Confessions of a researchaholic

June 2, 2013

How to do a paper fast-forward

Filed under: Real — liyiwei @ 12:39 am
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Giving a good paper presentation is already hard, but at least you have 20 minutes’ worth of wiggle room. Giving a good paper fast-forward is even harder, because you have only 40 seconds. Even one tiny mistake can ruin you.

Goal

The most common mistake is trying to explain too much (I like to call it “geek’s asymmetry”). Trust me; almost nobody will care, and certainly nobody will understand, within 40 seconds and among 100+ presentations.

The fast-forward is pure advertisement with one main goal: get people read your paper and attend your talk.
On top of that, if you are really good, show what a cool and interesting guy you are. But do not even try unless you are absolute sure. (A good rule of thumb is this: are you already cool and interesting?)

Design

Write down the script first, so that you know what you want to talk about and you can comfortably utter the sentences within the limited time frame. Practice and rehearse a sufficient number of times, especially if you lack verbal proficiency. Only design your slides after the script is in a stable condition. This is extremely important. If you do it the other way around, and I know this is what most people would do, you are making a grave mistake, because (just like what movie critics would say) you are letting the effects get in the way of the substance.

Do not force people read your slides. Use pictures and animations instead of texts to explain your points.

Practice

After having both the script and the slides, practice, until you can do it perfectly during sleep.

March 15, 2013

Burnout

Filed under: Real — liyiwei @ 4:22 pm
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A recent PhD graduate whom I know well, who has been doing quite well in graphics research, just took a non-research job in a non-graphics position, among several postdoc offers from top research institutions.

The reasons he cited are numerous, but the 2 main ones are:

. He likes graphics research, but he is getting bored and tired of the SIGGRAPH game, including deadline crunch and dealing with reviewers.

. He would like to learn something new and try a different life style.

In a nutshell, I think he is burned out. I hope I have done my best to help him achieve work-life balance (I did that quite well myself, even when I was a grad student), but I guess it is just too hard for normal people to be much disciplined.

Then I realized that I probably also had some kind of burnout around my graduation. I took a non-research position as the first job, even though it was in a graphics company (NVIDIA). I also wanted to try learning new things (hardware architecture) and living a different style (engineer).

So I guess it is probably OK. People are not meant to be doing the same thing all the time. This is also why I like to try different job sectors and geographical locations.

There are two things to watch out, though, all based on my personal experiences: passion, and rust.

Passion

I have a very simple rule to choose jobs: do what you really like, and be very good at it.

Sometimes, when people get burned out, they might temporarily settle for something that they neither really like nor really be good at. But eventually, you will know if the job is not for you. I did not realize how much I like doing research until after I was not been able to spend enough time on it for about 3.5 years after my graduation.

The important thing is to get out there as quickly as possible. Otherwise, you will eventually become one of these people who are not really happy or good at their jobs but also cannot quit.

Rust

People tend to get rusty for skills that they have not practiced for a while. This is particularly so for advanced skills, like research.

So, make sure you do whatever you can to be active in research during your non-research job. Otherwise, you might not be able to come back, even if you want.

I have been trying my best to be engaged in research during my NVIDIA days. I even managed to publish a single authored graphics hardware paper. But it still took me about 2 to 3 years to get back in shape for SIGGRAPH after joining MSR. The difference between SIGGRAPH and other graphics venue is like the difference between playing professional sports and working out in a gym.
I guess SIGGRAPH is probably an extreme case, but I hope you get what I mean.

January 7, 2013

Sharing source code and data

Filed under: Real — liyiwei @ 1:08 am
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It is usually a very good idea to share the code and data along with your published papers. This will make it easier for others to reproduce and compare against your methods, which in turn can make you more popular and your papers more widely cited.

As a corollary, it could also a good idea to submit your code and data along with your paper for peer review. Reviewers tend to like your paper more if they can reproduce your results. However, there are a few things to watch for. The first, and the obvious one, is to make sure your code actually works as advertised. Another is author anonymity; for venues that mandate it you will want to make sure your code and data (just like your paper) do not contain any information that can identify the authors. If you have doubts on this, one possibility is to submit your code and data as non-anonymous materials, if the particular venues allow this (e.g. SIGGRAPH). This can reduce the risk of breaching anonymity and yet allows the committee members to back you if the need arises.

December 31, 2012

How to design demos

Filed under: Real — liyiwei @ 9:51 pm
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In a nutshell, a demo should properly demonstrate technical aspects with sufficient artistic appeals.

The technical part is usually more important, and can suffice alone for many science and engineering disciplines. However, the artistic part is also very important for graphics and HCI, or any fields which involve direct human perception and consumption.

