Confessions of a researchaholic

April 20, 2016

Hypes

Filed under: Real — liyiwei @ 8:47 pm
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Nowadays a quick way to filter a job/school application is to see whether and how it says the candidate wants to do machine learning.
(Some neural network probably already existed precisely for this.)

Machine learning by itself is not the problem (quite on the contrary).
The problem is whether you can even form your own independent opinions.

When something (investment, technology, or research field) becomes hot it is already too late to bandwagon.
Those pioneers you see today started (and stuck to) their stuff when it is not yet hot.

Stick with your passion, belief, and opinion might not lead to success, but at least you can have fun, face less competition, and success/fail in your own style.

And if you are smart and creative enough you can have the cake and eat it.

Say your expertise and/or interests are about user interface design. But you also want to do some machine learning like everyone else.

You can switch field, and compete with a lot of smart people who have more passion and knowledge.

Or you can stick with user interfaces, and use machine learning to make them better. You can pick up something new without ditching what you already have.

April 12, 2016

Research coherence

Filed under: Real — liyiwei @ 7:31 pm
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One common advice on research is to have a coherent theme among our papers. I heard this from a bigwig around 2003 after getting my PhD.

This is one of these advices that I agree in principle but have violated in practice.
=D

Yes, coherence can help recognition from the community, especially when one enters a new field.

However, I am not sure if this should be intentionally aimed for. Unless you are extremely smart and versatile, you are likely end up doing related stuff without even trying.

There is this implicit force that drags us towards similar, and thus incremental, ideas. We should fight against this force, not follow it.
So, just do whatever you like. You will have more fun and more likely to produce novel stuff which, even if lacks coherence, beats being incremental.

April 9, 2016

How to design talk slides

Filed under: Real — liyiwei @ 11:57 pm
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Using slides is a popular way to give presentations. I am not sure if it is the best way, but things can go very badly if done in the wrong way.

Take a look at Jim Blinn’s post about giving presentations.
Below are some quick high level suggestions. (I plan to refine this post later.)

Aim for simplicity and minimalism.

The slides are for conveying information to your audience, not serving as memo for the speaker.

Use intuitive pictures, illustrations, and animations, instead of texts and (worse) equations.

If you find yourself worrying about typography, it is a sign of too much texts.
No sentence should run over one line.

Rid of visual clutters like bullet points.

Gratuitous colors and unnecessary font variations tend to confuse people.

March 24, 2016

Recording and sharing presentation

Filed under: Real — liyiwei @ 9:43 am
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Talks are usually easier to understand than the corresponding papers. To get accepted, papers need to be written in a way that look formal and rigorous, but not necessarily easy to understand. However, when authors present their (accepted) papers, they tend to cut the chase and talk straight.

In the past, people have to attend conferences for the talks.
Nowadays, everyone can easily share their talk slides online.
Better yet, record and share your presentations as videos (e.g. via PowerPoint). This can be done during practices or official presentation.
You do practice your talks, right? So why not record during your rehearsals, so that you can review now and share later.
Recording in official presentation might be trickier, e.g. the conference may prefer presenters using a shared machine and the recording might disrupt your presentation, but can be worth a try.

I have not done this for my own talks, but realized it can be a good idea after watching a few recorded talks online. I really appreciate the efforts from the authors, and plan to do so for my future talks.

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 26, 2016

How to write grant proposals

Filed under: Real — liyiwei @ 6:24 pm
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Having written only 5 grant proposals so far, I am still relatively new to the game. But the following summarizes the gist quite well.

Basically, it is not just about writing what you plan to do. You are essentially authoring a top paper, but not yet published.

I used to think that writing grants is a necessary evil, but now I realized it is a great way to plan research at a high level, beyond individual papers or projects.

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.

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