1, 2016

The cat experiment

Filed under: Real — liyiwei @ 11:07 am
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Once, when I was around 9 or 10, I was visiting my aunt’s place.

One of the cousins, X, and I were standing near the swimming pool. The family cat walked by. Cousin X and I got into the discussion about whether cats can swim. I have seen a few dogs and at least one horse swam, so I was pretty sure the answer is yes (cats seem more agile). Cousin X disagreed (he is older but not necessarily smarter), so we decided to have a bet.

Clearly, the only way to settle the bet is to experiment, so I grabbed the cat and threw it into the pool.
(That was before the age of YouTube and Google, BTW.)

What followed was amazing, and happened like within a few milliseconds. The cat sprang on the water surface like a trampoline, and immediate landed back near my feet. It was dripping, so it clearly fell into the water, but I had no idea how it managed to jump back. Meanwhile, our debate remained unsettled.

I am trying to come up with a very concrete way to tell a new PhD student how to decide whether someone is suitable for (scientific) research. So here is my try. Let me know if you have better ideas.

Do you like to ask questions that seem interesting at least to you (e.g. whether cats can swim)?

Do you enjoy finding the answers yourself through investigations and experiments (e.g. grab the cat and throw it into the pool, and observe what happens)?

Are you very comfortable with the consequences, regardless of the outcomes of the experiments (e.g. the cat neither swam nor sank and my aunt beat me up)?

Can you do this continuously as a career? Imagine it is Friday lunch time, and all the works you have done this week have turned out to be failures (e.g. no other ways you have tried can tell you whether cats can swim).
You have no idea what is going to happen this afternoon when you try your 101th experiment with that cat.

If you hesitate for any of these questions or you think I am crazy, you are probably not suitable for research. At least, you will not be happy or successful.

Talent and personality are important; you have to be sufficiently smart and tough for research. But passion is even more important; the only way to be truly happy and productive is to do what you really like.

January 29, 2016

School versus job performance

Filed under: Real — liyiwei @ 4:29 pm
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How predictive is the school performance for the eventual job (and life) performance of an individual?

This is a very important question. The schools are supposed to educate what is actually useful. (But clearly that is not the case in practice.)

This is also a very broad question under perennial discussion.

In my personal experiences on the creative side of computer science (e.g. research and development for the cutting edge of graphics and HCI), there is a weak positive correlation between school and job performance (around 0.2 to 0.3 if I have to be numeric).
Good school performance reflects positive traits such as talent and work ethics, but also negative traits such as conformity, lack of creativity, and risk aversion.

This is why standard statistics, like grades, schools, and rankings, are not enough and sometimes even misleading. We have to look at more practical evidence, such as publications, projects, and recommendations.

This is also why recruiting top students and employees is very challenging. Top schools and companies do have advantages in attracting top talents, but we only get what we look for. Many of the best people I have worked so far had been bypassed by the traditional screening standards. Conversely, I have also seen many weak people in top institutions.

Maybe one day data analysis and machine learning will solve this problem.
Before that, I rely on the good old way of people reading.

January 24, 2016

How to deal with scoops

Filed under: Real — liyiwei @ 11:16 am
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Ideas that can be easily scooped are probably incremental.

Just move on to a better idea, and use that to beat those who steal your idea.

And be careful with whom you share ideas in the future.

December 9, 2015

The PhD grind

Filed under: Real — liyiwei @ 6:10 pm
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I bumped into this PhD student memoir by Philip Guo, and liked it so much that I read the entire book within a few hours.

I highly recommend it to anyone doing research, especially junior PhD students.

There are many advices out there about research and PhD, but this memoir format provides concrete events that are easier to relate on a personal level.
It also helped that the fields covered are HCI and software engineering, which every CS major can understand to some degree.

In retrospect, I hope to have written something similar around the time of my PhD study. Back then I simply had too much fun for this, and I probably have too much selective bias now to write a genuine one.
But if you can write one, I would love to read.

December 3, 2015

How to judge potential conflict with multiple projects

Filed under: Real — liyiwei @ 6:29 pm
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Working on concurrent projects with sufficient similarity may cause potential conflict of interests.

The way I will judge it is asking the following question: how likely is it for me to come up with an idea that can help both projects in a non-trivial way? If the answer is yes, then I might be in conflict, as I will have to choose which project to use that idea.

November 30, 2015

How to write papers

Filed under: Real — liyiwei @ 7:55 pm
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Just like many other skills, the more you do, the better you get.

Writing anything is better than writing nothing. It is an iterative process. The readers do not care (and cannot tell) how many iterations you have made, or how crappy the earlier versions were.

Read good papers, and learn their styles.
Look at suggestions (books, articles, online tutorials, etc.) on how to improve writing.

Aside from telepathy and telekinesis, any other form of external communication has inherently narrower bandwidth than your internal brain circuitry.
The challenge is to figure out what you know that others don’t, and effectively communicate these.
(I used to think that teaching is orthogonal to research. Now I realized that both rely on the above, after being a prof.)

What you want to write might not match what you really have written. To detect this discrepancy, flush your brain cache as follows. After having a draft, leave it there until you have forgotten most of it. Then look at it again.

When you have only minor updates between revisions, show your draft to other people for comments. Ask them to be honest and brutal, like reviewers.

November 20, 2015

How to have a bad research career by David Patterson

Filed under: Real — liyiwei @ 7:24 pm
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The filename suggested the seventh version, but I still learned new things from it just like the previous ones.

David Patterson did warn about his system bias, though.
In particular, I have different experiences with “big teams” and “technology transfer”.

The number of collaborators should be naturally proportional to the scale of the projects, which in turn depends on the fields.
System architecture naturally involves various experts from PL/compiler, OS, architecture, VLSI, and networks, but in my fields (graphics, vision, and HCI) the most influential papers are usually authored by one or two people, and seldom more than four.

It is difficult for companies to adopt new system architecture, but much easier for them to “steal” algorithm ideas. It cannot hurt to protect your IP before going out selling your ideas; you can quickly file provisional patent applications at low cost without involving any lawyers.

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