Demos usually take a lot of time and efforts, on top of the usual workload in ideation, writing, algorithm, implementation, and experimentation. And whether you like it or not, a solid and novel algorithm cannot be adequately assessed or appreciated by the readers if it is not demonstrated through proper demos.

Thus, designing demos is kind of an art. Below are recent suggestions from Sylvain Lefebvre which I have found to be excellent.


A guideline that worked fine for me is to consider whether 1) the result demonstrates the technique properly and 2) the result looks just good enough that it appears useful; in particular we want to avoid people think that the example is contrived to only show the advantage of our approach.

The problem is that 1) and 2) sometimes compete with each other (e.g. a fantastic rendering possibly making it hard to properly see the motion, etc.). Also we do not want to spend too much time on 2), only enough that people will think that it is convincing.

November 10, 2012

How I work with each student

Filed under: Real — liyiwei @ 11:34 am
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This figure illustrates how I collaborate with each student.

There are several main layers of a research project: idea creation, algorithm design, system implementation, and experimentation + results production. They are all wrapped up and glued together by presentation, which includes paper writing, video production, and (conference) talk.

For a beginner student, I would expect you to completely take charge of the implementation and production (including video), given the main ideas and algorithms as described in a paper draft (as part of the presentation). We could discuss anything you like, but you should be able to handle all the implementation and production details (e.g. I am not supposed to look into your code). Otherwise, you are not ready for research.

As you progress, I would expect you to be able to take charge more tasks at the higher layers. Idea creation is the most difficult and only a few can pull that off, but my experience is that a decent senior student can at least take part in algorithm design and paper writing.

September 8, 2012

How to manage project progress

Filed under: Real — liyiwei @ 11:26 pm
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Managing project is a huge topic that involves multiple aspects. This post focuses on one specific aspect, on how to manage and ensure progress. To me this is the most important aspect, because a project that is not done is worth nothing.

My strategy is very simple: use schedules to manage progress.

\paragraph{What}
Here is a quick example.

First milestone demo: October 17, 2012.
(A milestone is a demo that can show the project (1) is of SIGGRAPH potential and (2) is likely to be completed.)

Demo production (assuming we have 3, each with complete and concrete descriptions):
. Demo 1: November 1, 2012.
. Demo 2: November 16, 2012.
. Demo 3: December 1, 2012.

First complete paper draft: December 17, 2012.
(SIGGRAPH is not just about getting things done; it is a refinement progress towards perfection. My hope is to have a one month margin, even though few of my projects, mostly first authored by myself, have made this.)

SIGGRAPH 2013 deadline: January 17, 2013.
(Hard deadline; everything has to be done before this.)

\paragraph{How}
The schedule should be drafted, usually by the first author student, in the beginning of the project, and constantly updated and maintained throughout the project progress. All team members will look at the schedule and agree with its content.

The most important part is to make sure you actually meet your schedule. Do not let anything slip. Otherwise, it will not work.

Of course, research, or any project, can and will bump into unexpected snags. It is also not possible to accurately predict the amount of time needed for every item. The key is to keep the schedule as accurate as possible. For example, if you originally think demo 2 above will take only 2 weeks but it ends up taking 3 weeks, you should question whether you are too optimistic about your schedule and allocate more time for other items.
(If you find yourself not being able to meet the deadline, it means you probably have to speed things up by either working harder or working more efficiently. But the good thing is that you realize this early rather than late.)
As you become more experienced, you will also become more accurate in managing and predicting schedules. Without this practice, you will less likely learn this very important life skill.

I guess it probably very hard to fully comprehend what I mean here. If you are my student, you will learn this through a lot of practice with me.

\paragraph{Background}
I learned this through my NVIDIA days. One thing I find particularly proud about NVIDIA is that to my memory *not a single product* has ever been delayed during my stay. This was in sharp contrast to ATI, the (then) fierce competitor to NVIDIA, which (for reasons I do not know) constantly bumped into product delays. The rest is history.

The way the NVIDIANS did it is like this. Everybody and their brothers used schedules to manage progress, and slipping a milestone is considered a more serious crime than shooting someone. The CEO dropped in group meetings constantly and unannounced, and called out those responsible for delays. (Trust me, you will prefer being shot rather than getting called out.)

I like to list one most important thing I learned with every one of my jobs. And for NVIDIA, it will be project management. I do not think I will have learned this if I started as a professor immediately after school.

July 19, 2012

Should you do that rebuttal?

Filed under: Real — liyiwei @ 4:06 pm
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See here for the related post about how to do a rebuttal.

Again, this is mostly for SIGGRAPH, but likely applicable for other publication venues.

My answer is absolutely yes, for a very simple reason: the amount of time and efforts you spend on rebuttal will be a small percentage of what you have spent in submitting the paper (4 days versus months if not years). Thus, you have everything to gain, and little to lose.

More reasons:

. Review is a highly stochastic and unpredictable process, and nobody knows what is going to happen. I have seen papers with strong rating (e.g. > 4) rejected, and weak rating (e.g. < 2.8) accepted. (And yes, both are mine. And I was told the latter is a SIGGRAPH record.)

. Not submitting a rebuttal is usually perceived by reviewers as a (negative) sign that authors agree with their (negative) comments.

. Rebuttal is a good practice for both research method and emotion control, especially for students, junior researchers, and myself.

. Show you get balls. As a reviewer I admire authors who can manage to come up with strong rebuttal even for a paper with low ratings.

March 26, 2012

Another data point

Filed under: Real — liyiwei @ 1:49 am
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In responding to my earlier post, a very talented graphics researcher has shared with me his statistics, as shown below.

As you can see, his has much better success rate than me, especially considering that he has been doing rendering, a tough field for SIGGRAPH. :-)

March 24, 2012

Fooled by randomness

Filed under: Real — liyiwei @ 11:41 pm
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Feeling down from some recent rejections? I hope this post will make you more positive. The gist: never, ever, quit.

Assume you are throwing N loaded dices, each with a probability p for coming up head.

Now, if p is greater than 0 and smaller than 1, there is always a chance that the N dices will come up with all heads or all tails. And the smaller the N value, the more likely for such extreme cases to happen.

This is all pure chance. But unfortunately, human brains have difficulty accepting randomness, and always want to impose determinism, e.g. patterns or rules or causalities.
For example, if you are a scholar submitting N papers to a conference, you will likely consider yourself to be very good/bad (or the paper committee has treated you very well/badly) if all N submissions are accepted/rejected.

This human fallacy is brilliantly illustrated by Nassim Nicholas Taleb’s book “Fooled by Randomness”.

However, even without reading that book, I can recommend a very simple remedy: law of large numbers. This is a well-known mathematical theorem, which says that the expected value of a random variable can be more accurately predicted by averaging a larger number of samples.

So, for example, to measure your intrinsic paper acceptance rate towards a specific conference, you can take the total number of acceptances divided by the total number of submissions. This will be a much more meaningful measure than your acceptance rate for a single year, especially if you have a sufficient number of submissions across multiple years.

For example, the plot below shows my cumulative acceptance rate for SIGGRAPH, the top venue for computer graphics and interactive techniques. As you can see, the rate seems to be gradually converging to a certain value, around 0.34. This is much more stable measure than my yearly rate, which can be anywhere between 0 and 1.

Now, if you are new to a field, your rate will have a higher variance, just like the initial portions of mine. I was lucky that I had a good start which boosted my confidence. (Initial condition is actually very important and has been found to greatly influence the performance of many careers, e.g. hockey players. Note to myself: dig out that book/article. I guess it should be Geoff Colvin’s “Talent is Overrated” or Malcolm Gladwell’s “Outliers”.) However, if you happen have an unlucky start, do not give up too early; hang on for a while, so that you can have a chance to see your *intrinsic performance*.
As you can see, my intrinsic performance did not really show up until about a decade doing SIGGRAPH.

(With all these rational arguments, I have to confess that it still hurts to get rejected!)

Some notes about the graph: (1) I plot SIGGRAPH at integer years and SIGGRAPH Asia at integer + 0.5 years, (2) missing data points are for years which I did not submit anything (2004 and 2005 while in NVIDIA and 2011.5 when I have nothing to submit for SIGGRAPH Asia 2011), (3) a more accurate measure would be “moving average” (with exponential decay of past values) but I probably need another 10 years to warrant this, (4) I really want to improve my intrinsic rate to at least 50 percent!, (5) I guess the ultimate test is to have multiple disjoint committees + reviewers, all with similar qualities, to evaluate the same batch of submissions, and see if they will accept similar sets of papers.

January 17, 2012

Representative image for SIGGRAPH submission

Filed under: Real — liyiwei @ 5:24 am
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Basically, its main function is to be displayed on the front of the room during the committee meeting, so that people can have a visual reference/cue of the paper being talked about.
Even though the representative image does not have any practical value for the paper (it is not illegal to use an arbitrary image, like, say, picture of Lindsay Lohan, at least with clothes on), the committee members are humans and thus can be subtly influenced by all psychological factors.
Thus, it is to your advantage to use an image that can help your paper, i.e. conveys the main ideas or results while looks appealing.

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